TrollHunter2020: Real-time Detection of Trolling Narratives on Twitter During the 2020 U.S. Elections

Abstract

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.

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

“…Perhaps outside the scope of this study, the ethical questions remains whether Twitter, or any social media platform, acting as a private entity, could set a precedent of an ultimate arbiter of what constitutes misinformation and what does not. Twitter most likely applies an automated means of warning labeling in conjunction with manual moderation Jachim et al. (2021) , as evidence with the strange labeling of Tweets that contained the words “oxygen” and “frequency” for COVID-19 related Tweets Zannettou (2021) .…”

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“…One could argue that the after-the-fact notification is chosen to counter "habituation", or the diminished response with repetitions of the same warning screens like these, or perhaps break the effect of "generalization" that might occur when habituation to these screens carries over to novel security interventions that look like the warning tags [79]. Seemingly designed to camouflage itself amongst the existing interface features, the warning tags are blue and not red in color, they do not obscure the suspected misinformation tweet, nor do they occur predictably like the warning screens every time an Internet browser cannot verify the visiting website's certificate (the tweets in question have to be fact checked, if not automatically flagged [32]).…”

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