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• W3129543715 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 quickly seized the opportunity to create 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." @default.
• W3129543715 created "2021-03-01" @default.
• W3129543715 creator A5018677917 @default.
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• W3129543715 date "2020-10-26" @default.
• W3129543715 modified "2023-09-27" @default.
• W3129543715 title "TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls During the COVID-19 Pandemic" @default.
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• W3129543715 doi "https://doi.org/10.1145/3442167.3442169" @default.
• W3129543715 hasPublicationYear "2020" @default.
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