Over the past few years, a number of new "fringe" communities, like 4chan or certain subreddits, have gained traction on the Web at a rapid pace. However, more often than not, little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web's information ecosystem. In August 2016, a new social network called Gab was created as an alternative to Twitter. It positions itself as putting "people and free speech first", welcoming users banned or suspended from other social networks. In this paper, we provide, to the best of our knowledge, the first characterization of Gab. We collect and analyze 22M posts produced by 336K users between August 2016 and January 2018, finding that Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls. We also measure the prevalence of hate speech on the platform, finding it to be much higher than Twitter, but lower than 4chan's Politically Incorrect board.
With the same multi-platform point of view, @cite_10 propose an approach, called Bag of Communities, which aims to identify abusive content within a community. Using training data from nine communities within 4chan, Reddit, Voat, and Metafilter, they outperform approaches that focus only on in-community data.
@cite_10
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Since its earliest days, harassment and abuse have plagued the Internet. Recent research has focused on in-domain methods to detect abusive content and faces several challenges, most notably the need to obtain large training corpora. In this paper, we introduce a novel computational approach to address this problem called Bag of Communities (BoC)---a technique that leverages large-scale, preexisting data from other Internet communities. We then apply BoC toward identifying abusive behavior within a major Internet community. Specifically, we compute a post's similarity to 9 other communities from 4chan, Reddit, Voat and MetaFilter. We show that a BoC model can be used on communities "off the shelf" with roughly 75 accuracy---no training examples are needed from the target community. A dynamic BoC model achieves 91.18 accuracy after seeing 100,000 human-moderated posts, and uniformly outperforms in-domain methods. Using this conceptual and empirical work, we argue that the BoC approach may allow communities to deal with a range of common problems, like abusive behavior, faster and with fewer engineering resources. As the number and the diversity of news outlets on the Web grows, so does the opportunity for "alternative" sources of information to emerge. Using large social networks like Twitter and Facebook, misleading, false, or agenda-driven information can quickly and seamlessly spread online, deceiving people or influencing their opinions. Also, the increased engagement of tightly knit communities, such as Reddit and 4chan, further compounds the problem, as their users initiate and propagate alternative information, not only within their own communities, but also to different ones as well as various social media. In fact, these platforms have become an important piece of the modern information ecosystem, which, thus far, has not been studied as a whole. In this paper, we begin to fill this gap by studying mainstream and alternative news shared on Twitter, Reddit, and 4chan. By analyzing millions of posts around several axes, we measure how mainstream and alternative news flows between these platforms. Our results indicate that alt-right communities within 4chan and Reddit can have a surprising level of influence on Twitter, providing evidence that "fringe" communities often succeed in spreading alternative news to mainstream social networks and the greater Web. Platforms like Reddit have attracted large and vibrant communities, but the individuals in those communities are free to migrate to other platforms at any time. History has borne this out with the mass migration from Slashdot to Digg. The underlying motivations of individuals who migrate between platforms, and the conditions that favor migration online are not well-understood. We examine Reddit during a period of community unrest affecting millions of users in the summer of 2015, and analyze large-scale changes in user behavior and migration patterns to Reddit-like alternative platforms. Using self-reported statements from user comments, surveys, and a computational analysis of the activity of users with accounts on multiple platforms, we identify the primary motivations driving user migration. While a notable number of Reddit users left for other platforms, we found that an important pull factor that enabled Reddit to retain users was its long tail of niche content. Other platforms may reach critical mass to support popular or “mainstream” topics, but Reddit’s large userbase provides a key advantage in supporting niche topics. When large social-media platforms allow users to easily form and self-organize into interest groups, highly related communities can arise. For example, the Reddit site hosts not just a group called food, but also HealthyFood, foodhacks, foodporn, and cooking, among others. Are these highly related communities created for similar classes of reasons (e.g., to focus on a subtopic, to create a place for allegedly more "high-minded" discourse, etc.)? How do users allocate attention between such close alternatives when they are available or emerge over time? Are there different types of relations between close alternatives such as sharing many users vs. a new community drawing away members of an older one vs. a splinter group failing to cohere into a viable separate community? We investigate the interactions between highly related communities using data from reddit.com consisting of 975M posts and comments spanning an 8-year period. We identify a set of typical affixes that users adopt to create highly related communities and build a taxonomy of affixes. One interesting finding regarding users' behavior is: after a newer community is created, for several types of highly-related community pairs, users that engage in a newer community tend to be more active in their original community than users that do not explore, even when controlling for previous level of engagement.