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VOX-Pol Blog Post.

Despite the visible presence of violent extremist groups, and evidence of their connection with transnational groups such as Islamic State and Al-Qaeda, the number of empirical studies on this topic is limited in Bangladesh. There have been over 50 violent extremist attacks in Bangladesh in recent years (2013-2017) and digital media was used in different ways in most of these attacks. There appear to be no scholarly articles addressing this phenomenon in Bangladesh. To fill this gap, this project sheds light on the subject by analyzing life stories of Bangladeshi violent extremists. The data for this project was collected from content analysis of newspaper reports, Jihadi online magazines, and videos. Besides presenting current trends on how extremists are using digital media, this paper identifies challenges for counter-terrorism efforts in Bangladesh and provides suggestions to mitigate these challenges.

In this article, we propose a general model of data collection from social media, in the context of terrorism research, focusing on recent studies of jihadists. By analyzing Twitter data collection methods in the existing research, we show that the methods used are prone to sampling biases, and that the sampled datasets are not sufficiently filtered or validated to ensure reliability of conclusions derived from them. Alternatively, we propose some best practices for the collection of data in future research on jihadist using social media (as well as other kinds of terrorist groups). Given the similarity of the methodological challenges posed by research on almost all social media platforms, in the context of terrorism studies, the critique and recommendations offered remain relevant despite the recent shift of most jihadists from Twitter to Telegram and other forms of social media.

This paper presents a dataset with over 3.3M threads and 134.5M posts from the Politically Incorrect board (/pol/) of the imageboard forum 4chan, posted over a period of almost 3.5 years (June 2016-November 2019). To the best of our knowledge, this represents the largest publicly available 4chan dataset, providing the community with an archive of posts that have been permanently deleted from 4chan and are otherwise inaccessible. We augment the data with a few set of additional labels, including toxicity scores and the named entities mentioned in each post. We also present a statistical analysis of the dataset, providing an overview of what researchers interested in using it can expect, as well as a simple content analysis, shedding light on the most prominent discussion topics, the most popular entities mentioned, and the level of toxicity in each post. Overall, we are confident that our work will further motivate and assist researchers in studying and understanding 4chan as well as its role on the greater Web. For instance, we hope this dataset may be used for cross-platform studies of social media, as well as being useful for other types of research like natural language processing. Finally, our dataset can assist qualitative work focusing on in-depth case studies of specific narratives, events, or social theories.

VOX-Pol Blog Post.

YouTube is by far the largest host of user-generated video content worldwide. Alas, the platform also hosts inappropriate, toxic, and/or hateful content. One community that has come into the spotlight for sharing and publishing hateful content are the so-called Involuntary Celibates (Incels), a loosely defined movement ostensibly focusing on men’s issues, who have often been linked to misogynistic views. In this paper, we set out to analyze the Incel community on YouTube. We collect videos shared on Incel-related communities within Reddit, and perform a data-driven characterization of the content posted on YouTube along several axes. Among other things, we find that the Incel community on YouTube is growing rapidly, that they post a substantial number of negative comments, and that they discuss a broad range of topics ranging from ideology, e.g., around the Men Going Their Own Way movement, to discussions filled with racism and/or misogyny. Finally, we quantify the probability that a user will encounter an Incel-related video by virtue of YouTube’s recommendation algorithm. Within five hops when starting from a non-Incel-related video, this probability is 1 in 5, which is alarmingly high given the toxicity of said content.