  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 adversarial machine learning mechanism
called TrollHunter-Evader. TrollHunter-Evader employs a Test Time Evasion (TTE)
approach in a combination with a Markov chain-based mechanism to recycle
originally trolling tweets. The recycled tweets were able to achieve a
remarkable 40% decrease in the TrollHunter's ability to correctly identify
trolling tweets. Because the COVID-19 infodemic could have a harmful impact on
the COVID-19 pandemic, we provide an elaborate discussion about the
implications of employing adversarial machine learning to evade Twitter troll
hunts.
