Online Library

Hate groups are increasingly using multiple social media platforms to promote extremist ideologies. Yet we know little about their communication practices across platforms. How do hate groups (or “in-groups”), frame their hateful agenda against the targeted group or the “out-group?” How do they share information? Utilizing “framing” theory from social movement research and analyzing domains in the shared links, we juxtapose the Facebook and Twitter communication of 72 Southern Poverty Law Center (SPLC) designated hate groups spanning five hate ideologies. Our findings show that hate groups use Twitter for educating the audience about problems with the out-group, maintaining positive self-image by emphasizing in-group’s high social status, and for demanding policy changes to negatively affect the out-group. On Facebook, they use fear appeals, call for active participation in group events (membership requests), all while portraying themselves as being oppressed by the out-group and failed by the system. Our study unravels the ecosystem of cross-platform communication by hate groups, suggesting that they use Facebook for group radicalization and recruitment, while Twitter for reaching a diverse follower base.

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations and compare different embedding strategies. We demonstrate the capabilities of the proposed approach on a multimedia collection originating from the violent online extremism forum Stormfront, which is particularly interesting due to the high semantic level of the discussions it features.

The alt-right is a growing radical right-wing network that is particularly effective at mobilizing emotion through digital communications. Introducing ‘white thymos’ as a framework to theorize the role of rage, anger, and indignation in alt-right communications, this study argues that emotive communication connects alt-right users and mobilizes white thymos to the benefit of populist radical right politics. By combining linguistic, computational, and interpretive techniques on data collected from Twitter, this study demonstrates that the alt-right weaponizes white thymos in three ways: visual documentation of white victimization, processes of legitimization of racialized pride, and reinforcement of the rectitude of rage and indignation. The weaponization of white thymos is then shown to be central to the culture of the alt-right and its connectivity with populist radical right politics.

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.

This article examines the forms and feelings of togetherness evident in both Manchester city centre and on social media during the first anniversary of the 22 May 2017 Manchester Arena bombing. To do this, we introduce a conceptual framework that conceives commemorative public atmospheres as composed of a combination of ‘more or less digital’ elements. We also present a methodological approach that combines the computational collection and analysis of Twitter content with short-term team autoethnography. First, the article addresses the concept of public atmospheres before introducing the case study and outlining our methodology. We then analyse the shifting moods of togetherness created by the official programme of commemorative events known as Manchester Together and their digital mediatisation through Twitter. We then explore a grassroots initiative, #LoveMCRBees, and how it relied on the materialisation of social media logics to connect people. Overall, we demonstrate how public atmospheres, as constituted in more and less digital ways, provide a framework for conceptualising commemorative events, and how togetherness is reworked by social media, especially in the context of responses to terrorism.

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.