The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> This article presents the results of a market experiment in which the perceived popularity of programs on a large commercial online system were manipulated. The download counts of software, which indicate how many previous users had obtained a software program, were artificially increased by repeated downloading. This manipulation was done to one member of a matched program pair, with the manipulation levels set randomly to achieve a balanced experimental design over a set of matched pairs. Subsequent downloads of the programs made by the service's subscribers were then recorded. Substantial additional downloads were induced by the manipulation. Some consumers apparently are drawn to market leaders, further increasing their lead. This raises an important issue regarding the need for verifiability of online popularity measures. <s> BIB001 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> I propose the index $h$, defined as the number of papers with citation number higher or equal to $h$, as a useful index to characterize the scientific output of a researcher. <s> BIB002 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> Individuals influence each others' decisions about cultural products such as songs, books, and movies; but to what extent can the perception of success become a “self-fulfilling prophecy”? We have explored this question experimentally by artificially inverting the true popularity of songs in an online “music market,” in which 12,207 participants listened to and downloaded songs by unknown bands. We found that most songs experienced self-ful- filling prophecies, in which perceived—but initially false—popularity became real over time. We also found, however, that the inversion was not self-fulfilling for the market as a whole, in part because the very best songs recovered their popularity in the long run. Moreover, the distortion of market information reduced the correlation between appeal and popularity, and led to fewer overall downloads. These results, although partial and speculative, suggest a new approach to the study of cultural markets, and indicate the potential of web-based experiments to explore ... <s> BIB003 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> We analyze the social network emerging from the user comment activity on the website Slashdot. The network presents common features of traditional social networks such as a giant component, small average path length and high clustering, but differs from them showing moderate reciprocity and neutral assortativity by degree. Using Kolmogorov-Smirnov statistical tests, we show that the degree distributions are better explained by log-normal instead of power-law distributions. We also study the structure of discussion threads using an intuitive radial tree representation. Threads show strong heterogeneity and self-similarity throughout the different nesting levels of a conversation. We use these results to propose a simple measure to evaluate the degree of controversy provoked by a post. <s> BIB004 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> Our society is increasingly relying on the digitized, aggregated opinions of others to make decisions. We therefore designed and analyzed a large-scale randomized experiment on a social news aggregation Web site to investigate whether knowledge of such aggregates distorts decision-making. Prior ratings created significant bias in individual rating behavior, and positive and negative social influences created asymmetric herding effects. Whereas negative social influence inspired users to correct manipulated ratings, positive social influence increased the likelihood of positive ratings by 32% and created accumulating positive herding that increased final ratings by 25% on average. This positive herding was topic-dependent and affected by whether individuals were viewing the opinions of friends or enemies. A mixture of changing opinion and greater turnout under both manipulations together with a natural tendency to up-vote on the site combined to create the herding effects. Such findings will help interpret collective judgment accurately and avoid social influence bias in collective intelligence in the future. <s> BIB005 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> We introduce and validate a learning model of human behavior change in response to feedback on social media. People who participate in these types of websites, like Wikipedia, Reddit, and others, are learning agents whose choices about how to allocate their effort are dynamic and responsive to how they feel their efforts were received in the past. By explicitly taking into account the reinforcement effects of different types of feedback received on prior contributions, our model is able to significantly outperform all known baselines in predicting future contributions both on synthetic data and on real data collected from the social news site reddit.com. Our model has an intuitive interpretation as users playing mixed strategies in a game-like setting with thousands of other users and thousands of available pure strategies. In this interpretation, our task is then inverse reinforcement learning: recovering users' reward functions based on observed behavior. <s> BIB006 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> The activity of passive content consumers on social media sites is typically difficult to measure. This paper explores the activity of a subset of such consumers by looking at the influence on Wikipedia pageviews of one large Reddit community which frequently links to Wikipedia articles. The subreddit used in this analysis, /r/todayilearned (TIL), features a large number of posts on an eclectic set of topics, but excludes current events, which helps rule out the primary threat to being able to make causal statements. Wikipedia's public hourly pageview data provides a unique opportunity to study the influence of a Reddit post on a Wikipedia page at different time horizons. We here present analyses using posts from 2012 in TIL, showing that the week in which a post references a specific Wikipedia article is associated with a substantial increase in pageviews relative to prior and successive weeks. We then apply functional PCA to the dataset in order to characterize pageview dynamics. We also provide a qualitative analysis of the subset of Wikipedia topics posted to Reddit. <s> BIB007 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> 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. <s> BIB008 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> A community's identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities. To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community's identity: how distinctive, and how temporally dynamic it is. By mapping almost 300 Reddit communities into the landscape induced by this typology, we reveal regularities in how patterns of user engagement vary with the characteristics of a community. ::: Our results suggest that the way new and existing users engage with a community depends strongly and systematically on the nature of the collective identity it fosters, in ways that are highly consequential to community maintainers. For example, communities with distinctive and highly dynamic identities are more likely to retain their users. However, such niche communities also exhibit much larger acculturation gaps between existing users and newcomers, which potentially hinder the integration of the latter. ::: More generally, our methodology reveals differences in how various social phenomena manifest across communities, and shows that structuring the multi-community landscape can lead to a better understanding of the systematic nature of this diversity. <s> BIB009 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> Existing studies of how information diffuses across social networks have thus far concentrated on analysing and recovering the spread of deterministic innovations such as URLs, hashtags, and group membership. However investigating how mentions of real-world entities appear and spread has yet to be explored, largely due to the computationally intractable nature of performing large-scale entity extraction. In this paper we present, to the best of our knowledge, one of the first pieces of work to closely examine the diffusion of named entities on social media, using Reddit as our case study platform. We first investigate how named entities can be accurately recognised and extracted from discussion posts. We then use these extracted entities to study the patterns of entity cascades and how the probability of a user adopting an entity (i.e. mentioning it) is associated with exposures to the entity. We put these pieces together by presenting a parallelised diffusion model that can forecast the probability of entity adoption, finding that the influence of adoption between users can be characterised by their prior interactions -- as opposed to whether the users propagated entity-adoptions beforehand. Our findings have important implications for researchers studying influence and language, and for community analysts who wish to understand entity-level influence dynamics. <s> BIB010 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> Today, users are reading the news through social platforms. These platforms are built to facilitate crowd engagement, but not necessarily disseminate useful news to inform the masses. Hence, the news that is highly engaged with may not be the news that best informs. While predicting news popularity has been well studied, it has not been studied in the context of crowd manipulations. In this paper, we provide some preliminary results to a longer term project on crowd and platform manipulations of news and news popularity. In particular, we choose to study known features for predicting news popularity and how those features may change on this http URL, a social platform used commonly for news aggregation. Along with this, we explore ways in which users can alter the perception of news through changing the title of an article. We find that news on reddit is predictable using previously studied sentiment and content features and that posts with titles changed by reddit users tend to be more popular than posts with the original article title. <s> BIB011 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between communities and how they impact users. Here we study intercommunity interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community. We show that such conflicts tend to be initiated by a handful of communities---less than 1% of communities start 74% of conflicts. While conflicts tend to be initiated by highly active community members, they are carried out by significantly less active members. We find that conflicts are marked by formation of echo chambers, where users primarily talk to other users from their own community. In the long-term, conflicts have adverse effects and reduce the overall activity of users in the targeted communities. Our analysis of user interactions also suggests strategies for mitigating the negative impact of conflicts---such as increasing direct engagement between attackers and defenders. Further, we accurately predict whether a conflict will occur by creating a novel LSTM model that combines graph embeddings, user, community, and text features. This model can be used toreate early-warning systems for community moderators to prevent conflicts. Altogether, this work presents a data-driven view of community interactions and conflict, and paves the way towards healthier online communities. <s> BIB012 </s> The anatomy of Reddit: An overview of academic research <s> Self-collected Reddit dataset <s> The process by which new communities emerge is a central research issue in the social sciences. While a growing body of research analyzes the formation of a single community by examining social networks between individuals, we introduce a novel community-centered perspective. We highlight the fact that the context in which a new community emerges contains numerous existing communities. We reveal the emerging process of communities by tracing their early members' previous community memberships. Our testbed is Reddit, a website that consists of tens of thousands of user-created communities. We analyze a dataset that spans over a decade and includes the posting history of users on Reddit from its inception to April 2017. We first propose a computational framework for building genealogy graphs between communities. We present the first large-scale characterization of such genealogy graphs. Surprisingly, basic graph properties, such as the number of parents and max parent weight, converge quickly despite the fact that the number of communities increases rapidly over time. Furthermore, we investigate the connection between a community's origin and its future growth. Our results show that strong parent connections are associated with future community growth, confirming the importance of existing community structures in which a new community emerges. Finally, we turn to the individual level and examine the characteristics of early members. We find that a diverse portfolio across existing communities is the most important predictor for becoming an early member in a new community. <s> BIB013
Machine learning, reinforcement learning, Hierarchical Dirichlet Process Kumar et al. BIB012 Mobilization and attacks between communities Reddit, dataset  Reply networks, lexical analysis, LSTM, Mechanical Turk Tan BIB013 Genealogy of subreddits Reddit, dataset  Relational networks sharing name affixes, for example affix "ask" (science, askscience), "true" (atheism, trueatheism), "help" (tech, techhelp), etc. A curious finding is that when such a highly-related community is created, users engaging in the newer community tend to be more active than in their original one. However, in a prevailing number of cases, newer related subreddits do not detach out of their old partners (in terms of the user base), but in about 25% of the cases, the newer subreddit overtakes its counterpart in participation rate. Some reasons for this behaviour may include the absence of moderation in the new community, a more general scope, or simply a more appealing name spelling. The authors note an interesting result that users who explore the newer created communities generally become relatively more active in their home communities instead of being distracted. Users may also migrate under external pressure. In 2015, a series of external events triggered closure of several popular subreddits. Newell et al. BIB008 studied the history of this unrest period and observed that users migrating to new subreddits increase their level of participation with respect to their previous community. The authors followed users when they migrated to other discussion platforms and observed that although alternative platforms deliver a space for a broad audience, Reddit users value its advantage of hosting niche communities. In a similar vein, Zhang et al. BIB009 created a scalable framework for typing subreddits along the "niche" and "volatile" dimensions and used these types to understand the user retention and assimilation in subreddits. Finally, Muchnik et al. BIB005 performed a large-scale experiment on a Reddit-like platform to study the herding effect of social influence and how the system reacts to the manipulation of comment scores. They observed that users tend to correct artificially down-voted comments. However, comments that are artificially up-voted received an enhanced number of positive votes, thereby increasing the initial bias. Similar herding effects were found in other social systems as well BIB001 BIB003 . Das and Lavoie BIB006 also used a self-collected Reddit dataset of users posts and comments to train a reinforcement-learning model for how users select subreddits to post in reaction to community feedback. Inherent networks of communities. Kumar et al. BIB012 considered interactions between communities in the form of mobilization by users of a community (the source of the 'attack') for hateful comments on posts from another community (the target of the attack). Such mobilization happens when a user in source community posts a link to a post in a target community and titles it with the intention of mobilizing a subset of users, who further write hateful comments on the target post. Such interactions may cause users of a target community to leave. By analyzing reply networks in target discussions, the authors found the effect of echo chambers, i.e. attackers preferentially interact with other attackers and defenders with other defenders. When a direct interaction happen, the attackers "gang-up" on defenders and only a small part of the defenders is involved into interactions with the attackers. The authors propose an LSTM neural network model that uses textual and social features in order to identify whether a given cross-linked post will produce a mobilization. Gomez et al. BIB004 constructed and analysed the inherent social network of Slashdot, generated by replies in the discussion threads. The network exhibits neutral mixing by degree, almost identical in and out degree distributions, only moderated reciprocity and an absence of a community structure. The authors conjectured that users are more inclined to be linked to people who express different points of view, and that the network may help to identify users with a high diversity in opinions. The authors also proposed a measure for the controversy of a discussion, based on the h-index BIB002 . Tan BIB013 considered the genealogy of communities in Reddit. The author builds a weighted directed network of communities, where community A is linked to a community B if a substantial fraction of first 100 posting users in B had their posts in A. The weight of a link (A, B) is simply the fraction of posting users. The network shows user migration across the communities and is useful for predicting growth of communities. One finds that the diverse portfolio of memberships is the most important characteristic of early adopters, whereas community feedback and language similarity does not seem to matter. Other. Discussion platforms are a tested for a broad spectrum of possible research questions. In addition to the topics covered above, we give now some other research directions. One phenomenon which frequently happens in online discussions is trolling -provocative, offensive or menacing messaging  . Mojica  collected and studied an annotated dataset of trolling comments in discussions on Reddit using a variety of language features. Derczynski and Rowe BIB010 used Reddit comments to create an annotated corpus of named entities -proper nouns representing a person, place or an organisation. Horne and Adali BIB011 studied how posting news articles on subreddit /r/worldnews influences their popularity and conclude that changing the article titles results in greater popularity comparing to leaving the original one. In a similar way, Moyer et al. BIB007 studied how posts on the subreddit /r/todayilearned influenced the pageviews of Wikipedia.