"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation

This paper presents Out-of-Context Summarizer, a tool that takes arbitrary public news articles out of context by summarizing them to coherently fit either a liberal- or conservative-leaning agenda. The Out-of-Context Summarizer also suggests hashtag keywords to bolster the polarization of the summary, in case one is inclined to take it to Twitter, Parler or other platforms for trolling. Out-of-Context Summarizer achieved 79% precision and 99% recall when summarizing COVID-19 articles, 93% precision and 93% recall when summarizing politically-centered articles, and 87% precision and 88% recall when taking liberally-biased articles out of context. Summarizing valid sources instead of synthesizing fake text, the Out-of-Context Summarizer could fairly pass the "adversarial disclosure" test, but we didn't take this easy route in our paper. Instead, we used the Out-of-Context Summarizer to push the debate of potential misuse of automated text generation beyond the boilerplate text of responsible disclosure of adversarial language models.

TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls During the COVID-19 Pandemic

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.

TrollHunter2020: Real-Time Detection of Trolling Narratives on Twitter During the 2020 US Elections

This paper presents TrollHunter2020, a real-time detection mechanism we used to hunt for trolling narratives on Twitter during the 2020 U.S. elections. Trolling narratives form on Twitter as alternative explanations of polarizing events like the 2020 U.S. elections with the goal to conduct information operations or provoke emotional response. Detecting trolling narratives thus is an imperative step to preserve constructive discourse on Twitter and remove an influx of misinformation. Using existing techniques, this 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 dozens of trending Twitter topics and hashtags corresponding to the candidates' debates, the election night, and the election aftermath. TrollHunter2020 collects trending data and 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.