论文标题
TrollHunter [Evader]：在COVID-19大流行期间对Twitter巨魔的自动检测[逃避]
TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls During the COVID-19 Pandemic
论文作者
论文摘要
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.为了对抗COVID-19的Infodemic，TrollHunter利用了多维Twitter内容特征的独特语言分析来检测是否是为了拖钓。 Trollhunter在130万条推文的数据集中获得了98.5％的精度，75.4％的精度和69.8％的召回。如果没有最终的大流行决议，巨魔不太可能消失，尽管他们可能被迫逃避自动狩猎。为了探索这种策略的合理性，我们开发并测试了一种称为Trollhunter-evader的对抗机器学习机制。 Trollhunter-Evader与基于马尔可夫链的机制结合使用测试时间逃避时间（TTE）方法，以回收最初拖钓推文。回收的推文能够使巨大的40％降低巨大的巨型拖钓推文的能力。由于COVID-19的Infodemic可能会对Covid-19的大流行有害影响，因此我们就使用对抗机器学习逃避Twitter Troll Troll Hunts的含义进行了详尽的讨论。
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.
