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Conceptualising and measuring social media engagement: A systematic literature review

• Review Article
• Open access
• Published: 11 August 2021
• Volume 2021 , pages 267–292, ( 2021 )

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• Mariapina Trunfio 1 &
• Simona Rossi   ORCID: orcid.org/0000-0003-4384-0002 1  

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The spread of social media platforms enhanced academic and professional debate on social media engagement that attempted to better understand its theoretical foundations and measurements. This paper aims to systematically contribute to this academic debate by analysing, discussing, and synthesising social media engagement literature in the perspective of social media metrics. Adopting a systematic literature review, the research provides an overarching picture of what has already been investigated and the existing gaps that need further research. The paper confirms the polysemic and multidimensional nature of social media engagement. It identifies the behavioural dimension as the most used proxy for users' level of engagement suggesting the COBRA model as a conceptual tool to classify and interpret the construct. Four categories of metrics emerged: quantitative metrics, normalised indexes, set of indexes, qualitative metrics. It also offers insights and guidance to practitioners on modelling and managing social media engagement.

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1 Introduction

Over the last decade, customer engagement has received increasing attention in academic and professional debate (Hollebeek, 2019 ; Kumar et al., 2019 ; Marketing Science Institute, 2020 ; Peltier et al., 2020 ; Rather et al., 2019 ; Rossmann et al., 2016 ). It can be considered a “consumer’s positively brand-related cognitive, emotional and behavioural activity during, or related to, focal consumer/brand interactions” (Hollebeek, 2014 , p.149). Engaged customers display greater brand loyalty and satisfaction (Bowden, 2009 ; Jaakkola & Alexander, 2014 ) and are more likely to contribute to new product development (Haumann et al., 2015 ), service innovation (Kumar et al., 2010 ), and viral marketing activity spread by word of mouth (Wu et al., 2018 ). Customer engagement can also be linked with important brand performance indicators, including sales growth, feedback, and referrals (Van Doorn et al., 2010 ).

Acknowledging the potential of ICTs, scholars and practitioners are experimenting with new ways to capitalise on customer engagement and adapt to the new challenges of digital platforms (Barger et al., 2016 ; Peltier et al., 2020 ). Social media platforms reshaped the dyadic interaction between customers and organisations, creating spaces for digital sharing and engagement. By enabling users to comment, review, create, and share content across online networks, social media provide direct access to brands and allow co-creation processes. As such, the pervasive character of social media with its potential for engaging with customers and building relationships generated much interest in the concept of social media engagement (Barger et al., 2016 ; Hallock et al., 2019 ; Oviedo-García et al., 2014 ; Peltier et al., 2020 ; Schivinski et al., 2016 ). Engaging with customers in real-time and managing many incoming customers’ big data interested academic investigation and opened opportunities for marketers to enhance social media marketing success (Liu et al., 2019 ).

Understanding, monitoring, and measuring social media engagement are key aspects that interest scholars and practitioners who proposed diverse conceptualisations, several indicators and KPIs. With the spread of social media analytics, social networking platforms, digital service providers, marketers, and freelancers developed their metrics to measure engagement with brand-related social media contents and advertising campaigns. At the same time, scholars have pointed out various metrics and procedures that contribute to evaluating social media engagement in different fields (Mariani et al., 2018 ; Muñoz-Expósito et al., 2017 ; Trunfio & Della Lucia, 2019 ). Nevertheless, many of these studies offer a partial perspective of analysis that does not allow the phenomenon to be represented in diverse aspects (Oviedo-García et al., 2014 ). As a result, social media engagement remains an enigma wrapped in a riddle for many executives (McKinsey, 2012 ). How communities across an ever-growing variety of platforms, new forms of customer-brand interactions, different dimensions and cultural differences impact social media engagement measurement represents one of the main challenges (Peltier et al., 2020 ).

Although social media engagement represented a key topic in marketing research (Barger et al., 2016 ; Peltier et al., 2020 ), an overarching perspective of the existing knowledge can drive the investigation of the state of the field, including the study of the research streams, and the analysis of the measurement tools. This paper aims to systematically contribute to the academic debate by analysing, discussing, and synthesising social media engagement literature from the social media metrics perspective. A systematic literature review approach provides an overarching picture of what has already been investigated and the existing gaps that need further research. It contributes towards a systematic advancement of knowledge in the field and offers insights and guidance to practitioners on modelling and managing social media engagement (Tranfield et al., 2003 ).

The remainder of the paper is structured as follows. Section  2 presents the theoretical background of the study on customer engagement and social media engagement. Section  3 describes the methodology used for conducting the systematic literature review (Pickering & Byrne, 2014 ; Tranfield et al., 2003 ). Section  4 presents the bibliometric analysis results, including the year in which research began, the journals that publish most research, and the most relevant authors with publications on the topic. Then, Sect.  5 classifies these studies in terms of four macro-themes, conceptualisations, platforms, measurement, and behaviours and describes the key results available in the literature. Section  6 provides a critical discussion of the findings from the literature review and highlights its key contributions. Lastly, Sect.  7 concludes the study by highlighting its limitations and proposing directions for future research.

2 Theoretical background

2.1 customer engagement.

Although customer engagement research has increased theoretical and managerial relevance (Brodie et al., 2011 ; Hollebeek et al., 2016 , 2019 ; Kumar et al., 2019 ; Vivek et al., 2012 ), to date, there is still no consensus on its definition due to its multidimensional, multidisciplinary and polysemic nature.

Several customer engagement conceptualisations have been proposed in the literature, drawing on various theoretical backgrounds, particularly service-dominant logic, and relationship marketing. From a psychological perspective, one of the first definitions of customer engagement is the one of Bowden ( 2009 ) that conceptualises it as a psychological process that drives customer loyalty. Similarly, Brodie et al. ( 2011 ) define customer engagement as a psychological state that occurs by interactive, co-creative customer experiences with a focal object. Later, focusing on the behavioural aspects, it has been described as the intensity of an individual’s participation in an organisation’s offerings or organisational activities (Vivek et al., 2012 ). More recently, from a value-based perspective, customer engagement has been defined as the mechanics that customers use to add value to the firm (Kumar et al., 2019 ).

Although the perspectives may vary, common elements can be identified in various conceptualisations. Literature generally understands customer engagement as a highly experiential, subjective, and context-dependent construct (Brodie et al., 2011 ) based on customer-brand interactions (Hollebeek, 2018 ). Moreover, scholars agree on its multidimensional nature (Brodie et al., 2013 ; Hollebeek et al., 2016 ; So et al., 2016 ; Vivek et al., 2012 ) encompassing cognitive (customer focus and interest in a brand), emotional (feelings of inspiration or pride caused by a brand), and behavioural (customer effort and energy necessary for interaction with a brand) dimensions. Also, researchers have proposed that customer engagement affects different marketing constructs (Brodie et al., 2011 ; Van Doorn et al., 2010 ). For example, in Bowden’s research (2009), there is evidence to support that customer engagement is a predictor of loyalty. Brodie et al. ( 2011 ) explore its effects on customer satisfaction, empowerment, trust, and affective commitment towards the members of a community. Van Doorn et al. ( 2010 ) propose customer-based drivers, including attitudinal factors such as satisfaction, brand commitment and trust, as well as customer goals, resources, and value perceptions.

2.2 Social media engagement: The academic perspective

Social media engagement has also been investigated as brand-user interaction on social media platforms (Barger et al., 2016 ; De Vries & Carlson, 2014 ; Hallock et al., 2019 ; Oviedo-García et al., 2014 ; Peltier et al., 2020 ; Schivinski et al., 2016 ). However, while conceptual discussions appear to dominate the existing customer engagement literature, research results fragmented when moving to the online context. Scholars agree that social media engagement is a context-specific occurrence of customer engagement (Brodie et al., 2013 ) that reflects customers’ individual positive dispositions towards the community or a focal brand (Dessart, 2017 ). Social media engagement can emerge with respect to different objects: the community, representing other customers in the network, and the brand (Dessart, 2017 ). Furthermore, antecedents and consequences of social media engagement have been identified to understand why customers interact on social media and the possible outcomes (Barger et al., 2016 ), such as loyalty, satisfaction, trust, and commitment (Van Doorn et al., 2010 ).

In continuity with literature on customer engagement, also social media engagement can be traced back to affective, cognitive, and behavioural dimensions (Van Doorn et al., 2010 ). Most of the literature focuses on the behavioural dimension as it can be expressed through actions such as liking, commenting, sharing, and viewing contents from a brand (Barger et al., 2016 ; Muntinga et al., 2011 ; Oh et al., 2017 ; Oviedo-García et al., 2014 ; Peltier et al., 2020 ; Rietveld et al., 2020 ; Schivinski et al., 2016 ). It is worth pointing out that not all these actions determine the same level of engagement. Schivinski et al. ( 2016 ) in the COBRA (Consumer Online Brand Related Activities) Model differentiate between three levels of social media engagement: consumption, contribution, and creation. Consumption constitutes the minimum level of engagement and is the most common brand-related activity among customers (e.g., viewing brand-related audio, video, or pictures). Contribution denotes the response in peer-to-peer interactions related to brands (e.g., liking, sharing, commenting on brand-related contents). Creation is the most substantial level of the online brand-related activities that occur when customers spontaneously participate in customising the brand experiences (e.g., publishing brand-related content, uploading brand-related video, pictures, audio or writing brand-related articles). Starting from these social media actions, scholars attempted to measure social media engagement in several ways developing scales, indexes, and metrics (Harrigan et al., 2017 ; Oviedo-García et al., 2014 ; Schivinski et al., 2016 ; Trunfio & Della Lucia, 2019 ). Nevertheless, many of these studies offer a partial perspective of analysis that does not allow the phenomenon to be represented in its diverse aspects (Oviedo-García et al., 2014 ). Researchers have also examined emotional and cognitive dimensions (Dessart, 2017 ) as essential components of social media engagement that lead to positive brand outcomes (Loureiro et al., 2017 ).

2.3 Social media engagement: The practitioners’ perspective

In business practice, the concept of customer engagement appeared for the first time in 2006 when the Advertising Research Foundation (ARF), in conjunction with the American Association of Advertising Agencies and the Association of National Advertisers, defined it as a turning on a prospect to a brand idea enhanced by the surrounding context (ARF, 2006 ) . Later, several consulting firms tried to give their definition emphasising different aspects and perspectives. For example, in 2008, Forrester Consulting, an American market research company, defined customer engagement as a way to create ‘deep connections with customers that drive purchase decisions, interaction, and participation over time’ (Forrester Consulting, 2008 , p.4). Gallup Consulting identified four levels of customer engagement and defined it as an emotional connection between customers and companies (Gallup Consulting, 2009 ). Similarly, the famous American software provider Hubspot ( 2014 ) identified social media engagement as ‘ the ongoing interactions between company and customer, offered by the company, chosen by the customer’ (Hubspot, 2014 , p.1).

With the increasing spread of social networks and their exploitation as an important marketing tool, practitioners recognised a clear linkage between customer engagement and the metrics to assess digital strategy success. Over time, social networking platforms such as Facebook, LinkedIn, and YouTube, developed their metrics to measure engagement with brand-related social media contents and advertising campaigns (Table 1 ).

With the spread of social media analytics, platforms and digital service providers developed dashboards and analytical indicators to assess, measure and monitor the engagement generated by social media marketing activities (Table 2 ). At the same time, many bloggers, marketers, and freelancers have weighed in on the topic, enriching the debate with new contributions.

As a result, while scholars still have to agree upon a shared definition of social media engagement, marketers have recognised it as one of the most important online outcome companies need to deliver with social media and a key metric to assess social media strategy success . Despite the growing interest in business practice and its solid traditional theoretical roots, most of the existing literature on social media engagement offers only conceptual guidelines (Barger et al., 2016 ; Peltier et al., 2020 ). The measurement of engagement in social media and its financial impact remains an enigma wrapped in a riddle for many executives (McKinsey, 2012 ) and requires further investigations. Mainly, how new and emerging platforms, new forms of customer-brand interactions, different dimensions, and cultural differences impact social media engagement measurement remains an understudied phenomenon (Peltier et al., 2020 ).

3 Methodology

The literature review is one of the most appropriate research methods, which aims to map the relevant literature identifying the potential research gaps that need further research to contribute towards a systematic advancement of new knowledge in the field (Tranfield et al., 2003 ). This research is built upon the rigorous, transparent, and reproducible protocol of the systematic literature review as a scientific and transparent process that reduces the selection bias through an exhaustive literature search (Pencarelli & Mele, 2019 ; Pickering & Byrne, 2014 ; Tranfield et al., 2003 ). Building on recent studies (Inamdar et al., 2020 ; Linnenluecke et al., 2020 ; Phulwani et al., 2020 ), in addition to the systematic literature review, a bibliometric analysis (Li et al., 2017 ) was also performed to provide greater comprehensions into the field's current state and highlight the future research directions.

3.1 Database, keywords, inclusion, and exclusion criteria

To conduct a literature review, quality journals are considered the basis for selecting quality publications (Wallace & Wray, 2016 ). Therefore, the database Scopus, run by Elsevier Publishing, was considered to search for relevant literature, being the most significant abstract and citation source database used in recent reviews.

When conducting a literature review, a fundamental issue is determining the keywords that allow identifying the papers (Aveyard, 2007 ). To address it, the most frequently used keywords in peer-reviewed literature have been under investigation. As such, the following research chain was used: “Social media” “Engagement” AND “metric*”, searching under title, abstract, and keywords.

The systematic literature review protocol (Fig.  1 ) has been conducted on the 26 th of March 2020. The study considers an open starting time to trace back to the origin of social media engagement metrics research up to late March 2020. The initial search attempts identified 259 documents.

The systematic literature review protocol

After the articles’ identification, criteria for inclusion and exclusion were adopted. First, the 259 articles were screened, considering English-language articles published in peer-reviewed academic journals to safeguard the quality and effectiveness of the review. Due to variability in the peer-review process and their limited availability, book reviews, editorials, and papers from conference proceedings were excluded from this research. After the screening, a sample of 157 papers was obtained.

Afterwards, the full text of these papers was reviewed to assess eligible articles. As a result, 116 articles were excluded because their subject matter was not closely related to the topic of social media engagement metrics. In detail, papers were excluded when: 1) they mainly focused on social media engagement but superficially touched the metrics or 2) they mainly focused on metrics but superficially touched on social media engagement. In the end, 41 eligible articles were identified.

3.2 Analysis tools

The relevant data of the 41 documents in the final sample were saved and organised in a Microsoft Excel spreadsheet to include all the essential paper information such as paper title, authors’ names, and affiliations, abstract, keywords and references. Then, adopting the bibliometrics analysis method (Aria & Cuccurullo, 2017 ), the R-Tool ‘Biblioshiny for Bibliometrix’ was used to perform a comprehensive bibliometric analysis. Bibliometrix is a recent R-package that facilitates a more complete bibliometric analysis, employing specific tools for both bibliometric and scientometric quantitative research (Aria & Cuccurullo, 2017 ; Dervis, 2019 ; Jalal, 2019 ).

4 An overview of social media engagement metrics research.

The bibliometric analysis provided information on the 41 articles, allowing to highlight the significance of the topic.

4.1 Publication trend

The number of annual publications shows a rollercoaster trend (Fig.  2 ). Although the first relevant paper was published in 2013, only since 2016 publications begun to increase significantly with a slight decrease in 2018. This renders social media engagement metrics a relatively young research field.

Timeline of the studies (January 2013- March 2020)

It is worth pointing out that the articles extraction was done in March 2020: this explains the low number of articles published in 2020.

4.2 Most relevant sources

When looking at the Journal sources overview, the analysis revealed 34 journals covering different fields, including marketing, management, economics, tourism and hospitality, engineering, communication, and technology. As shown in Fig.  3 , only four journals have more than two publications: Internet Research , Journal of Engineering and Applied Sciences , International Journal of Sports Marketing and Sponsorship. and Online Information Review .

Most relevant sources

4.3 Seminal papers

Interesting findings emerged considering the most global cited documents that allow identifying the seminal articles in according to the timeliness, utility and quality, expressed by the scientific community (Okubo, 1997 ). The number of citations an article receives, and the studies cited in an article are two of the most popular bibliometric indicators used to determine the popularity of a publication.

Figure  4 shows the number of author citations for each article, identifying as seminal works: Malthouse’s (2013) paper ‘ Managing Customer Relationships in the Social Media Era: Introducing the Social CRM House’ with 278 global citations; Sabate’s (2014) paper ‘Factors influencing popularity of branded content in Facebook fan pages’ with 145 global citations; Mariani’s (2016) paper ‘ Facebook as a destination marketing tool: Evidence from Italian regional Destination Management Organizations ’ with 104 global citations; Oh’s (2017) paper ‘ Beyond likes and tweets: Consumer engagement behavior and movie box office in social media ’ with 54 global citations; Colicev’s (2018)’ Improving consumer mindset metrics and shareholder value through social media: The different roles of owned and earned media ’ with 39 global citations; Rossmann’s (2016) ‘ Drivers of user engagement in eWoM communication ’ with 35 global citations; Oviedo-Garcia’s (2014) ‘ Metric proposal for customer engagement in Facebook’ with 33 global citations .

Most cited articles

The analysis of the papers reviewed revealed that the theme of social media engagement metrics turns out to be a hot topic and a newly emerging stream of research.

5 Social media engagement: areas of investigation

In recent years social media engagement has gained relevance in academic research, and many scholars have questioned its measurement, intensifying the academic debate with ever new contributions. Following previous studies, a comprehensive analysis allows framing the following categories of broad research subjects, used to conduct the subsequent systematic literature review (Fig.  5 ): (1) conceptualisation, (2) platforms, (3) measurement and (4) behaviours. All 41 articles were analysed according to the proposed scheme.

Areas of investigation

5.1 Investigating social media engagement

What emerges from the analysis of the 41 papers is that scholars used different approaches and methodologies to conceptualise and measure engagement in the digital context of social media.

As shown in Fig.  6 , most studies (66%) employ quantitative methodologies. For instance, Yoon et al. ( 2018 ) explored the relationship between digital engagement metrics and financial performance in terms of company revenue, confirming that customer engagement on a company’s Facebook fan page can influence revenue. Colicev et al. ( 2018 ) developed three social media metrics, including engagement, to study the effects of earned social media and owned social media on brand awareness, purchase intention, and customer satisfaction. In comparison, Wang and Kubickova ( 2017 ) examined factors affecting the engagement metrics of Facebook fan pages in the Northeast America hotel industry, factors such as time-of-day, day-of-week, age, gender and distance between the hotel and users’ origin of residence. They also analysed the impact of Facebook engagement on electronic word-of-mouth (eWOM), to better understand the importance of the engagement metrics within the hospitality context.

Classification of the 41 articles based on the methodology applied

From a qualitative point of view (17% of the papers), Hallock et al. ( 2019 ) used a case study approach to understand the firm perspective on social media engagement metrics, shedding light on how companies view engagement with social media as measurable metrics of customer interactions with the platform. Conversely, Michopoulou and Moisa ( 2019 ) used the same approach to investigate the use of social media marketing metrics and practices in the U.K. hotel industry.

Only a small part of the studies analysed (10% of the papers) explores social media engagement from a purely conceptual perspective. In this sense, Oviedo-Garcìa et al. ( 2014 ) and Muñoz-Expósito et al. ( 2017 ) directly identified social media engagement metrics for Facebook and Twitter, providing fascinating insights for scholars and practitioners.

Finally, among the papers analysed, only three studies (7% of the papers) use mixed methodologies to explore the phenomenon from qualitative and quantitative perspectives.

5.2 Defining social media engagement

Researchers identified 30 unique definitions of engagement applied to the social media context. Multiple definitions used several terms when defining engagement on social media. They were not singular and straightforward but were interspersed with various key terms and overlapping concepts, as presented in Table 3 .

The presence of synonymous terms directly addresses the lack of a standard definition and the challenges that this presents to researchers and practitioners in the field (Table 4 ).

As a relevant result, most authors focus on its behavioural manifestation (22% of the studies) resulting from motivational drivers when defining social media engagement. It is considered as the active behavioural efforts that both existing and potential customers exert toward online brand-related content (Yoon et al., 2018 ). It involves various activities that range from consuming content, participating in discussions, and interacting with other customers to digital buying (Oh et al., 2017 ; Yoon et al., 2018 ). Similarly, in addition to the behavioural manifestations, other scholars (12%) focus on the emotional connection expressed through the intensity of interactions and their implications, toward the offers and activities of a brand, product, or firm, regardless of whether it is initiated by the individual or by the firm (Muñoz-Expósito et al., 2017 ).

Shifting the observation lens from the customers to the firms, another group of scholars (10% of the studies) define social media engagement as the non-monetary return that derives from the online marketing strategies of brands (Khan, 2017 ; Medjani et al., 2019 ; Michopoulou & Moisa, 2019 ). In this case, engagement is viewed exclusively as a non-financial metric and as a measure of the performance of social media marketing activities.

Lastly, a small percentage of studies (10% of the studies) considers engagement as the number of people who acknowledge agreement or preference for content, who participate in creating, sharing and using content (Colicev et al., 2018 ; Li et al., 2019 ; Rahman et al., 2017 ).

5.3 Social Media Platforms

In a total of 41 articles reviewed, 85% of studies mention the platforms analysed, as shown in Table 5 . Facebook is the most popular platform analysed, followed by Twitter, YouTube, LinkedIn, and Instagram. These results were rather expected, given the fact that Facebook, with 2.6 billion monthly active users (Facebook, May 2020), is the most popular social media platform worldwide.

An interesting finding is that there are several articles (15% of the studies) which do not refer to a specific platform or that consider all the platforms together, when measuring social media engagement (e.g., Hallock et al., 2019 ; Medjani et al., 2019 ). This is interesting, given that each social network has different features that make the engagement measurement unique and not replicable.

5.4 Measuring social media engagement

The systematic literature review confirms that there is no theoretical certainty or solid consensus among scholars about measuring engagement on social media.

As can be seen from Table 6 , studies on social media engagement metrics can be grouped and classified into four macro-categories. The first group of studies, namely ‘quantitative metrics’, which is also the most numerous (66% of the studies), attempts to propose a simplistic assessment of the impact of social media engagement, based on the number of comments, likes, shares, followers etc. (Khan et al., 2019 ; Medjani et al., 2019 ; Yoon et al., 2018 ).

The second group of studies (17% of the studies), namely ‘normalised indexes’, provide a quantitative evaluation of the engagement a content generates in relation to the number of people to whom that content has been displayed. In this way, it is possible to obtain an average measure of the users’ engagement, dividing the total actions of interest by the total number of posts (Osokin, 2019 ; Zanini et al., 2019 ), the number of followers (Vlachvei & Kyparissi, 2017 ) or the number of people reached by a post (Muñoz-Expósito et al., 2017 ; Rossmann et al., 2016 ).

In a more complex and detailed way, studies from the third group (10% of the studies) identify social media engagement metrics developing ‘set of indexes’. For example, Li et al. ( 2019 ) use three social media metrics to measure engagement in the casual-dining restaurant setting: rates of conversation, amplification, and applause. In detail, conversation rate measures the number of comments or reviews in response to a post, amplification rate measures how much online content is shared, and applause rate measures the number of positive reactions on posts. Similarly, drawing from previous literature, Mariani et al. ( 2018 ) develop three social media metrics, namely generic engagement, brand engagement, and user engagement. Authors calculated these metrics by assessing different weights to different interaction actions, to emphasise the degree of users’ involvement implied by the underlying activities of respectively liking, sharing, or commenting.

Despite their great diffusion among academics and practitioners, some scholars (7% of the studies) argue that quantitative metrics are not enough to appreciate the real value of customer engagement on social media, and a qualitative approach is more suitable. For example, Abuljadail and Ha ( 2019 ) conducted an online survey of 576 Facebook users in Saudi Arabia to examine customer engagement on Facebook. Rogers ( 2018 ) critiques contemporary social media metrics considered ‘vanity metrics’ and repurpose alt metrics scores and other engagement measures for social research—namely dominant voice, concern, commitment, positioning, and alignment—to measure the ‘otherwise engaged’.

5.5 Social media engagement brand-related activities

When measuring social media engagement, scholars dealt with different social media actions that can be classified (Table 7 ) according to the three dimensions of the COBRA model (Consumer Online Brand Related Activities): consumption, contribution, or creation (Schivinski et al., 2016 ).

In a total of 41 articles reviewed, the most investigated dimension by researchers is contribution, i.e. when a customer comments, shares, likes a form of pre-existing brand content (e.g., Buffard et al., 2020 ; Khan et al., 2019 ). Its popularity among the studies may be due to its interactive nature of “liking” and “commenting”, which can be said to be the most common behaviour exhibited across social media platforms and often one of the most manageable interactions to obtain data. Additionally, studies that include creation in the measurement of social media engagement consider posting/publishing brand-related content, uploading brand-related video, pictures, audio or writing brand-related articles (e.g., Zanini et al., 2019 ). Among the sampled papers, the least investigated dimension of the COBRA model is consumption, considered by only seven studies (e.g., Colicev et al., 2018 ; Oh et al., 2017 ). It considers viewing brand-related audio, video, and pictures, following threads on online brand community forums or downloading branded widgets.

Dimensions have been investigated individually, for example, just considering the number of likes or comments (Khan et al., 2019 ; Yoon et al., 2018 ), or jointly using composite indicators, as in the case of Oviedo-Oviedo-García et al., 2014 ).

6 Discussion

This research presents fresh knowledge in the academic debate by providing an overarching picture of social media engagement, framing the phenomenon conceptually and offering a lens to interpret platforms and measuring tools. Conceptual and empirical studies tried to define, conceptualise, and measure social media engagement in diverse ways from different fields of research. They increased the gap between academia and managerial practice, where the topic of social media engagement metrics seems to be much more consolidated. The paper contributes to the academic debate on social media engagement, presenting continuity and discontinuity elements between different fields of enquiry. It also offers avenues for future research that both academics and marketers should explore. It also provides insights and guidance to practitioners on modelling and managing social media engagement.

6.1 Theoretical contribution

The article offers some theoretical contributions to this relatively young research field through the systematic literature review approach.

Firstly, the paper confirms the multidimensional and polysemic nature of engagement, even in the specific context of social media platforms, in continuity with the academic customer engagement research (Brodie et al., 2013 ; Hollebeek et al., 2016 ; So et al., 2016 ; Vivek et al., 2012 ). The concept of social media engagement can be traced back to three dimensions of analysis (Van Doorn, 2010 )—affective, cognitive, and behavioural—and some empirical studies measure it as such (Dessart, 2017 ; Vivek et al., 2014 ). However, the behavioural dimension is still the most used proxy to measure users’ level of engagement. Similarly, marketers and social media platforms have focused on behavioural interactions associated with likes, comments and sharing when reporting engagement metric (Peltier et al., 2020 ). What is worth pointing out is that emotional and cognitive dimensions are also essential components of social media engagement and should be adequately addressed by future research.

Secondly, strictly related to the first point, the paper suggests the COBRA model (Schivinski, 2016 ) as a conceptual tool to classify and interpret social media engagement from the behavioural perspective. Social media engagement can be manifested symbolically through actions (Barger et al., 2016 ; Oh et al., 2017 ; Van Doorn et al., 2010 ) that can be traced back to the three dimensions of consumption, contribution and creation (Schivinski et al., 2016 ). However, it is worth pointing out that not all these actions determine the same level of engagement. When measuring social media engagement, researchers should pay attention not only to ‘contribution’ but also to ‘consumption’ and ‘creation’, which are important indicators of the attention a post receives (Oviedo-Garcìa, 2014 ; Schivinski et al., 2016 ), giving them a different weight. It becomes even more important if considering that the same social networks provide different weights to users' actions. For example, in several countries, Instagram has tested removing the like feature on content posted by others, although users can still see the number of likes on their posts. YouTube has also decided to stop showing precise subscriber counts and Facebook is experimenting with hiding like counts, similar to Instagram.

Thirdly, the paper presents some of the key metrics used to evaluate social media engagement identifying quantitative metrics, normalised indexes, set of indexes and qualitative metrics. Although all indicators are based on the interaction between the user and the brand, as the literature suggests (Barger et al., 2016 ; Oviedo-Garcìa, 2014 ; Vivek et al., 2014 ), the paper argues that different metrics measure diverse aspects of social media engagement and should be used carefully by researchers. Despite the conceptual and qualitative research on the topic, even the most recent metrics offer measurements that do not allow engagement to be widely represented in its multidimensional and polysemic nature (Oviedo-García et al., 2014 ; Peltier et al., 2020 ). To get a deeper understanding of the construct, researchers should also consider some of the most recent advances in business practice. As an example, more and more practitioners have the chance to measure engagement by tracking the time spent on content and web pages to blend the different types of material, such as pictures, text, or even videos. Also, cursor movements, which are known to correlate with visual attention, and eye-tracking, can provide insights into the within-content engagement.

6.2 Managerial implications

Even if the topic of social media engagement seems to be more consolidated in business practice, this study also provides valuable implications for practitioners. Particularly, the findings shed light on the nature of social media engagement construct and on how metrics can be an extremely useful tool to evaluate, monitor, and interpret the effectiveness of social media strategies and campaigns.

This research offers a strategic-operational guide to the measurement of social media engagement, helping marketers understand what engagement is and choose the most effective and suitable KPIs to assess the performance and success of their marketing efforts. In this sense, marketers should accompany traditional metrics, such as likes, comments and shares, with new metrics capable of better capturing user behaviours.

Marketers also need to realise that engagement is a complex construct that goes beyond the simple behavioural dimension, encompassing cognitive and emotional traits. As a result, in some cases, the so-called “vanity metrics” could fail in fully representing all the aspects of social media engagement. In these cases, it should be accompanied by qualitative insights to analyse what users like to share or talk about and not merely look at likes, comments, and shares counts.

7 Limitations and future research

This research is not without limitations. First, the systematic literature review only includes English articles published in Journals. As social media engagement and engagement metrics are emerging research topics, conference proceedings and book chapters could also be included to deepen the understanding of the subject. Second, this research was conducted on the database Scopus of Elsevier for the keywords “social media engagement metrics”. Researchers could use a combination of different databases and keywords to search for new contributions and insights. Third, although the paper is based on a systematic literature review, this methodology reveals the subjectivity in the social sciences.

As this is a relatively young field of research, a further academic investigation is needed to overcome the limitations of the study and outline new scenarios and directions for future research. In addition, considering the growing importance of social media, there is value in broadening the analysis through additional studies. Future marketing research could use mixed approaches to integrate the three dimensions of social media engagement, linking qualitative and quantitative data. Advanced sentiment web mining techniques could be applied to allow researchers to analyse what users like to share or talk about and not merely look at likes, comments, and shares as the only metrics (Peltier et al., 2020 ).

Although Facebook and Twitter are the most used social network by brands, and the most significant part of the literature focuses on these two platforms, researchers should not forget that there are new and emerging social media in different countries (e.g., TikTok, Clubhouse). They already represent a hot topic for practitioners and are calling scholars to define new metrics to measure engagement. Additionally, as the use of social media increased during the COVID-19 pandemic, future research should take this into account to better understand social media engagement across different social media platforms.

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Trunfio, M., Rossi, S. Conceptualising and measuring social media engagement: A systematic literature review. Ital. J. Mark. 2021 , 267–292 (2021). https://doi.org/10.1007/s43039-021-00035-8

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This sample media research paper features: 2800 words (approx. 9 pages), an outline, and a bibliography with 13 sources. Browse other research paper examples for more inspiration. If you need a thorough research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our writing service for professional assistance. We offer high-quality assignments for reasonable rates.

The term media refers broadly to the range of tools that humans have used throughout history to communicate with each other about a shared reality. The most common reference is to the set of modern technologies – from the printing press to the Internet – which facilitate communication across space, time, and social collectives.

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Get 10% off with 24start discount code, history of the concept of media.

The Oxford English Dictionary Online (OED) notes that while classical Latin medium referred to some middle entity or state, in postclassical Latin and in British sources from the twelfth century onward, medium and media also came to denote the means of doing something. On the one hand, a medium could be understood as a more or less incidental presence, linking natural phenomena of this world and some metaphysical realm. On the other hand, a medium can serve as an intentional instrument of human action in a modern sense. In the latter respect, the OED distinguishes two conceptions – medium as an artistic modality, material, or technique; and medium as a channel of mass communication – both of them from the mid-nineteenth century. This was the period when a general idea of communication took hold (Peters 1999), partly in response to new technological means of communication with important social and aesthetic implications, from telegraph and telephone, to film, radio and, later, television. It was not until the 1960s, however, that media came into general use as a term covering diverse technologies and institutions, most commonly in the sense of mass media, communicating from one center to a mass of dispersed and anonymous receivers.

Three Disciplinary Roots of Concepts

Each media concept implies a particular understanding of the basic communication model of sender, message, and receiver. The first concept, articulated in Lasswell’s paradigm (Lasswell 1948) – who says what, in which channel, to whom, with what effect – approached the medium as a neutral conduit for the dissemination of information of all kinds. In order to assess the effects and implications of a given medium, such as a newspaper or a radio station, scholars might focus their attention on the strategies of the sender, the selectivity of the communicated message, the reach of the medium in question, or the susceptibility of the receivers to particular ideas. A great deal of subsequent work has questioned Lasswell’s focus on separate stages of communication, as associated with separate forms of media analysis. In fairness, Lasswell further emphasized the function of media as mechanisms of surveillance at a macro-level. Media are means of monitoring a society as well as its surroundings with a view to self-protection, self-regulation, and long-term stability. In this regard, media can be understood in social scientific terms as a particular set of institutions in society.

The second variant was stated in the mathematical theory of communication. Its basis was Claude Shannon’s research and development regarding the physical and technological conditions for the transfer of signals in telephone systems. A number of the insights were presented in a joint publication with Warren Weaver (Shannon & Weaver 1949), and it was this volume that influenced a good deal of theory development on media. In fact, Shannon was addressing the material aspects of how to design a communication system. In its popularized form, however, the underlying model of engineering was applied to humans as a description of social interaction. Although such applications have regularly been criticized as metaphorical and imprecise, the model has remained an important part of the heritage of communication theory. This may be due, in part, to the obvious point that media are concrete vehicles whose affordances and constraints condition their potential role in human communication. The attempt to account for media as material technologies with social implications has continued to occupy communication researchers.

The third concept derives from humanistic perspectives on media as aesthetic means of expression and as carriers of cultural and historical meaning. Rooted in centuries of rhetorical and hermeneutic scholarship, this discursive media concept received an influential formulation in Roman Jakobson’s (1960) model of communication. While carrying an outward resemblance to the models of Lasswell and Shannon and Weaver, Jakobson’s model grew out of literary theory, highlighting the various communicative functions of different linguistic and aesthetic choices by authors. Jakobson further made a distinction between the channel (what he termed contact – the material relation, such as book, newspaper, or Internet) and the code (the modalities or forms of expression, such as speech, writing, music, moving images, etc.). Compared to both Lasswell and Shannon and Weaver, however, Jakobson stayed entirely within the boundaries of the text or message, calling for an immanent analysis of how communicative functions manifest themselves in concrete textual structures, and bracketing the social contexts and uses of, for instance, literature or advertising. Much humanistic scholarship, accordingly, has approached media as forms of expression that are externalized and available for study in the form of discourses.

An Interdisciplinary Concept of Media

Particularly since the 1980s, much media research has been characterized by efforts at combining and integrating these concepts as dimensions within some form of theoretical systematic. A common position is that all three perspectives are necessary, and none of them sufficient, for a scientifically valid and socially relevant field of media studies. Interdisciplinary research and debate has explored not least the relationship between social sciences (media as institutions) and humanities (media as discourses) (for overview, see Jensen 2002b). Until recently, there appears to have been relatively less theory development devoted specifically to the interrelations between media as material technologies and media as institutions and discourses – despite the wealth of research on new media technologies as well as a growing interest in the distinctive affordances of different media technologies and their historical uses. Digitization has provided an impetus for reconsidering how, concretely, the materiality of media shapes, and is shaped by, culture and society.

The individual media can be understood as characteristic configurations of the human potential for communication at a given historical time. These configurations are organized along three dimensions – materials, modalities, and institutions – as identified in the three conceptions of a medium.

Media are physical materials which – in a particular cultural shape – enable forms of communication that previously had not been possible. Sound recordings, from the late 1800s, made possible the preservation of parts of the cultural heritage that until then had disappeared into the air. From the 1910s, recorded sound became mobile with the introduction of portable gramophones. And, from 1979, media users wearing a Walkman were able to create soundscapes that were at once mobile and private.

It is through specific forms of expression and experience that media enable human communication – language, music, moving images, etc. These modalities, on the one hand, are grounded biologically in the human senses. On the other hand, modalities have been subject to millennia of differentiation and cultivation. In modern media technologies, the modalities have entered into shifting and evolving genres – from novels and radio serials, to music videos and virtual worlds.

Institutions

Media, finally, constitute distinctive institutions in society: through media, individuals and collectives can describe and reflect upon themselves as well as the rest of society. Media and other social institutions have jointly reproduced each other under changing technological and cultural circumstances. Print and electronic media extended cultures in space and sustained nation-states over time; nation-states and international treaties regulate the legal limits of public communication and the economic bases of each new medium. Television, for example, was developed as a consumer good for the home, financed by advertising or license fees, even though the material technology might have been framed socially on the model of cinema as a public or community activity.

In comparison with other meaningful cultural artifacts and social arrangements – from interior decorating to business transactions – the media that constitute the objects of analysis in media and communication research, are distinguished by their programmability, being uniquely flexible resources for the articulation of information and communicative interaction as part of an ongoing social structuration (Giddens 1984).

Whereas programmability is most commonly associated with the various levels of the digital computer, other communication platforms also lend themselves to combinatorial configurations. First, the modalities of media amount to semiotic registers of language, music, images, etc., allowing for an immense repertoire of genres and discourses, and engaging the human senses in selective and culturally conventional ways. Thus, media make possible the rendering of and interaction with worlds past and present, real and imagined. Second, the technologies of media provide the material substratum of such representations, not as fixed conduits, but as resources for accomplishing particular social and aesthetic ends. Third, media communicate to, about, and on behalf of social institutions, which, again, are shaped and reshaped through communication. As combinatorial systems, media and societies can be said to mutually program each other – a notion that, for example, systems theory has elaborated and formalized. The degrees of freedom that condition this entire process, in three dimensions, help to account for the relative indetermination of the structures and outcomes of mediated communication, and continue to challenge research on the question of what difference the media make.

Media of Three Degrees

The coming of digital media has stimulated renewed research interest in the duality of mediated and nonmediated communication. For one thing, ordinary human conversation, while nonmediated by technologies, is mediated by aural–oral modalities, in addition to body language, broadly speaking. For another thing, computer-mediated communication – email, chat, online gaming – often carries a stronger resemblance to interpersonal than to mass communication. In order to assess the implications of digital media as emerging social and cultural institutions, much ongoing work has begun to address the interrelations between different media types (Bolter & Grusin 1999; Manovich 2001; Lievrouw & Livingstone 2002). One explanatory framework would distinguish between media of three degrees (Jensen 2002a).

Media of the first degree can be defined as the biologically based, socially formed resources that enable humans to articulate an understanding of reality, for a particular purpose, and to engage in communication about it with others. The central example is verbal language, or speech, as constitutive of oral cultures and subcultures – additional examples include song and other musical expression, dance, drama, painting, and creative arts generally, often relying on mechanical techniques such as musical instruments and artistic or writing utensils as necessary elements. Importantly, such media depend on the presence of the human body in local time–space. While one might identify (spoken) language, or the human voice, as the medium, it is helpful to differentiate between, for instance, speech and song as media with reference to their different modalities, sharing the same material substratum, but commonly addressing different social institutions, contexts, and practices.

Media of the second degree come under the classic definition by Walter Benjamin (1936/1977) of the technically reproduced and enhanced forms of representation and interaction which support communication across space and time, irrespective of the presence and number of participants. Whereas Benjamin emphasized photography, film, and radio, media of the second degree range from early modern examples, including the standardized reproduction of religious and political texts by the printing press, to television and video. The common features are, first, one-to-one reproduction, storage, and presentation of a particular content and, second, radically extended possibilities for dissemination across time and space. These technologies had important consequences for major social institutions – from the breakup of the Catholic church to the rise of the nation-state. Also, modalities from media of the first degree were refashioned. In radio talk shows, conversation took on new conventions, just as acting styles were adapted from the theater stage to cinema and television.

It is debatable whether manuscripts, which fix speech, drawing, music, and other human communication in a stable format, should be considered a separate media category, partly in view of their epochal significance. In historical perspective, Meyrowitz (1994, 54) suggested that its comparatively inefficient forms of reproduction and distribution made handwriting a transitional cultural form. For a systematics of media, and from the perspective of media and communication research as a field, it can be argued that the production of manuscripts, like other media of the first degree, is embodied and local, laborious and error-prone; that their distribution is commonly selective rather than public, within established institutions, as supported by oral commentary; and that the constitutive role of handwriting in the reproduction of cultural tradition and social institutions has been taken over by media of the second degree.

Media of the third degree are the digitally processed forms of representation and interaction. Digital technology enables reproduction and recombination of all media of the second degree on a single platform: computers, thus, can be understood as metamedia (Kay & Goldberg 1977/1999). The central current example is the networked personal computer, although this interface, like that of mobile telephones, is likely to change substantially as technologies are adapted further to the human senses, and integrated into both common objects and social arrangements. Whereas classic mass media, such as illustrated magazines and television, combined modalities to a considerable degree, the scale and speed with which digitalization facilitates their incorporation and reconfiguration suggests that digital media may represent a qualitative shift from media of the second degree that is comparable to the shift from first-degree to second-degree media. The media types have not replaced each other – they recirculate the forms and contents of shifting cultural traditions in social contexts. They do, however, offer distinctive and ascending degrees of programmability in terms of adaptable technologies, differentiated modalities, and institutions transcending time, space, and social collectives.

The Double Hermeneutics

The development both of the concept of media and of media studies indicates that media are understood in historical context. The modern, general concept of communication was, in part, a response to nineteenth-century analog technologies (Peters 1999); current debates about the concept of media may be a response to twentieth-century digital technologies. This interplay of social and conceptual changes has been called a double hermeneutics (Giddens 1984): changing social realities challenge research to deliver new interpretations and explanations – which, in turn, may change society, for example, through the design and regulation of media.

Bibliography:

• Benjamin, W. (1977). The work of art in the age of mechanical reproduction. In J. Curran, M. Gurevitch, & J. Woollacott (eds.), Mass communication and society. London: Edward Arnold. (Original work published 1936).
• Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding new media. Cambridge, MA: MIT Press.
• Giddens, A. (1984). The constitution of society. Berkeley, CA: University of California Press.
• Jakobson, R. (1960). Closing statement: Linguistics and poetics. In T. A. Sebeok (ed.), Style in language. Cambridge, MA: MIT Press.
• Jensen, K. B. (2002a). Introduction: The state of convergence in media and communication research. In K. B. Jensen (ed.), A handbook of media and communication research: Qualitative and quantitative methodologies. London: Routledge.
• Jensen, K. B. (ed.) (2002b). A handbook of media and communication research: Qualitative and quantitative methodologies. London: Routledge.
• Kay, A., & Goldberg, A. (1999). Personal dynamic media. In P. A. Mayer (ed.), Computer media and communication: A reader. Oxford: Oxford University Press, pp. 111–119. (Original work published 1977).
• Lasswell, H. D. (1948). The structure and function of communication in society. In L. Bryson (ed.), The communication of ideas. New York: Harper, pp. 32 –51.
• Lievrouw, L., & Livingstone, S. (eds.) (2002). Handbook of new media: Social shaping and social consequences. London: Sage.
• Manovich, L. (2001). The language of new media. Cambridge, MA: MIT Press.
• Meyrowitz, J. (1994). Medium theory. In D. Crowley & D. Mitchell (eds.), Communication theory today. Cambridge: Polity Press.
• Peters, J. D. (1999). Speaking into the air: A history of the idea of communication. Chicago, IL: University of Chicago Press.
• Shannon, C., & Weaver, W. (1949). The mathematical theory of communication. Urbana, IL: University of Illinois Press.

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ASTAN Media Corridor: A strategy for policymakers for bridging the gaps Between Pakistan, China, and Central Asia

21 Pages Posted: 1 Jul 2024

Hafiz Muhammad Salman

Sindh Madressa-tul-Islam University

Date Written: June 26, 2024

This research will present an exhaustive review of the media environment and give policymakers a strategy for the development of a media corridor between Pakistan, China, and Central Asia. Geographically, this region is landlocked but very rich in oil and natural resources. Especially Pakistan and Central Asia have a common history, culture, religion, and agriculture. Their friendly relations are reflected in the policy statements delivered by the government's officials, who appear in the national and international media. Theoretically, the media can affect foreign policies through its agenda-setting, agenda-reflecting, and agenda-building policies. In this research, the researcher has focused on developing a strategy for the "Astan Media Corridor" between Pakistan, China, and Central Asia. The objectives of this study are to find out the possible media's role in strengthening the relationship between Pakistan, China, and Central Asia and to explore the prospects and challenges for developing a suggested "media corridor" between Pakistan, China, and Central Asia. The "main purpose" of this research is to identify the obstacles and opportunities in relations between media organizations and people belonging to media houses in Pakistan, China, and Central Asia. The key conclusion is that the media on both ends, instead of playing a very important role in building the relationship between Pakistan, China, and Central Asia, follows the official narrative of the governments.

Keywords: "Astan Media Corridor", Strategic Depth, Electronic and Print Media, Pakistan, China, and

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Social Media Role and Its Impact on Public Health: A Narrative Review

Sushim kanchan.

1 Epidemiology and Public Health, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Abhay Gaidhane

Social media refers to online social networking sites and is a broad example of Web 2.0, such as Twitter, YouTube, TikTok, Facebook, Snapchat, Reddit, Instagram, WhatsApp, and blogs. It is a new and ever-changing field. Access to the internet, social media platforms and mobile communications are all tools that can be leveraged to make health information available and accessible. This research aimed to conduct an introductory study of the existing published literature on why to choose and how to use social media to obtain population health information and to gain knowledge about various health sectors like disease surveillance, health education, health research, health and behavioral modification, influence policy, enhance professional development and doctor-patient relation development. We searched for publications using databases like PubMed, NCBI, and Google Scholar, and combined 2022 social media usage statistics from PWC, Infographics Archive, and Statista online websites. The American Medical Association (AMA) policy about Professionalism in Social Media Use, American College of Physicians-Federations of State Medical Boards (ACP-FSMB) guidelines for Online Medical Professionalism, and Health Insurance Portability and Accountability Act (HIPAA) social media violations were also briefly reviewed. Our findings reflect the benefits and drawbacks of using web platforms and how they impact public health ethically, professionally, and socially. During our research, we discovered that social media's impact on public health concerns is both positive and negative, and we attempted to explain how social networks are assisting people in achieving health, which is still a source of much debate.

Introduction and background

The term "social media" was first used to describe the evolution of Web 2.0 applications that are open and social in nature [ 1 ]. Web 2.0 social networking sites are broad online platforms where people can communicate and share information and as we enter the digital age, this media platform is becoming more popular. With 3.81 billion active social media users in April 2020 [ 1 , 2 ], increasing access to the internet and mobile phone connections, more people have access to public health information more quickly and directly than ever before.

In the world of social media, 2020 was extremely important, world's most popular social media website, Facebook, has 1.1 billion monthly users [ 3 ] in 2013 which got increased to 2.9 million by 2022 [ 4 ]. Globally, there are more than 3.6 billion users of social media, and by 2025, that number is projected to increase to 4.41 billion [ 5 ]. It was found that YouTube is the second most actively used networking site after Facebook, with 2,562 million users in 2022. WhatsApp had 2000 million, Wein/WeChat - 1,263; TikTok - 1,000; Facebook Messenger - 988; Snapchat - 557; Telegram - 550; Pinterest - 444; Twitter - 436; Reddit - 430, and Quora - 300 million active users. Figure ​ Figure1 1 is a percentage-based compilation of global usage data from January 2022 for all mentioned social media networks.

For several reasons, online social media platforms appear to have much potential for public health campaigns. For instance, they can connect with very reasonably large audiences, Facebook has 1.1 billion monthly users [ 3 ] in 2013. Second, messages can be sent to personal contacts, possibly making them more advantageous than traditional health marketing tactics [ 6 ]. Third, user involvement and retention are typically high on online social networks in contrast to conventional web-based interventions [ 7 ]. Finally, because social media involves users taking an active role and creating content, it can be more impactful than traditional websites [ 8 ]. Various studies provided an overview of social media's potential as a tool for health interventions, socializing with supportive friends and family, talking about your emotions, healthy behavior change and counseling, health campaigns, medical education, disease outbreak surveillance, health research, and more [ 9 ]. These recent developments contemplate how social media offers healthcare professionals and patients opportunities to communicate affordably and reciprocally, which can positively impact current medical practice.

Despite the promising strategies Web 2.0 technologies and eHealth applications provide, it raises many questions, establishing trust, adhering to rules, and choosing the best content are just a few of these [ 10 ]. A lot of user-generated content (UGC) from self-media and various facts about the epidemic on social media have a significant emotional undertone [ 11 ]. It shed light on the patterns and characteristics of how users' emotional dispositions change during times of public health emergency [ 11 ], and how social networking can influence people's and groups' decision-making behavior [ 12 ], potentially increasing the risk of misinformation, various conspiracy theories, stigma, violence, and religious-cultural sentiments damage. Overusing social media has been linked to significant issues with the mental health of both adults and adolescents. Fear of missing out (FOMO) is the anxiety associated with the motivation to keep up with what other people are doing on social media. Cyberbullying, sleep disruption, stress, depression, and false prestige are just a few of the negative effects of social media on today's youth. Other challenges that can arise are offending people and defaming their relationships and reputations, either unintentionally or intentionally [ 13 ]. It can be difficult to use numerous social networking sites for medical purposes to improve communication because one must be sure that the information is accurate and easily accessible [ 10 ]. Due to concerns with compliance, trust, and patient privacy, social media has been warned about having a significant negative impact on doctor-patient relationships [ 14 ]. However, the accepted protocols for using web networks to transmit health information have not yet been investigated. Another topic that has to be investigated is how people view and use personal health data and cultural and social standards that vary by region.

This narrative study intends to shed light on the potential use of social media as a new platform for the population health and healthcare industries. It was also emphasized that it was important to examine the many difficulties that could arise when using this platform for the health sectors and to provide guidelines on certain key social media usage best practices.

The topic "Social Media Role and Its Impact on Public Health" was thoroughly researched using databases and websites for up-to-date related data and literature, such as PubMed, NCBI, and Google Scholar. Search terms included social media, social networking, public health, online health information, online health communication, online health management, social media platforms, social media usage statistics, HIPAA violation, and legal and ethical standards. In addition, an online search was conducted using a search engine such as Google to discover health sites data from five portals and websites including 2022 social media usage statistics from PWC, Infographics Archive, and Statista online domains on some of the most well-known social media toolkits. Figure ​ Figure2 2 shows the inclusion and exclusion criteria of this study and Figure ​ Figure3 3 shows the summary status of this study.

The image is created by the author (ASR) of this study.

Role of social media in public health

Disease Surveillance and Public Health Surveillance

Social-networking sites for clinicians, patients, and the general public hold potential for harnessing the collective wisdom of the masses for public health surveillance. Organizations like the Global Outbreak Alert and Response Network of the World Health Organization (WHO) also relies on web sources for up-to-date surveillance activities as data were not captured via any traditional method [ 15 ]. A 2018 study conducted by Yasmin and her team examined geolocated tweets for public health surveillance during a mass gathering in Canada and compare Twitter data against other data sources for heat alerts during the 2015 Pan/Parapan American Games. His study stated that Syndromic surveillance uses pre-diagnostic data, and the inclusion of syndromic data sources in public health surveillance for mass gatherings has been shown is useful [ 16 ].

The platform has an opportune disease surveillance area, improving its capability to detect disease outbreaks. Media and techniques known as user-generated information and real-time information surveillance of various public health outcomes, such as influenza, foodborne illnesses, or heat alerts, can identify cases of infectious diseases more quickly, which in the case of alerts, may permit investigation or action. A study conducted by Wakamiya et al. in 2018 using Twitter to detect Influenza outbreaks via geotagging tweets and trapped sensors supported the evidence [ 17 ]. Another study, Platform for Automated Extraction of Disease Information from the Web, by Arsevska et al., developed a platform to detect automatically animal infection outbreaks in France from their online news sources (PADI-web). Data were retrieved from 4,500 news websites, including Google News [ 18 ]. This paper serves as an excellent illustration of how such a web-based system has been fully implemented and evaluated. Using text categorization, authors Effland et al. created a system for finding foodborne illnesses reported in Yelp restaurant reviews. The New York City Department of Health and Mental Hygiene (DOHMH) uses the system to track concerns about foodborne illnesses on Yelp [ 19 ].

Health Researchers

Studies have shown that social media is used by health researchers for a variety of research-related goals. The platform is most frequently utilized to find participants and get data from the Internet (e.g., content analysis of social media posts and data mining on social media) [ 20 ]. It also helps in networking with colleagues and knowledge users, to distribute public health research, for example, sharing links about scientific publications or research on social media can help to broaden readership, exponentially increasing reach [ 21 ]. We identified the three most popular social media platforms in 2020; Facebook, Twitter, and Instagram, as the major social media platforms in use for health research [ 22 ]. Professional associations, public health organizations (such as WHO and the Centers for Disease Control and Prevention (CDC)), and hospitals frequently communicate via social media about science and health [ 21 ]. Social media is used by all major news organizations, giving additional distribution channels and ways to mix current events with smartphone capabilities [ 21 ].

Enhance Professional Development

There are many opportunities for professional engagement outside of conventional contexts due to the growing social media presence of academics, physicians, business professionals, public health departments, and healthcare systems. Public health experts can interact with the public through several Twitter chats, including CDC chats [ 21 ].

Influence Policy

Sharing arguments in favor of or against health policies with the general public, decision-makers, and other important stakeholders is made possible by social media. Using social media to inform constituents about proposed laws and encouraging them to contact political representatives to voice their ideas can have an impact on politicians' behavior because politicians are driven to please their voters. Social media is becoming more and more important in discussions of politics and policy, as evidenced by the president's massive Twitter following and usage of the platform to interact directly with the people [ 21 ].

Combat Misinformation

Suppose public health professionals are more active on social media. In that case, it may be possible to mitigate the impacts of people making false claims and to boost fact-checking initiatives by making more accurate online health information available. Hence, social media interaction with experts might help dispel incorrect information [ 21 ].

Health and Behavioral Change

In a study released in 2022 by Bonar et al., the authors employed social media adverts to target young people who engaged in risky drinking and came up with some encouraging findings [ 23 ]. The prevalence of cannabis use among emerging adults (aged 18 to 25) necessitates preventive measures. Bonar et al. conducted another study in 2022 to develop an eight-week persuasive questioning and behavioral intervention focusing on cannabis usage among emerging adults using the unique platform of social media [ 24 ].

Health Promotion

The included research showed that various social media outlets could raise the degree of women's health promotion [ 25 ], awareness of menstrual hygiene, understanding of breast cancer awareness [ 26 ], breastfeeding techniques [ 27 ], and adherence, self-perception and promotion of oral health [ 28 , 29 ], significant use of antibiotics [ 30 ], consistency with exercise, sexual health promotion [ 31 ], road safety awareness [ 32 ], smoking cessation, adverse drug reaction reporting [ 33 ], and many more in a row.

Healthcare Provider’s Perspectives on Social Media Usage

Healthcare professionals (HCPs) are constantly looking for better and more effective ways to reach greater audiences, particularly those who were difficult to reach through conventional techniques. This social networking platform offers professionals for health promotion with cost-effective opportunities to advance their careers by building communities of professionals, participating in professional development activities, and meliorating classroom learning [ 34 ]. These interactive tools and platforms are already commonplace in clinical settings, and many practitioners use them to connect with their target audience on both a personal and professional level [ 34 ].

Patients’ Perspectives on Social Media Usage

In social media, there are 74% of Internet users, and 80% of those use social media to research doctors, hospitals, and medical news and information [ 35 ]. Consumers on social media who view health-related consumer reviews are 42% and 32% of users share their friends' or family members' health experiences (PWC) [ 36 ]. In the most popular use social platform Facebook, 28% of health conversations support a health-related cause (from the Infographics Archive) [ 36 ]. Susannah Fox, Chief Technology Officer at the U.S. Department of Health and Human Services, refers to this emerging trend as "peer-to-peer health care," explaining that "patients are willing to share what they know, related to health, treatments, sources, facilities." "Peer-to-peer health care" is described by Fox as "the most exciting innovation in health care today" [ 13 ].

Due to patients' improved knowledge of health information and their increased involvement in maintaining their health, social media has indisputably altered the relationship between patients and practitioners. Other areas where social media can be helpful for patients include identifying health professionals, peer support and sharing experiences, promoting healthy behavior, and so on [ 37 ]. It may improve health outcomes by facilitating communication about health issues between general health professionals, patients, and the public.

Table ​ Table1 1 enlists studies conducted all over the world on the use of social media as a tool in various health sectors.

Challenges to using social media for health purposes

Misinformation

The longest impediment to the internet, in general, is that it's open to everyone; anyone can post information on any topic they want [ 10 ]. This turns people into self-appointed experts and (knowingly or unknowingly) spreads false information, certain online information can have different points of view and vary depending on geographical and cultural factors [ 10 ]. These conflict-causing situations are tactics to deal with, and how users sharing that information can be protected are some of the digital era’s other challenges.

Patient Privacy Concerns

Due to data confidentiality concerns, some patients are hesitant to share information via the web platform [ 36 ]. Posting distinguishable health information on these platforms without concern for patients would undoubtedly create unfaith and overstep their privacy boundary, as well as question occupation as a whole. If the staff does not follow the necessary guidelines, social media can lead to HIPAA violations (Health Insurance Portability and Accountability Act) [ 36 ]. In some cases, the content posted by healthcare professionals on their social accounts was so bad that the provider who published the content that drew criticism left the platform altogether - Reputational Harm [ 10 ].

Social Media and Mental Health

Although the majority of college students use social media without incident, a minority percentage of users engage in excessive or compulsive behavior on these platforms. Problematic social media use is a behavioral addiction defined by excessive worry over online activities, uncontrollable cravings to access or use social media, and spending so much time and energy on social media that it has a negative influence on significant parts of one's life. In India, 19.9% of college students that use social media have problematic social media [ 43 ]. It is understandable that parents, policymakers, and researchers all want to know how adolescents' frequent use of social media affects their mental health because it gives them numerous opportunities to engage in risky behaviors, join questionable communities, and interact with strangers without parental supervision [ 44 ].

Public Health Emergencies and Social Media

People and communities experience stress during public health emergencies. It is challenging to disseminate official public health information on infectious diseases because people frequently get information from social contacts through personal interactions or social media, subject to bias and misunderstanding. Misinformation during the Ebola outbreak in the Democratic Republic of the Congo in 2019 was associated with aggression, mistrust, social unrest, and targeted assaults on healthcare workers [ 45 ]. During the SARS outbreak in China in 2002-2003, Asian people faced social stigma as a result of their fear of contracting the disease [ 45 ]. Rumors about COVID-19 have been labeled a global enemy by the UN secretary-general [ 45 ]. Although debunking research has demonstrated that well-designed corrections can reduce the effects of false information, nothing is known about the effects of correction in the context of protracted social media arguments [ 46 ].

Subjecting Ethical Issue

Patients must be empowered with accurate and up-to-date information about their health to make fully informed treatment decisions, as their autonomy should be valued [ 47 ]. Physicians should be held accountable for using the powers endowed upon them by the patient's trust as trust is a pillar of the medical profession [ 47 ], and their healthcare practice should be motivated by good intentions at all times. Indeed, when using social media to connect with patients directly or share information, physicians as healthcare professionals should always consider the proper measure while communicating or sharing content [ 47 ]. When using social media, one should be mindful of the rules and ethical considerations. Information subjecting to harm someone's beliefs, norms, or any religious concerns is another major issue.

Legal Requirements for Social Media Use in the Health Sector

Guidelines are especially useful in new and evolving areas. If information or practices turn out to be incomparable, guidelines are created to connect them [ 2 ]. The guidelines aim to suggest, discover, and guide learners through questionnaires [ 2 ]. Medical schools have taken disciplinary action against amateurish digital information posted by medical students, including dismissal in some cases [ 48 ]. In November 2010, the American Medical Association (AMA) issued a policy statement about how professionals should use social media channels cautiously, separating professional and personal profiles and keeping patient details private [ 48 ]. Similarly, the Accreditation Council for Graduate Medical Education (ACGME) outlines some similar areas [ 49 ]. The American College of Physicians-Federations of State Medical Boards (ACP-FSMB) guidelines on Online Medical Professionalism state that guidelines are a baseline and starting point which needs to be evolved or restructured timely by parallelly adopting advanced technologies and eventually emerging with best practices [ 2 ]. They also worked on social media and web networking usage guidelines [ 2 ]. Nevertheless, there are still concerns about maintaining professional boundaries when using social media. In addition, there are still no agreements on what constitutes professional Internet behavior, except for the most horrific mistakes in professional unethical and illegal activities [ 49 ]. Even though it is difficult to measure and teach professionalism objectively, progress is being made in areas such as confidential patient details, pharmaceutical companies’ involvement details, ethics, and a lawsuit, skills of interaction, and portable health insurance [ 49 ].

Mix influence

Social media platforms allow for the exchange of health-related information, health promotion, policy influence, the development of relationships between healthcare practitioners and patients, the identification of drug misuse or misunderstandings among the general public, the dissemination of accurate information, and the collection of concurrent health data. This platform is used by public health organizations such as the WHO's Global Outbreak Alert and Response Network for real-time surveillance [ 15 ]. It appears to be a useful platform for health researchers looking to recruit participants and collect data from the Internet. These online platforms appear to be a useful way of providing behavioral counseling, lending credence to the idea that social networking influences individual and group decision-making. Trust, compliance, and knowledgeable content should be prioritized for social media to have an impact on the population, which in turn has an impact on public health. More research is needed to determine how to promote healthy behaviors and collect and disseminate reliable information using these tools. False-positive data, on the other hand, continues to impede the accuracy of the Internet-based monitoring system. Patient privacy concerns, as well as religious-cultural sentiments, can all be easily violated as a result of an undefined policy of using social media to spread violence and disbelief. Social media acts as a quick platform during a public emergency for disseminating rumors, exposing false information and conspiracy theories, and escalating fear and stigma directed at specific people and locations. Numerous challenges, including authority, professionalism, confidentiality, customs, information quality, and secrecy, as well as the tremendous role that social networks play in medical and public health care, remain unaddressed.

Conclusions

Our findings suggest that social media is an emerging platform with numerous opportunities for us to use it in public health and that it has an impact on the relationship between physician and patient, public trust in the system, and potential lawsuits, as well as changes in various health sectors such as health interventions, behavioral modification and promotion, health campaigns, medical education, disease outbreak surveillance, health research, and more. Because of the two ends of the spectrum, our analysis shows that, while social media can be a powerful tool for the public health sector in the current digital era, there are also drawbacks to consider. These booming platforms are not exempt from these drawbacks, which include potential moral, ethical, legal, and privacy violations, professional behavior concerns, compliance-related issues, and societal ramifications. In addition, some major ethical issues are discussed briefly, the AMA policy about professionalism in social media use, ACP-FSMB guidelines for Online Medical Professionalism, and HIPAA social media violations are used to present certain proposed regulations and guidelines for using social media for the population's health, which may be applied for avoiding such consequences.

To summarize, even if there are multiple issues, risks, and dangers, we can overcome these obstacles and utilize technology to its fullest extent if problems are addressed, acknowledged, and tried to be eliminated. Focusing on how we might use social media and its attendant demands is both necessary and ethical because it may be difficult to achieve continuous growth and evolution without setting adequate criteria and regulations for doing so. The limited and conflicting results of critical evaluations of previously published research on the influence of social media on public health issues give credibility to this argument. Our research indicates that the use of social media in public health has conflicting results, and it is advised that more research be done in this area.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

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Public Trust in Government: 1958-2024

Public trust in the federal government, which has been low for decades, has increased modestly since 2023 . As of April 2024, 22% of Americans say they trust the government in Washington to do what is right “just about always” (2%) or “most of the time” (21%). Last year, 16% said they trusted the government just about always or most of the time, which was among the lowest measures in nearly seven decades of polling.

When the National Election Study began asking about trust in government in 1958, about three-quarters of Americans trusted the federal government to do the right thing almost always or most of the time.

Trust in government began eroding during the 1960s, amid the escalation of the Vietnam War, and the decline continued in the 1970s with the Watergate scandal and worsening economic struggles.

Confidence in government recovered in the mid-1980s before falling again in the mid-’90s. But as the economy grew in the late 1990s, so too did trust in government. Public trust reached a three-decade high shortly after the 9/11 terrorist attacks but declined quickly after. Since 2007, the shares saying they can trust the government always or most of the time have not been higher than 30%.

Today, 35% of Democrats and Democratic-leaning independents say they trust the federal government just about always or most of the time, compared with 11% of Republicans and Republican leaners.

Democrats report slightly more trust in the federal government today than a year ago. Republicans’ views have been relatively unchanged over this period.

Since the 1970s, trust in government has been consistently higher among members of the party that controls the White House than among the opposition party.

Republicans have often been more reactive than Democrats to changes in political leadership, with Republicans expressing much lower levels of trust during Democratic presidencies. Democrats’ attitudes have tended to be somewhat more consistent, regardless of which party controls the White House.

However, Republican and Democratic shifts in attitudes from the end of Donald Trump’s presidency to the start of Joe Biden’s were roughly the same magnitude.

Among Asian, Hispanic and Black adults, 36%, 30% and 27% respectively say they trust the federal government “most of the time” or “just about always” – higher levels of trust than among White adults (19%).

During the last Democratic administration, Black and Hispanic adults similarly expressed more trust in government than White adults. Throughout most recent Republican administrations, White Americans were substantially more likely than Black Americans to express trust in the federal government to do the right thing.

Note: For full question wording, refer to the topline . White, Black and Asian American adults include those who report being one race and are not Hispanic. Hispanics are of any race. Estimates for Asian adults are representative of English speakers only.

Sources: Pew Research Center, National Election Studies, Gallup, ABC/Washington Post, CBS/New York Times, and CNN Polls. Data from 2020 and later comes from Pew Research Center’s online American Trends Panel; prior data is from telephone surveys. Details about changes in survey mode can be found in this 2020 report . Read more about the Center’s polling methodology . For analysis by party and race/ethnicity, selected datasets were obtained from searches of the iPOLL Databank provided by the Roper Center for Public Opinion Research .

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Committee for a Responsible Federal Budget

Trump and biden: the national debt.

The national debt is on course to reach a record share of the economy under the next presidential administration, due in part to policies approved by Presidents Trump and Biden during their time in office, including executive actions and legislation passed by Congress. 

While it is important to understand the fiscal impact of the promises candidates make on the campaign trail – particularly because they reflect the candidates’ own policy preferences and are not impacted by unexpected external events or the actions of Congress – the fact that both leading candidates have served as President also allows for a comparison of their actual fiscal records. This analysis focuses on the estimated ten-year debt impact of policies approved by Presidents Trump and Biden around the time of enactment. 1 In this analysis, we find:

• President Trump  approved $8.4 trillion of new ten-year borrowing during his full term in office, or $4.8 trillion excluding the CARES Act and other COVID relief.
• President Biden , in his first three years and five months in office, approved $4.3 trillion of new ten-year borrowing, or $2.2 trillion excluding the American Rescue Plan.
• President Trump approved $8.8 trillion  of gross new borrowing and $443 billion  of deficit reduction during his full presidential term. 
• President Biden has so far approved $6.2 trillion of gross new borrowing and $1.9 trillion of deficit reduction.

In companion analyses, we will show:

• Roughly 77 percent  of President Trump’s approved ten-year debt came from bipartisan legislation, and 29 percent  of the net ten-year debt President Biden has approved thus far came from bipartisan legislation. The rest was from partisan actions.
• President Trump approved $2.2 trillion of debt in his first two years in office and $6.2 trillion  ($2.6 trillion non-COVID) in his second two years. President Biden approved $4.9 trillion ($2.9 trillion non-COVID) in his first two years in office and has so far approved over $600 billion of net ten-year deficit reduction since. 
• President Trump approved $5.9 trillion of net spending increases including interest ($2.8 trillion non-COVID) and $2.5 trillion of net tax cuts ($2.0 trillion non-COVID). President Biden has approved $4.3 trillion of net spending increases including interest ($2.3 trillion non-COVID) and roughly $0 of net tax changes ($60 billion revenue increase non-COVID).
• Debt held by the public rose by $7.2 trillion during President Trump’s term including $5.9 trillion in the first three years and five months. Debt held by the public has grown by $6.0 trillion during President Biden’s term so far. 
• President Trump’s executive actions added less than $20 billion to ten-year debt on net. President Biden’s executive actions have added $1.2 trillion to ten-year debt so far. 
• The President’s budget was on average 39 days late under President Trump and 58 days late under President Biden. 

Summary Table: Executive Actions & Legislation Approved by Presidents Trump & Biden

Note: bipartisan indicates legislation passed with votes from both political parties in either chamber of Congress. *Includes $23 billion of executive actions in the form of student debt payment pauses. 

How Much Debt Did President Trump Approve?

During his four-year term in office, President Trump approved $8.4 trillion  of new ten-year borrowing above prior law, or $4.8 trillion  when excluding the bipartisan COVID relief bills and COVID-related executive actions. Looking at all legislation and executive actions with meaningful fiscal impact, the full amount of approved ten-year borrowing includes $8.8 trillion of deficit-increasing laws and actions offset by $443 billion of deficit-reducing actions. 2

These estimates are based on scores of legislation and executive actions rather than retrospective estimates. Scores are generally made on a conventional basis, though the Tax Cuts and Jobs Act (TCJA) is scored dynamically. The actual debt impact of the policies was likely somewhat higher than these scores. In particular, the TCJA likely reduced revenue more than projected and saved less from repealing the individual health care mandate penalty, 3 while the Employee Retention Credit was likely far more expensive than originally estimated.

Sources: CRFB estimates based on CBO and OMB projections.

The major actions approved by President Trump (and ten-year impact with interest) include:

• The Tax Cuts and Jobs Act of 2017 ( $1.9 trillion debt increase )
• The Bipartisan Budget Acts of 2018 and 2019 ( $2.1 trillion debt increase ) 
• ACA Tax Delays and Repeals ( $539 billion debt increase )
• Health Executive Actions ( $456 billion debt increase ) 
• Other Legislation ( $310 billion debt increase )  
• New and Increased Tariffs ( $443 billion debt reduction )
• The CARES Act ( $1.9 trillion debt increase ) 
• The Response & Relief Act ( $983 billion debt increase ) 
• Other COVID Relief ( $756 billion debt increase )

How Much Debt Has President Biden Approved?

Over his first three years and five months in office, President Biden has approved $4.3 trillion  of new ten-year borrowing, or $2.2 trillion  when excluding the American Rescue Plan Act. This includes $6.2 trillion of deficit-increasing legislation and actions, offset by $1.9 trillion of legislation and actions scored as reducing the deficit.

These estimates are based on scores of legislation and executive actions rather than retrospective estimates and do not include preliminary rules, unexecuted “side deals,” or actions ruled illegal by the Supreme Court. Updated scores and in-process actions would increase the total. For example, an updated estimate would likely wipe away the $252 billion of scored savings from the Inflation Reduction Act, 4 the informal FRA side deals would reduce its savings by  about $500 billion , and the new student debt cancellation plan could cost  $250 to $750 billion .

The major actions approved by President Biden so far (and ten-year impact with interest) include:

• Appropriations for FY 2022 and 2023 ( $1.4 trillion debt increase ) 
• The Honoring Our PACT Act ( $520 billion debt increase )
• The Bipartisan Infrastructure Law ( $439 billion debt increase ) 
• Other Legislation ( $422 billion debt increase )
• Student Debt Actions ( $620 billion debt increase )
• Other Executive Actions ( $548 billion debt increase ) 
• The Fiscal Responsibility Act ( $1.5 trillion debt reduction )
• The Inflation Reduction Act ( $252 billion debt reduction )
• Deficit-Reducing Executive Actions ( $129 billion debt reduction )
• The American Rescue Plan Act ( $2.1 trillion debt increase )

The next presidential term will present significant fiscal challenges. While past performance is not necessarily indicative of future actions, it is helpful to examine the fiscal performance from each President’s time in office for clues as to how they plan to confront these challenges or how high of a priority fiscal responsibility will be on their agendas.

Both candidates approved substantial amounts of new borrowing in their first term. President Trump approved $8.4 trillion in borrowing over a decade, while President Biden has approved $4.3 trillion so far in his first three years and five months in office. Of course, accountability also rests with Congress as a co-equal branch of government, which passed legislation constituting the majority of the fiscal impact under both presidents.

Some of this borrowing was clearly justified, particularly in the early parts of the COVID-19 pandemic when joblessness was rising rapidly and large parts of the economy were effectively shut down. However, funding classified as COVID relief explains less than half of the borrowing authorized by either President, and arguably, a meaningful portion of this COVID relief was either extraneous, excessive, poorly targeted, or otherwise unnecessary. 5

In supplemental analyses, we will compare a number of other aspects of the candidates’ fiscal records. 

During the next presidential term, the national debt is projected to reach a record share of the economy, interest costs are slated to surge, the debt limit will re-emerge, discretionary spending caps and major tax cuts are scheduled to expire, and major trust funds will be hurtling toward insolvency. 

Adding trillions more to the national debt will only worsen these challenges, just as both Presidents Trump and Biden did during their terms along with lawmakers in Congress. The country would be better served if the candidates put forward and stuck to plans to reduce the national debt, secure the trust funds, and put the budget on a sustainable long-term path.

Appendix I : Details of Policies Approved by President Trump

• Tax Cuts and Jobs Act of 2017 ( $1.9 trillion debt increase )   – The TCJA included several tax cuts and reforms. Among those changes, the law reduced individual and corporate income tax rates, virtually eliminated the alternative minimum taxes, repealed or limited numerous deductions and tax breaks, replaced personal and dependent exemptions with an expanded standard deduction and Child Tax Credit, established a new deduction for pass-through business income, shrunk the estate tax, offered full expensing of equipment purchases, and reformed the tax treatment of international income. Most individual and estate tax changes were temporary while most corporate changes were permanent. The legislation also repealed the Affordable Care Act’s individual mandate penalty. As a result of these policy changes, the Congressional Budget Office (CBO) projected the TCJA would boost output by roughly 1 percent at peak and 0.6 percent after a decade. The estimate incorporated in this analysis includes the dynamic feedback effects of this faster growth, based on CBO’s April 2018 analysis of the bill. While it is impossible to know exactly how the bill’s fiscal impact compared to this prospective estimate, a number of factors point towards it adding significantly more to the debt, including: higher-than-expected inflation and nominal incomes and profits leading to higher revenue loss; SALT cap workarounds; increased use of bonus depreciation; and lower than expected revenue from limiting the use of pass-through losses. As a reference point, CBO’s latest estimate for extending the expiring elements of the TCJA is almost  50 percent higher than its 2018 estimate. In addition, the budgetary savings from the individual mandate penalty repeal were likely less than originally projected.
• The Bipartisan Budget Acts of 2018 and 2019  ( $2.1 trillion debt increase )   – The Bipartisan Budget Acts (BBA) of 2018 and 2019 increased the caps on defense and nondefense discretionary spending set by the 2011 Budget Control Act (BCA) and further reduced through a ‘sequester’ activated after the failure of the Joint Select Committee on Deficit Reduction. BBA 2018  increased the caps in FY 2018 and 2019 by a combined $296 billion,  effectively repealing the $91 billion per year sequester and further increasing spending above the BCA caps. BBA 2019 essentially codified these increases by  boosting the FY 2020 and 2021 caps by a combined $320 billion. Because the 2021 cap was the final year of the BCA caps, BBA 2019 increased baseline discretionary spending levels beyond 2021 to the new 2021 level plus inflation. Both bills also included smaller additional policies, including some partial offsets. In total, BBA 2018 added $418 billion to the ten-year debt and BBA 2019 added $1.7 trillion.
• ACA Tax Delays and Repeals  ( $539 billion debt increase )   – Three taxes enacted by the 2010 Affordable Care Act (ACA) – the health insurer tax, the “Cadillac tax” on high-cost health insurance, and the medical device excise tax – were delayed in a 2018 continuing resolution. They were subsequently repealed in one of the full-year funding bills for FY 2020. The Joint Committee on Taxation (JCT) estimated that the health insurer tax would have raised about $150 billion over a decade, the Cadillac tax would have raised $200 billion, and the medical device excise tax would have raised $25 billion. In addition to these tax repeals, policymakers enacted roughly $70 billion of other unpaid-for policies related to health care, retirement savings, and other priorities in these two bills. Interest costs added $64 billion more.
• Health Executive Actions  ( $456 billion debt increase )   – President Trump approved two health-related executive actions with significant costs over his term. Ending federal appropriations for the  ACA’s cost-sharing reduction payments in 2017 led insurers to raise premiums on “silver” ACA plans to fund low-income cost sharing subsidies, ultimately increasing the cost of federal subsidies by an estimated $220 billion. Meanwhile, a  2020 rule to restrict prescription drug rebates paid to pharmacy benefit managers and insurer plans was estimated to cost $177 billion. Interest costs added $59 billion more. Importantly, the rebate rule was delayed and ultimately repealed by Congress under President Biden.
• Other Legislation ( $310 billion debt increase )   – President Trump signed a number of other deficit-increasing bills into law over the course of his term. This includes several appropriations bills for disaster relief as well as the changes to mandatory programs (CHIMPs) that boosted spending in the full-year appropriations bills enacted in his term. Additionally, President Trump signed a permanent extension of several tax “extenders,” which are tax policies that have been routinely extended for short periods. Finally, he signed the Great American Outdoors Act, which transferred certain offsetting receipts and authorized them to be spent without appropriation, and the permanent authorization of the 9/11 victims fund, which authorized funds to pay out claims to 9/11 victims.
• Tariffs  ( $443 billion debt reduction )   – Over the course of his presidency, President Trump used his authority under the Trade Act of 1974 and the International Emergency Economic Powers Act of 1978 to increase a number of import tariffs through executive action. Beginning in 2018, the Trump Administration announced the imposition or increase to a variety of tariffs, including on washing machines, solar panels, and steel and aluminum products. In 2019, the tariff rate on many Chinese imports was increased from 10 percent to 25 percent. Based on CBO’s estimates at the time, we estimate these tariffs will have generated over $440 billion of revenue and interest savings over a decade.
• The CARES Act  ( $1.9 trillion debt increase ) – Enacted in the wake of the COVID-19 pandemic in March 2020, the bipartisan CARES Act included expanded and extended unemployment benefits, economic relief checks of $1,200 per eligible adult and $500 per child, the Paycheck Protection Program (PPP) to provide support to small businesses to keep employees on payroll, and emergency disaster loans and grants to businesses, industries, health care facilities, educational institutions, state and local governments, and others, among many other provisions. Based on our ongoing  tracking , the actual fiscal impact of the CARES Act was likely similar to the initial score though perhaps slightly higher overall.
• The Response & Relief Act  ( $983 billion debt increase )   – Enacted in December 2020 as part of the omnibus appropriations bill for Fiscal Year (FY) 2021, the  Response & Relief Act included funding for a second tranche of PPP payments and small business grants, an extension of enhanced unemployment benefits, economic relief checks of $600 per eligible person, funding support for schools and higher education institutions, vaccine and testing funding, targeted support to industries greatly impacted by COVID-19, an extension and expansion of the Employee Retention Credit, and an extension of various other COVID-related tax and spending relief programs. Based on our ongoing  tracking , the actual fiscal impact of the Response & Relief Act was likely higher than the initial score due to the significantly higher-than-expected deficit increase from the  Employee Retention Credit .
• Other COVID Relief  ( $756 billion debt increase )   – President Trump approved several other measures related to the COVID-19 pandemic and recession. This includes the three other COVID relief laws enacted in March and April 2020: the  Coronavirus Preparedness and Response Supplemental Appropriations Act , the  Families First Coronavirus Response Act , and the  Paycheck Protection Program and Health Care Enhancement Act . It also includes the student loan repayment pauses enacted at the onset of COVID and extended after the CARES Act’s pause ended in October 2020. President Trump also approved  other executive actions that resulted in little deficit impact. Based on our ongoing  tracking , the actual fiscal impact of these bills were likely much higher than the initial score due to the significantly higher-than-expected revenue loss from the  Employee Retention Credit and the higher Medicaid and SNAP costs resulting from a longer-than-projected public health emergency.

Appendix II : Details of Policies Approved by President Biden So Far

• Appropriations for FY 2022 and 2023  ( $1.4 trillion debt increase )   –President Biden signed full-year omnibus appropriations bills for  FY 2022 and  2023 , boosting nominal appropriations by 6 percent and then 9 percent. While those bills only set funding for those specific years, future-year projected levels are calculated by assuming continued inflation growth. This is consistent with the reality that appropriators generally work from the prior year’s spending levels. Based on CBO, we estimate the FY 2022 omnibus directly increased spending by $50 billion and indirectly by $519 billion above baseline, while the FY 2023 omnibus increased spending directly by $58 billion and base discretionary spending indirectly by $511 billion. Interest costs added $175 billion more. Both laws’ impacts on baseline deficits would be substantially smaller had they been scored against an updated CBO baseline that reflected actual inflation rather than projections – the bulk of the increases under both laws kept spending apace with the very-high rate of inflation for those years.
• The Honoring Our PACT Act ( $520 billion debt increase )   – Enacted in August 2022, the PACT Act created new benefits for veterans exposed to toxic substances during their tours of duty, expanded existing health and disability benefits, and modified eligibility tests that allowed more veterans to automatically qualify for benefits. Although veterans’ health spending is generally discretionary, the PACT Act allowed the cost of the expansion to be classified as mandatory spending and allowed lawmakers to  shift existing discretionary costs to the mandatory side of the budget. Based on CBO’s score, the PACT Act increased spending by between $277 billion and $667 billion, depending on how much funding was reclassified. Our estimate reflects the midpoint (plus interest), which policymakers effectively codified in the Fiscal Responsibility Act of 2023. 
• The Bipartisan Infrastructure Law  ( $439 billion debt increase )   – The 2021  Infrastructure Investment and Jobs Act authorized more than $500 billion of direct spending and tax breaks related to surface transportation, broadband, energy and water, transit, and other infrastructure. The law also increased baseline levels of highway spending, translating to more than $50 billion in indirect costs. While lawmakers claimed that it was fully paid for at the time of passage, CBO determined that it only contained $173 billion of scorable savings, leading to $439 billion of new borrowing when interest is included.
• Other Legislation  ( $422 billion debt increase )  –  President Biden signed several other bipartisan pieces of legislation during his first term. This includes  several   packages   of aid to Ukraine, Israel, and Gaza, additional emergency spending related to disaster relief and military readiness, $80 billion of investments and tax credits to encourage onshoring manufacturing facilities for semiconductors in the CHIPS and Science Act, and additional FY 2024 appropriations spending based on  “side deals” to the Fiscal Responsibility Act.
• Student Debt Actions ( $620 billion debt increase )   – The Biden Administration has instituted several changes to the federal student loan program through executive actions. Most significantly, the Education Department introduced the Savings on a Valuable Education (SAVE)  income-driven repayment (IDR) program , which reduced required payments and interest accrual for those enrolled, among other changes – estimated to cost $276 billion. In addition, President Biden extended the  pause of student debt repayments and cancellation of interest for 31 months at a cost of $146 billion. And finally, President Biden enacted a number of targeted debt cancellation measures, including expansions of the Public Service Loan Forgiveness program and cancellation of debt borrowed for institutions that closed or were found to be fraudulent, at a cost of $145 billion. President Biden also enacted a policy to cancel up to $20,000 per borrower of student debt that would have cost an additional $330 billion (after interactions with the SAVE plan), but this was ruled illegal by the Supreme Court. Recently, the Administration introduced  an alternative debt cancellation plan that could cost between $250 and $750 billion, though it has yet to be implemented and is not counted here because our estimates only include regulations that have been finalized through the full rulemaking process.
• Other Executive Actions  ( $548 billion debt increase )   – President Biden has also expanded deficits through other executive actions. Most significantly, he approved over $200 billion of borrowing by  changing the way Supplemental Nutrition Assistance Program (SNAP) benefits – also known as food stamps – are calculated and adjusted. More recently, the Administration announced a rule to limit vehicle emissions, which we estimate will add nearly $170 billion to the debt by boosting the cost of electric vehicle tax credits expanded under the IRA and reducing gas tax revenue. Other executive actions will add a combined $180 billion to the debt by expanding Medicaid enrollment, changing the way prescription drug price concessions are considered by Medicare plans, addressing the ACA’s “family glitch,” allowing states to boost Medicaid payments to managed care plans to pull in additional federal dollars, and an expansion of allowed income for Supplemental Security Income recipient households.
• Fiscal Responsibility Act ( $1.5 trillion debt reduction )   – In June 2023, President Biden signed the bipartisan Fiscal Responsibility Act (FRA), which capped discretionary spending for FY 2024 and 2025, among other changes. The FRA set 2024 nondefense discretionary levels to 5 percent below the 2023 level, set defense to be 3 percent higher, and set both to grow by 1 percent between 2024 and 2025. These  caps, along with other measures, were scored to generate over $250 billion of direct savings and also reduce the baseline for future spending to generate an additional $1.1 trillion of additional savings. With interest, the FRA was estimated to reduce deficits by $1.5 trillion over a decade. Importantly, negotiators at the time agreed to a number of  “side deals” mentioned above that would reduce the FRA’s savings to roughly $1 trillion if enacted in full in future appropriations bills. A different but similar set of side deals were enacted for FY 2024 and added about $85 billion to deficits – these are included in the “other legislation” category. Additional side deals will not be counted until enacted.
• Inflation Reduction Act  ( $252 billion debt reduction )   – In August 2022, President Biden signed the  Inflation Reduction Act (IRA ) into law, a reconciliation bill focused on energy, health care, and tax changes. The IRA established new and increased existing energy- and climate-related spending and tax credits, expanded ACA health insurance subsidies, required prescription drug negotiations and other drug pricing reforms, introduced a 15 percent corporate “book minimum tax,” established an excise tax on stock buybacks, increased funding to the IRS to close the tax gap, and made other changes. At the time of passage,  CBO and JCT estimated the IRA’s tax breaks and spending would reduce revenue and increase spending by about $500 billion, while its offsets would generate almost $740 billion. Recent estimates of the impact of repealing the IRA tax credits suggest these provisions will reduce revenue and increase spending by $260 billion higher than the official score; at the same time, the IRA’s offsets are also likely to raise more in revenue. On net, we expect a full re-estimate of the IRA would score as roughly budget neutral through 2031, excluding effects related to subsequent regulatory changes. This analysis attributes the additional cost of these regulations as executive actions.
• Deficit-Reducing Executive Actions  ( $129 billion debt reduction )   –President Biden approved two other executive actions that would result in savings over a decade, including changes to payments for Medicare Advantage plans and a temporary stay of the subsequently repealed Trump prescription drug rebate rule.
• American Rescue Plan Act  ( $2.1 trillion debt increase )  –  Enacted in the Spring of 2021, the American Rescue Plan Act was the final piece of legislation that contained many major components designed to provide COVID relief. It included several extensions of enhanced unemployment benefits, additional relief checks of $1,400 per person, and a slew of funding for state and local governments, educational institutions, health care providers, public health agencies, and others. The legislation also included  about $300 billion of policies that we have described as extraneous to the COVID crisis – including a pension bailout and expansions of the Child Tax Credit, Earned Income Tax Credit, health insurance subsidies, and child care tax credit – and roughly $100 billion of offsets.

Appendix III: Methodology 

This analysis estimates the additional borrowing approved by Presidents Trump and Biden through tax and spending changes passed by Congress or contained in executive actions from their administrations. It does not estimate the amount of debt that accumulated over their terms, which partially reflects actions taken prior to their time in office and does not account for the fiscal impact of the actions approved by the President but incurred outside of his four-year term. We will publish changes in debt during their terms in a supplemental analysis.

Our analysis incorporates all major pieces of legislation and executive actions – those with more than $10 billion of ten-year budget impact – approved by Presidents Trump and Biden. Estimates rely on ten-year budget scores, as under standard convention. In order to rely on official scores wherever possible, however, all estimates are based on the ten-year budget window at the time of enactment – meaning different policies cover different time frames and thus are not purely additive or comparable.

In general, estimates rely on official estimates from the Congressional Budget Office (CBO) and Joint Committee on Taxation (JCT) presented prospectively. When such scores are not available or not comprehensive, we may use estimates from the Office of Management and Budget, the regulatory agencies, or our own estimates. 

Estimates are not updated to incorporate data and results made available well after implementation; no legislation signed by either President Trump or President Biden has been re-estimated in full to incorporate observed costs or effects, and partial updates would bias the overall numbers. However, possible differences between initial scores and actual costs, including from the TCJA, the IRA, and COVID relief, are discussed throughout this paper.

Estimates incorporate impact on interest costs, which we calculate using the most recent CBO debt service tool at the time of enactment, unless interest impact is included in the estimate. Estimates are generally based on conventional scoring, but in the case of the Tax Cuts and Jobs Act, we incorporate macroeconomic impacts as estimated by CBO shortly after enactment.

All estimates are in nominal dollars at the time of approval, which means deficit impact from earlier budget windows generally represent a larger share of GDP per dollar due to higher price levels and output over time. 

Finally, the estimates are based on the policies as written and do not try to correct for arbitrary cliffs, side agreements, or other budget gimmicks that may create a misleading picture of the intended fiscal impact of the policy.

1 Our estimates compare ten-year estimates of each action before implementation, generally using prospective scores of policies and adding them together despite being over different windows. Although this is not a perfect apples-to-apples comparison for a variety of reasons, it allows us to rely on official numbers and continue to compare over time. See the methodology section for a more detailed explanation.

2 Many pieces of legislation with fiscal impact include tax and spending changes that both add to and reduce projected deficits. The $8.8 trillion figure is based on the net deficit impact of deficit-increasing bills, rather than the gross deficit increases within those bills. For example, the $1.9 trillion impact of the TCJA represents the combination of tax cuts, base broadening, lower spending as a result of repealing the individual mandate penalty, interest, and dynamic effects on revenue and spending.

3 The larger deficit impact from the TCJA is due to a combination of a larger nominal tax base, lower health savings from individual mandate repeal, the unexpected use of a SALT cap workaround, reduced revenue collection from the limit on pass-through losses, higher revenue loss related to bonus depreciation, and other factors.

4 Due to higher prices and output, greater demand for subsidized activities, and laxer-than-expected regulations, the IRA’s energy provisions are now expected to have a fiscal impact of  $660 billion – about two-thirds more than the original estimate of roughly $400 billion. This excludes the effects of the Administration’s vehicle emissions rule, which we’ve scored separately. At the same time, revenue collection under the IRA is also likely to be higher in light of  higher-than-projected nominal corporate profits , greater expected  voluntary tax compliance , and less-than-expected responsiveness to the buyback tax. Overall, we believe a re-estimate of the IRA would be roughly budget neutral. The emissions rule approved by President Biden would increase deficits by about $170 billion – mainly by further increasing the fiscal impact of the IRA tax credits – and is included in our tally of his executive actions.

5 In a previous analysis, we estimated that  $500 to 650 billion of COVID relief was extraneous – unrelated to the pandemic or subsequent economic fallout – including $300 to $335 billion enacted under President Trump and $200 to $315 billion under President Biden. These prior estimates are not perfectly comparable to estimates in this paper but give a sense of scale. In additional analyses, we estimated that the American Rescue plan likely  significantly overshot the output gap it was aiming to close while providing excessive relief to a number of sectors. There were also excesses and lack of targeting in earlier COVID relief packages, including as it related to  stimulus checks , the additional $600 of weekly  unemployment benefits , and the  Paycheck Protection Program.

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automated critic —

Openai’s new “criticgpt” model is trained to criticize gpt-4 outputs, research model catches bugs in ai-generated code, improving human oversight of ai..

Benj Edwards - Jun 27, 2024 7:40 pm UTC

On Thursday, OpenAI researchers unveiled CriticGPT , a new AI model designed to identify mistakes in code generated by ChatGPT. It aims to enhance the process of making AI systems behave in ways humans want (called "alignment") through Reinforcement Learning from Human Feedback (RLHF), which helps human reviewers make large language model (LLM) outputs more accurate.

Further Reading

As outlined in a new research paper called " LLM Critics Help Catch LLM Bugs ," OpenAI created CriticGPT to act as an AI assistant to human trainers who review programming code generated by the ChatGPT AI assistant. CriticGPT—based on the GPT-4 family of LLMS—analyzes the code and points out potential errors, making it easier for humans to spot mistakes that might otherwise go unnoticed. The researchers trained CriticGPT on a dataset of code samples with intentionally inserted bugs, teaching it to recognize and flag various coding errors.

The researchers found that CriticGPT's critiques were preferred by annotators over human critiques in 63 percent of cases involving naturally occurring LLM errors and that human-machine teams using CriticGPT wrote more comprehensive critiques than humans alone while reducing confabulation (hallucination) rates compared to AI-only critiques.

Developing an automated critic

The development of CriticGPT involved training the model on a large number of inputs containing deliberately inserted mistakes. Human trainers were asked to modify code written by ChatGPT, introducing errors and then providing example feedback as if they had discovered these bugs. This process allowed the model to learn how to identify and critique various types of coding errors.

In experiments, CriticGPT demonstrated its ability to catch both inserted bugs and naturally occurring errors in ChatGPT's output. The new model's critiques were preferred by trainers over those generated by ChatGPT itself in 63 percent of cases involving natural bugs (the aforementioned statistic). This preference was partly due to CriticGPT producing fewer unhelpful "nitpicks" and generating fewer false positives, or hallucinated problems.

The researchers also created a new technique they call Force Sampling Beam Search (FSBS). This method helps CriticGPT write more detailed reviews of code. It lets the researchers adjust how thorough CriticGPT is in looking for problems while also controlling how often it might make up issues that don't really exist. They can tweak this balance depending on what they need for different AI training tasks.

Interestingly, the researchers found that CriticGPT's capabilities extend beyond just code review. In their experiments, they applied the model to a subset of ChatGPT training data that had previously been rated as flawless by human annotators. Surprisingly, CriticGPT identified errors in 24 percent of these cases—errors that were subsequently confirmed by human reviewers. OpenAI thinks this demonstrates the model's potential to generalize to non-code tasks and highlights its ability to catch subtle mistakes that even careful human evaluation might miss.

Despite its promising results, like all AI models, CriticGPT has limitations. The model was trained on relatively short ChatGPT answers, which may not fully prepare it for evaluating longer, more complex tasks that future AI systems might tackle. Additionally, while CriticGPT reduces confabulations , it doesn't eliminate them entirely, and human trainers can still make labeling mistakes based on these false outputs.

The research team acknowledges that CriticGPT is most effective at identifying errors that can be pinpointed in one specific location within the code. However, real-world mistakes in AI outputs can often be spread across multiple parts of an answer, presenting a challenge for future model iterations.

OpenAI plans to integrate CriticGPT-like models into its RLHF labeling pipeline, providing its trainers with AI assistance. For OpenAI, it's a step toward developing better tools for evaluating outputs from LLM systems that may be difficult for humans to rate without additional support. However, the researchers caution that even with tools like CriticGPT, extremely complex tasks or responses may still prove challenging for human evaluators—even those assisted by AI.

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