Twitter, prompted by the rapid spread of alternative narratives, started actively warning users about the spread of COVID-19 misinformation. This form of soft moderation comes in two forms: as an interstitial cover before the Tweet is displayed to the user or as a contextual tag displayed below the Tweet. We conducted a 319-participants study with both verified and misleading Tweets covered or tagged with the COVID-19 misinformation warnings to investigate how Twitter users perceive the accuracy of COVID-19 vaccine content on Twitter. The results suggest that the interstitial covers work, but not the contextual tags, in reducing the perceived accuracy of COVID-19 misinformation. Soft moderation is known to create so-called ”belief echoes” where the warnings echo back, instead of dispelling, preexisting beliefs about morally-charged topics. We found that such “belief echoes” do exist among Twitter users in relationship to the perceived safety and efficacy of the COVID-19 vaccine as well as the vaccination hesitancy for themselves and their children. These “belief echoes” manifested as skepticism of adequate COVID-19 immunization particularly among Republicans and Independents as well as female Twitter users. Surprisingly, we found that the belief echoes are strong enough to preclude adult Twitter users to receive the COVID-19 vaccine regardless of their education level.

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This paper presents TrollHunter2020, a real-time detection mechanism we used to hunt for trolling narratives on Twitter during the 2020 U.S. elections. Trolling narratives form on Twitter as alternative explanations of polarizing events like the 2020 U.S. elections with the goal to conduct information operations or provoke emotional response. Detecting trolling narratives thus is an imperative step to preserve constructive discourse on Twitter and remove an influx of misinformation. Using existing techniques, this takes time and a wealth of data, which, in a rapidly changing election cycle with high stakes, might not be available. To overcome this limitation, we developed TrollHunter2020 to hunt for trolls in real-time with several dozens of trending Twitter topics and hashtags corresponding to the candidates' debates, the election night, and the election aftermath. TrollHunter2020 collects trending data and utilizes a correspondence analysis to detect meaningful relationships between the top nouns and verbs used in constructing trolling narratives while they emerge on Twitter. Our results suggest that the TrollHunter2020 indeed captures the emerging trolling narratives in a very early stage of an unfolding polarizing event. We discuss the utility of TrollHunter2020 for early detection of information operations or trolling and the implications of its use in supporting a constrictive discourse on the platform around polarizing topics.

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