The anatomy of Reddit: An overview of academic research <s> Review <s> We perform a statistical analysis of user's reaction time to a new discussion thread in online debates on the popular news site Slashdot. First, we show with Kolmogorov-Smirnov tests that a mixture of two log-normal distributions combined with the circadian rhythm of the community is able to explain with surprising accuracy the reaction time of comments within a discussion thread. Second, this characterization allows to predict intermediate and long-term user behavior with acceptable precision. The prediction method is based on activity-prototypes, which consist of a mixture of two log-normal distributions, and represent the average activity in a particular region of the circadian cycle. <s> BIB001 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> We analyze the structure and evolution of discussion cascades in four popular websites: Slashdot, Barrapunto, Meneame and Wikipedia. Despite the big heterogeneities between these sites, a preferential attachment (PA) model with bias to the root can capture the temporal evolution of the observed trees and many of their statistical properties, namely, probability distributions of the branching factors (degrees), subtree sizes and certain correlations. The parameters of the model are learned efficiently using a novel maximum likelihood estimation scheme for PA and provide a figurative interpretation about the communication habits and the resulting discussion cascades on the four different websites. <s> BIB002 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> Online discussion threads are conversational cascades in the form of posted messages that can be generally found in social systems that comprise many-to-many interaction such as blogs, news aggregators or bulletin board systems. We propose a framework based on generative models of growing trees to analyse the structure and evolution of discussion threads. We consider the growth of a discussion to be determined by an interplay between popularity, novelty and a trend (or bias) to reply to the thread originator. The relevance of these features is estimated using a full likelihood approach and allows to characterise the habits and communication patterns of a given platform and/or community. We apply the proposed framework on four popular websites: Slashdot, Barrapunto (a Spanish version of Slashdot), Meneame (a Spanish Digg-clone) and the article discussion pages of the English Wikipedia. Our results provide significant insight into understanding how discussion cascades grow and have potential applications in broader contexts such as community management or design of communication platforms. <s> BIB003 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> We present an analysis of user conversations in on-line social media and their evolution over time. We propose a dynamic model that accurately predicts the growth dynamics and structural properties of conversation threads. The model successfully reconciles the differing observations that have been reported in existing studies. By separating artificial factors from user behaviors, we show that there are actually underlying rules in common for on-line conversations in different social media websites. Results of our model are supported by empirical measurements throughout a number of different social media websites. <s> BIB004 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> 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. <s> BIB005 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> 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 \emph{interactions} 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. <s> BIB006 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> In this paper we seek to understand the relationship between the online popularity of an article and its intrinsic quality. Prior experimental work suggests that the relationship between quality and popularity can be very distorted due to factors like social influence bias and inequality in visibility. We conduct a study of popularity on two different social news aggregators, Reddit and Hacker News. We define quality as the number of votes an article would have received if each article was shown, in a bias-free way, to an equal number of users. We propose a simple Poisson regression method to estimate this quality metric from time-series voting data. We validate our methods on data from Reddit and Hacker News, as well the experimental data from prior work. Using these estimates, we find that popularity on Reddit and Hacker News is a relatively strong reflection of intrinsic quality. <s> BIB007 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> The content of today's social media is becoming more and more rich, increasingly mixing text, images, videos, and audio. It is an intriguing research question to model the interplay between these different modes in attracting user attention and engagement. But in order to pursue this study of multimodal content, we must also account for context: timing effects, community preferences, and social factors (e.g., which authors are already popular) also affect the amount of feedback and reaction that social-media posts receive. In this work, we separate out the influence of these non-content factors in several ways. First, we focus on ranking pairs of submissions posted to the same community in quick succession, e.g., within 30 seconds; this framing encourages models to focus on time-agnostic and community-specific content features. Within that setting, we determine the relative performance of author vs. content features. We find that victory usually belongs to "cats and captions," as visual and textual features together tend to outperform identity-based features. Moreover, our experiments show that when considered in isolation, simple unigram text features and deep neural network visual features yield the highest accuracy individually, and that the combination of the two modalities generally leads to the best accuracies overall. <s> BIB008 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> As crowd-sourced curation of news and information become the norm, it is important to understand not only how individuals consume information through social news Web sites, but also how they contribute to their ranking systems. In this paper, we introduce and make available a new data set containing the activity logs that recorded all activity for 309 Reddit users for one year. Using this newly collected data, we present findings that highlight the browsing and voting behavior of the study’s participants. We find that most users do not read the article that they vote on, and that, in total, 73% of posts were rated (i.e., upvoted or downvoted) without first viewing the content. We also show the evidence of cognitive fatigue in the browsing sessions of users that are most likely to vote. <s> BIB009 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> In order to keep up with the demand of curating the deluge of crowd-sourced content, social media platforms leverage user interaction feedback to make decisions about which content to display, highlight, and hide. User interactions such as likes, votes, clicks, and views are assumed to be a proxy of a content's quality, popularity, or news-worthiness. In this paper we ask: how predictable are the interactions of a user on social media? To answer this question we recorded the clicking, browsing, and voting behavior of 186 Reddit users over a year. We present interesting descriptive statistics about their combined 339,270 interactions, and we find that relatively simple models are able to predict users' individual browse- or vote-interactions with reasonable accuracy. <s> BIB010 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> Online discussion in form of written comments is a core component of many social media platforms. It has attracted increasing attention from academia, mainly because theories from social sciences can be explored at an unprecedented scale. This interest has led to the development of statistical models which are able to characterize the dynamics of threaded online conversations. In this paper, we review research on statistical modeling of online discussions, in particular, we describe current generative models of the structure and growth of discussion threads. These are parametrized network formation models that are able to generate synthetic discussion threads that reproduce certain features of the real discussions present in different online platforms. We aim to provide a clear overview of the state of the art and to motivate future work in this relevant research field. <s> BIB011 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> Some structural characteristics of online discussions have been successfully modeled in the recent years. When parameters of these models are properly estimated, the models are able to generate synthetic discussions that are structurally similar to the real discussions. A common aspect of these models is that they consider that all users behave according to the same model. In this paper, we combine a growth model with an Expectation–Maximization algorithm that finds different parameters for different latent groups of users. We use this method to find the different roles that coexist in the community. Moreover, we analyze whether we can predict users behaviors based on their roles. Indeed, we show that predictions are improved for some of the roles when compared with a simple growth model. <s> BIB012 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> Comunicacio presentada a la Eleventh International AAAI Conference on Web and Social Media, celebrada els dies 15 a 18 de maig de 2017 a Montreal, Canada. <s> BIB013 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> Internet boards are platforms for online discussion about a variety of topics. On these boards, individuals may start a new thread on a specific matter or leave comments in an existing discussion. The resulting collective process leads to the formation of ‘discussion trees’, where nodes represent a post and comments, and an edge represents a ‘reply-to’ relation. The structure of discussion trees has been analysed in previous works, but mainly from a static perspective. In this article, we focus on the structural and dynamical properties of discussion trees by modelling their formation as a self-exciting Hawkes process with a branching structure. We first study a Reddit data set of all posts and comments submitted between 2008 and 2014 to show that the structure of the trees resemble those produced by a Galton–Watson process with a special root offspring distribution. The dynamical aspect of the model is then used to predict future commenting activity and the final size of a discussion tree. We compare the efficiency of our approach with previous works and show its superiority for the prediction of discussion dynamics. In particular, we find that the Reddit discussion trees, being fundamentally broader than for example the Twitter cascades and developing with a qualitatively different dynamics, require appropriate models. The tree structure and the dynamics are influenced mainly by the age of the discussion, rather than by the number of acquired comments. The post and the comments are further commented by different processes, which is a key ingredient in modelling of discussions. <s> BIB014 </s> The anatomy of Reddit: An overview of academic research <s> Review <s> Internet memes are increasingly used to sway and manipulate public opinion. This prompts the need to study their propagation, evolution, and influence across the Web. In this paper, we detect and measure the propagation of memes across multiple Web communities, using a processing pipeline based on perceptual hashing and clustering techniques, and a dataset of 160M images from 2.6B posts gathered from Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab, over the course of 13 months. We group the images posted on fringe Web communities (/pol/, Gab, and The_Donald subreddit) into clusters, annotate them using meme metadata obtained from Know Your Meme, and also map images from mainstream communities (Twitter and Reddit) to the clusters. ::: Our analysis provides an assessment of the popularity and diversity of memes in the context of each community, showing, e.g., that racist memes are extremely common in fringe Web communities. We also find a substantial number of politics-related memes on both mainstream and fringe Web communities, supporting media reports that memes might be used to enhance or harm politicians. Finally, we use Hawkes processes to model the interplay between Web communities and quantify their reciprocal influence, finding that /pol/ substantially influences the meme ecosystem with the number of memes it produces, while \td has a higher success rate in pushing them to other communities. <s> BIB015
Medvedev et al. BIB014 Model structure and predict dynamics of discussion trees Reddit, dataset  Stochastic Hawkes processes Hessel et al. BIB008 considered pairs of posts, submitted within a very short time interval into the same communities (to exclude timing bias) and predicted the more popular posts in those pairs. Six primarily image sharing subreddits were selected for the study, with a set of features including textual and temporal information, but also image features assessed by the deep neural networks. The authors concluded that user-centric characteristics, e.g. previous popular submissions, and content-specific features, e.g. more complicated images and simpler titles, make a good predictor of popularity of the submission. The authors also reported an accuracy comparable to the that of human classification. The popularity of a content is influenced by various factors, and public endorsement may not properly reflect its inherent quality  . This phenomenon has been studied by Stoddard BIB007 by means of a Poisson regression model that infers the intrinsic quality of posts from voting activity of the users. The author collected a unique dataset of users' voting time series by tracking a number of top posts on front pages of 5 subreddits and the front page of Hacker News, and used data to predict final score of posts. A variable of quality was then introduced in the model parameters, and shown to correlate with the total post scores, although there were several situations when similar quality posts had different scores and vice versa. Amongst others, the mechanism of making popular more visible than less popu-lar ones leads to a multiplicative process that increases the variance of popularity, with the effect of making a substantial fraction of the posts ignored. According to Gilbert BIB005 , Reddit overlooks 52% of the most popular links the first time they were submitted. Lakkaraju, McAuley and Leskovec BIB006 explored this idea and showed that resubmissions of the same piece of content may gain more popularity than an original submission. The authors collected a dataset of image submissions between 2008 and 2013, where each image was resubmitted roughly 7.9 times 5 . Same pictures can in principle be resubmitted to different subreddits, thus the authors employed a success metric compared to the average post score in the community. The authors also proposed a statistical model that predicts the expected score of a resubmitted picture. The model parameters include the inherent content popularity, penalties from previous success and previous submissions to other communities or to the same community twice. Overall, the study supports the hypothesis that a high quality content 'speaks for itself' and determines its score. The choice of subreddit plays an important role -the model shows that the content, resubmitted to the same subreddit, in general was unlikely to be popular, as well as whether the content was previously highly rated in a popular subreddit (with a high number of visitors or subscribers). This effect gradually disappears with time, indicating forgetfulness of the audience. Resubmission titles were also found indicative: if the title is novel, written using subreddit-specific words and sentiment orientation, the submission has higher chances to receive positive feedback. Similarly, Glenski et al. BIB009 BIB010 found that users mostly vote on posts only after glancing over the title, without proper reading of the content or the discussion. The authors of BIB006 also performed an in situ experiment: they manually chose and resubmitted 85 images from the dataset, select a "good" and a "bad" title according to the model for each picture and post them in two different subreddits. The post scores, gathered after one day, show that submissions with a "good" title generated scores three times higher than the "bad" ones. Generative models for discussion trees. As mentioned above, comments under the post form a rooted tree. Such trees have dynamic nature and their temporal growth reveals the dynamic of attention to the post (see an example of a tree and a histogram of comment arrival on Figure 4 ). Generative models for discussion trees mostly question the tree structure of a discussion while disregarding the comments' or posts' textual features and exact timings. The reader may refer to the extensive review on generative models given by Aragón et al. BIB011 , and here we give only a brief overview of the representative contributions. Gomez et al. BIB002 considered discussion trees in four large Internet boards (Slashdot, Barrapunto, Meneame and Wikipedia) and proposed a generating model based on preferential attachment mechanism (PA model) with respect to the comment degree and the root bias. Later Gomez et al. BIB003 enriched this model by incorporating a notion of novelty of comments, which is represented by an exponentially decaying function of attractiveness. The model showed better results in likelihood of representing the tree structure and reproduced well the width/depth relation for discus- sion trees. Lumbreras et al. BIB012 proposed to enrich the PA model with the notion of roles, which are latent functions of community members. The PA model defines a set of parameters that regulate the place of attachment of a new comment. The authors suggest that this set of parameters is different for different users and propose to group them into the role sets, which are inferred accordingly. Despite of this extra natural assumption, the gain in model likelihood is marginal. Aragón et al. BIB013 considered the social system Meneame, where the change in discussion representation happened in 2015 from a plain list to a structured threaded view. This change was observed to have an impact on the structure of discussion trees, and the authors further enrich the PA model, with a reciprocity term that captures the tendency of posting authors to reply back in the discussion. It was observed that change of the platform interface had a positive effect on reciprocity, as well as on other parameters in general. The above mentioned models focus exclusively on the structure of discussion trees while leaving out the continuous time dynamics of the comment attraction process. Kaltenbrunner et al. BIB001 found that comment arrival time in Slashdot discussions fits well by double lognormal distribution, although the fitting quality depends on a circadian rhythm of the site. Based on this finding, the authors propose the prediction model, that predicts a total number of comments in a discussion thread. The dynamical aspect of tree generation was first studied by Wang et al. BIB004 , where the authors introduce a merely theoretical model for the structural and temporal evolution of discussions. The temporal evolution was described as a Lévy process with power-law interevent time distribution, when newly arriving comments were assumed to attach to the existing tree under the simple PA rule. The model was inspired by empirical observations of such discussion boards, like Reddit, Digg and Epinions, however the mean-field nature of it limits its calibration with real-world datasets. Medvedev et al. BIB014 used a Hawkes process along with its branching tree interpretation to jointly model structure and dynamics of discussion trees in Reddit. The model was further used for prediction of the discussion flow, performing better than contemporary models of cascade dynamics. Other. Zannettou et al. BIB015 perform a study of meme evolution and propagation across different platforms, e.g. Twitter, Reddit, 4chan and Gab, where the last two are image boards structurally similar to Reddit. The authors' analysis helps to reveal the influential actors in meme ecosystem, both in terms of creation and propagation, the authors build clusters of similar memes and make an analysis of reciprocal influence between the observed communities using Hawkes processes.