Appealing to Sense and Sensibility: System 1 and System 2 Interventions for Fake News on Social Media

Abstract

Disinformation on social media—commonly called “fake news”—has become a major concern around the world, and many fact-checking initiatives have been launched in response. However, if the presentation format of fact-checked results is not persuasive, fact-checking may not be effective. For instance, Facebook tested the idea of flagging dubious articles in 2017 but concluded that it was ineffective and removed the feature. We conducted three experiments with social media users to investigate two different approaches to implementing a fake news flag—one designed to be most effective when processed by automatic cognition (System 1) and the other designed to be most effective when processed by deliberate cognition (System 2). Both interventions were effective, and an intervention that combined both approaches was about twice as effective. The awareness training on the meaning of the flags increased the effectiveness of the System 2 intervention but not the System 1 intervention. Believability influenced the extent to which users would engage with the article (e.g., read, like, comment, and share). Our results suggest that both theoretical routes can be used—separately or together—in the presentation of fact-checking results in order to reduce the influence of fake news on social media users.

Misinformation warnings: Twitter’s soft moderation effects on COVID-19 vaccine belief echoes

“…(2019) . The design of warnings and how they are presented to users can impact the warnings effectiveness, with more explicit ones being more effective Moravec et al. (2020) .…”

Understanding patterns of COVID infodemic: A systematic and pragmatic approach to curb fake news

“…Prior research on fake news is mostly from political contexts or utilizes a plethora of empirical or modeling approaches that investigate issues such as impact on financial indicators, efficacy of warning statement attached to fake news title, and mathematical modeling of fake news propagation. Some examples are in recent studies by Clarke et al (2020), Moravec et al, 2020 , Papanastasiou, 2020 , Pennycook et al (2020a). Limited research has attempted to understand the topic and trend of COVID-19 fake news using topic modeling and sentiment analysis such as Sharma et al (2020) and Xue et al (2020).…”

Social Media and Fake News Detection using Adversarial Collaboration

“…Fake news is counterfeit news, while genuine news is comprised of news that has gone through fact checkers and editors [6]. Another definition of fake news refers to news articles that contain verifiably false information intentionally created to mislead others [7]. In our research, we will use this definition of fake news.…”