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.
@cite_7: Creating, placing, and presenting social media content is a difficult problem. In addition to the quality of the content itself, several factors such as the way the content is presented (the title), the community it is posted to, whether it has been seen before, and the time it is posted determine its success. There are also interesting between these factors. For example, the language of the title should be targeted to the community where the content is submitted, yet it should also highlight the distinctive nature of the content. In this paper, we examine how these factors interact to determine the popularity of social media content. We do so by studying resubmissions, i.e., content that has been submitted multiple times, with multiple titles, to multiple different communities. Such data allows us to tease apart' the extent to which each factor influences the success of that content. The models we develop help us understand how to better target social media content: by using the right title, for the right community, at the right time.
@cite_4: Requests are at the core of many social media systems such as question & answer sites and online philanthropy communities. While the success of such requests is critical to the success of the community, the factors that lead community members to satisfy a request are largely unknown. Success of a request depends on factors like who is asking, how they are asking, when are they asking, and most critically what is being requested, ranging from small favors to substantial monetary donations. We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors. Drawing from social psychology literature, we extract high-level social features from text that operationalize social relations between recipient and donor and demonstrate that these extracted relations are predictive of success. More speciﬁcally, we ﬁnd that clearly communicating need through the narrative is essential and that linguistic indications of gratitude, evidentiality, and generalized reciprocity, as well as high status of the asker further increase the likelihood of success. Building on this understanding, we develop a model that can predict the success of unseen requests, signiﬁcantly improving over several baselines. We link these ﬁndings to research in psychology on helping behavior, providing a basis for further analysis of success in social media systems.
@cite_6: Domestic abuse affects people of every race, class, age, and nation. There is significant research on the prevalence and effects of domestic abuse; however, such research typically involves population-based surveys that have high financial costs. This work provides a qualitative analysis of domestic abuse using data collected from the social and news-aggregation website reddit.com. We develop classifiers to detect submissions discussing domestic abuse, achieving accuracies of up to 92 , a substantial error reduction over its baseline. Analysis of the top features used in detecting abuse discourse provides insight into the dynamics of abusive relationships.
@cite_12: Many online communities ask their members to do work for the good of everyone on the site. On social voting sites like Reddit, this means that users judge a stream of incoming links by voting them up or down. The links with the most up-votes bubble up to the main page, pointing everyone toward the best content. A threat to all sites designed this way, however, is underprovision: when too many people rely on others to contribute without doing so themselves. In this paper, we present findings suggesting that widespread underprovision of votes is happening on Reddit, arguably the internet's largest social voting community. Notably, Reddit overlooked 52 of the most popular links the first time they were submitted. This suggests that many potentially popular links get ignored, jeopardizing the site's core purpose. We conclude by discussing possible reasons behind it, and suggest future research on social voting sites.
Despite exhibiting some undesirable upvoting patterns @cite_12 , Reddit itself has been used as a data source in various contexts. For instance, the study of altruistic requests @cite_4 , the study of domestic abuse discourse @cite_6 , and work about titles @cite_7 demonstrate that useful information can be learned from Reddit comments and upvotes.