Development of Trust Scores in Social Media (TSM) Algorithm and Application to Advertising Practice and Research

Roy, Atanu Singha; Huh, Jisu; Pfeuffer, Alexander; Srivastava, Jaideep

doi:10.1080/00913367.2017.1297272

Cited by 31 publications

(15 citation statements)

References 35 publications

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“…Trust is considered an important factor in the social sciences literature and has received increasing attention by organizational researchers who argue that in the absence of specific rules, trust is essential (Colquitt et al, 2007). This is particularly true for the social media context where platforms aggregate and generate content from a variety of news sources with different levels of trustworthiness and where people who trust this channel are shown to take actions despite the potential risks (Roy et al, 2017). As such trust frequently acts as a mental shortcut (Torres et al, 2018) and provides individuals with an excessive amount of security toward information on social media which might possibly lead them to disregard the presence of fake news.…”

Section: Social Media Information Trustmentioning

confidence: 99%

An empirical approach to understanding users' fake news identification on social media

Barakat

Dabbous

Tarhini

2021

OIR

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PurposeDuring the past few years, the rise in social media use for information purposes in the absence of adequate control mechanisms has led to growing concerns about the reliability of the information in circulation and increased the presence of fake news. While this topic has recently gained researchers' attention, very little is known about users' fake news identification behavior. Hence, the purpose of this study is to understand the factors that contribute to individuals' identification of fake news on social media.Design/methodology/approachThis study employs a quantitative approach and proposes a behavioral model that explores the factors influencing users' identification of fake news on social media. It relies on data collected from a sample of 211 social media users which is tested using SEM.FindingsThe findings show that expertise in social media use and verification behavior have a positive impact on fake news identification, while trust in social media as an information channel decreases this identification behavior. Furthermore, results establish the mediating role of social media information trust and verification behavior.Originality/valueThe present study enhances our understanding of social media users' fake news identification by presenting a behavioral model. It is one of the few that focuses on the individual and argues that by identifying the factors that reinforce users' fake news identification behavior on social media, this type of misinformation can be reduced. It offers several theoretical and practical contributions.

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Section: Social Media Information Trustmentioning

confidence: 99%

An empirical approach to understanding users' fake news identification on social media

Barakat

Dabbous

Tarhini

2021

OIR

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“…Social media, so it seems, is regarded as either an umbrella concept or a specific social medium seen as exemplary for all social media. As a result, few studies employ a holistic approach that directly compares social media platforms (Smith, Fischer, and Yongjian 2012;Roy et al 2017). Because this could result in disconnected insights, theory building might be impeded.…”

mentioning

confidence: 99%

Engagement with Social Media and Social Media Advertising: The Differentiating Role of Platform Type

Voorveld

Noort

Muntinga

et al. 2018

Journal of Advertising

528

288

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“…Knowledge of account carries the most weight goes in line with source credibility (Lewandowsky et al, 2012;Metzger & Flanagin, 2013;Tandoc et al, 2018) and source experience (Lucassen & Schraagen, 2011). Whereas the different definitions of source credibility focus on which indicators make a source credible (e.g., trustworthiness, expertise, believability, truthfulness; Roy, Huh, Pfeuffer, & Srivastava, 2017), knowledge about the account is to a large extent based on the individual's experience with the account. This can be based on previous posts or on the personal relationship that the individual might have to the account holder outside the OSN.…”

Section: Discussionmentioning

confidence: 99%

Detecting misinformation in online social networks: A think-aloud study on user strategies

Freiling¹

2019

SCM

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Although online social networks (OSN) facilitate the distribution of misinformation, one way of reducing the spread of false information in OSN is for users to detect it. Building on the framework of how audiences act to authenticate information, this study provides a user perspective on which strategies people use in evaluating information in OSN. In 15 qualitative interviews, participants were asked to think aloud while evaluating whether the content of posts from their own newsfeeds and of interviewer-supplied posts was true or false. Their answers were analyzed to determine which evaluation strategies they used. Analyzing participants’ thoughts as they evaluate information is more reliable than directly asking participants which strategies they think they use. Results show that users’ strategies in information evaluation are searching for more information, knowledge of account or content carries the most weight, and every detail needs to fit. A comparison of strategy usage for posts from befriended versus unknown personal accounts as well as for posts from followed news outlets versus not followed news outlets shows that for posts from followed news outlets, knowledge of the account was the most-used strategy followed by knowledge of the content. For other types of posts, strategy usage varied more widely and depended on each post. This highlights the importance and possible higher ecological validity of research on posts from news outlets that users actually follow, as users’ experiences with previous posts seem to play a major role in how they go about evaluating information in new posts.

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Development of Trust Scores in Social Media (TSM) Algorithm and Application to Advertising Practice and Research

Cited by 31 publications

References 35 publications

An empirical approach to understanding users' fake news identification on social media

An empirical approach to understanding users' fake news identification on social media

Engagement with Social Media and Social Media Advertising: The Differentiating Role of Platform Type

Detecting misinformation in online social networks: A think-aloud study on user strategies

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