TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls During the COVID-19 Pandemic
Peter Jachim, Filipo Sharevski, Paige Treebridge

TL;DR
This paper introduces TrollHunter, an automated system for detecting Twitter trolls during COVID-19 with high accuracy, and explores how trolls can evade detection using adversarial machine learning techniques.
Contribution
The paper presents a novel linguistic analysis-based detection method and an adversarial evasion mechanism, advancing the understanding of troll detection and evasion strategies during pandemics.
Findings
TrollHunter achieved 98.5% accuracy in detecting trolls.
TrollHunter-Evader reduced detection accuracy by 40%.
The study discusses implications of adversarial evasion in misinformation control.
Abstract
This paper presents TrollHunter, an automated reasoning mechanism we used to hunt for trolls on Twitter during the COVID-19 pandemic in 2020. Trolls, poised to disrupt the online discourse and spread disinformation, quickly seized the absence of a credible response to COVID-19 and created a COVID-19 infodemic by promulgating dubious content on Twitter. To counter the COVID-19 infodemic, the TrollHunter leverages a unique linguistic analysis of a multi-dimensional set of Twitter content features to detect whether or not a tweet was meant to troll. TrollHunter achieved 98.5% accuracy, 75.4% precision and 69.8% recall over a dataset of 1.3 million tweets. Without a final resolution of the pandemic in sight, it is unlikely that the trolls will go away, although they might be forced to evade automated hunting. To explore the plausibility of this strategy, we developed and tested an…
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