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In recent years, the emergence of fake news outlets has drawn out the importance of news literacy. This is particularly critical in social media where the flood of information makes it difficult for people to assess the veracity of the false stories from such deceitful sources. Therefore, people oftentimes fail to look skeptically at these stories. We explore a way to circumvent this problem by nudging users into making conscious assessments of what online contents are credible. For this purpose, we developed FeedReflect, a browser extension. The extension nudges users to pay more attention and uses reflective questions to engage in news credibility assessment on Twitter. We recruited a small number of university students to use this tool on Twitter. Both qualitative and quantitative analysis of the study suggests the extension helped people accurately assess the credibility of news. This implies FeedReflect can be used for the broader audience to improve online news literacy. 

Online communities play a crucial role in disseminating conspiracy theories. New theories often emerge in the aftermath of catastrophic events. Despite evidence of their widespread appeal, surprisingly little is known about who participates in these event-specific conspiratorial discussions or how do these discussions evolve over time. We study r/conspiracy, an active Reddit community of more than 200,000 users dedicated to conspiratorial discussions. By focusing on four tragic events and 10 years of discussions, we find three distinct user cohorts: joiners, who never participated in Reddit but joined r/conspiracy only after the event; converts who were active Reddit users but joined r/conspiracy only after the event; and veterans, who are longstanding r/conspiracy members. While joiners and converts have a shorter lifespan in the community in comparison to the veterans, joiners are more active during their shorter tenure, becoming increasingly engaged over time. Finally, to investigate how these events affect users’ conspiratorial discussions, we adopted a causal inference approach to analyze user comments around the time of the events. We find that discussions happening after the event exhibit signs of emotional shock, increased language complexity, and simultaneous expressions of certainty and doubtfulness. Our work provides insight on how online communities may detect new conspiracy theories that emerge ensuing dramatic events, and in the process stop them before they spread.