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 and in the aftermath of the 2020 U.S. elections. Trolling narratives form on Twitter as alternative explanations of polarizing events with the goal of conducting information operations or provoking emotional responses. Detecting trolling narratives thus is an imperative step to preserve constructive discourse on Twitter and remove the influx of misinformation. Using existing techniques, the detection of such content 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 dozen trending Twitter topics and hashtags corresponding to the candidates' debates, the election night, and the election aftermath. TrollHunter2020 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.