The Effect of Platform Intervention Policies on Fake News Dissemination and Survival: An Empirical Examination

Ng, Ka Chung; Tang, Jie; Lee, Dongwon

doi:10.1080/07421222.2021.1990612

Cited by 19 publications

(10 citation statements)

References 57 publications

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“…Increasingly, not only human actors but also bots (Salge et al., 2022) are spreading fake news (Hajli et al., 2022; Ross et al., 2019), thereby creating additional challenges in fake news mitigation interventions. In this regard, several mitigation interventions have been examined, including measures such as fact‐checking, fake news reporting, automatic fake news detection, flagging and correcting false news, forwarding restrictions, identity verification, verification badges, and different kinds of ratings, such as article or source ratings by users and experts (Gimpel et al., 2021; King et al., 2021; Moravec et al., 2019; Ng et al., 2021; Schuetz et al., 2021; Wang, Pang, et al., 2021; Zhang et al., 2019).…”

Section: Resultsmentioning

confidence: 99%

The many faces of social media in business and economics research: Taking stock of the literature and looking into the future

Tumasjan

2023

Journal of Economic Surveys

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Since their inception more than 15 years ago, social media have become a vibrant research topic in business and economics research. This article presents an integrative literature review taking stock of and showing the many faces of social media in extant research. Based on N = 1419 articles published in the leading peer‐reviewed business and economics journals in the years 2008–2022, we identify and describe seven overarching research themes, namely, social media as a: (1) market‐oriented interaction hub, (2) resource‐oriented interaction hub, (3) information market, (4) innovation and business venturing hub, (5) societal challenge, (6) political hub, and (7) data source. Finally, we derive a research agenda to stimulate future research on this increasingly important topic.

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Section: Resultsmentioning

confidence: 99%

The many faces of social media in business and economics research: Taking stock of the literature and looking into the future

Tumasjan

2023

Journal of Economic Surveys

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“…Kumar et al, 2018). Fail-ing to stop fake content from spreading on online platforms will reinforce filter bubbles and generate extreme emotions and highly polarized opinions (Allcott & Gentzkow, 2017;Ng et al, 2021;Vosoughi et al, 2018), or being exploited by threat actors to manipulate the business environment…”

Section: Discussionmentioning

confidence: 99%

“…To effectively curb the spread of fake content, intelligent screening systems must be developed that can maximize the accuracy of assessing deceptive content, as failure to do so may lead to devastating consequences that undermine the value of online content (Cui et al., 2018; S. Kumar et al., 2018). Failing to stop fake content from spreading on online platforms will reinforce filter bubbles and generate extreme emotions and highly polarized opinions (Allcott & Gentzkow, 2017; Ng et al., 2021; Vosoughi et al., 2018), or being exploited by threat actors to manipulate the business environment (Lee et al., 2018). However, legitimate content could be wrongly labeled with the presence of false alarms.…”

Section: Discussionmentioning

confidence: 99%

“…Increasingly, online platforms like social media and review websites are applying machine learning techniques to screen user‐generated content for quality control (Lee et al., 2018; X. Zhang et al., 2022). This is in response to surging societal concerns about the spread of fake content that fuel extreme emotions (Allcott & Gentzkow, 2017; Ng et al., 2021; Vosoughi et al., 2018). In 2016, Facebook began to tag articles that were identified as fake by third‐party fact‐checkers 1 .…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al, 2022). This is in response to surging societal concerns about the spread of fake content that fuel extreme emotions (Allcott & Gentzkow, 2017;Ng et al, 2021;Vosoughi et al, 2018). In 2016, Facebook began to tag articles that were identified as fake by third-party factcheckers.…”

Section: Introductionmentioning

confidence: 99%

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Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach

et al. 2023

Production and Operations Management

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Online platforms are experimenting with interventions such as content screening to moderate the effects of fake, biased, and incensing content. Yet, online platforms face an operational challenge in implementing machine learning algorithms for managing online content due to the labeling problem, where labeled data used for model training are limited and costly to obtain. To address this issue, we propose a domain adaptive transfer learning via adversarial training approach to augment fake content detection with collective human intelligence. We first start with a source domain dataset containing deceptive and trustworthy general news constructed from a large collection of labeled news sources based on human judgments and opinions. We then extract discriminating linguistic features commonly found in source domain news using advanced deep learning models. We transfer these features associated with the source domain to augment fake content detection in three target domains: political news, financial news, and online reviews. We show that domain invariant linguistic features learned from a source domain with abundant labeled examples can effectively improve fake content detection in a target domain with very few or highly unbalanced labeled data. We further show that these linguistic features offer the most value when the level of transferability between source and target domains is relatively high. Our study sheds light on the platform operation in managing online content and resources when applying machine learning for fake content detection. We also outline a modular architecture that can be adopted in developing content screening tools in a wide spectrum of fields.

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Misinformation Detection in Social Networks: A Systematic Literature Review

Duzen

Riveni²,

Aktaş³

2022

Computational Science and Its Applications – ICCSA 2022 Workshops

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The Effect of Platform Intervention Policies on Fake News Dissemination and Survival: An Empirical Examination

Cited by 19 publications

References 57 publications

The many faces of social media in business and economics research: Taking stock of the literature and looking into the future

The many faces of social media in business and economics research: Taking stock of the literature and looking into the future

Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach

Misinformation Detection in Social Networks: A Systematic Literature Review

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