The anatomy of Reddit: An overview of academic research <s> From the perspective of posts <s> Access to weblogs, both through commercial services and in academic studies, is usually limited to the content of the weblog posts. This overlooks an important aspect distinguishing weblogs from other web pages: the ability of weblog readers to respond to posts directly, by posting comments. In this paper we present a large-scale study of weblog comments and their relation to the posts. Using a sizable corpus of comments, we estimate the overall volume of comments in the blogosphere; analyze the relation between the weblog popularity and commenting patterns in it; and measure the contribution of comment content to various aspects of weblog access. <s> BIB001 </s> The anatomy of Reddit: An overview of academic research <s> From the perspective of posts <s> On-line news agents provide commenting facilities for readers to express their views with regard to news stories. The number of user supplied comments on a news article may be indicative of its importance or impact. We report on exploratory work that predicts the comment volume of news articles prior to publication using five feature sets. We address the prediction task as a two stage classifi- cation task: a binary classification identifies articles with the poten- tialto receivecomments, and asecondbinaryclassification receives the output from the first step to label articles "low" or "high" com- ment volume. The results show solid performance for the former task, while performance degrades for the latter. Categories and Subject Descriptors <s> BIB002 </s> The anatomy of Reddit: An overview of academic research <s> From the perspective of posts <s> We present a method for accurately predicting the long time popularity of online content from early measurements of user access. Using two content sharing portals, Youtube and Digg, we show that by modeling the accrual of views and votes on content offered by these services we can predict the long-term dynamics of individual submissions from initial data. In the case of Digg, measuring access to given stories during the first two hours allows us to forecast their popularity 30 days ahead with remarkable accuracy, while downloads of Youtube videos need to be followed for 10 days to attain the same performance. The differing time scales of the predictions are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal. We show that predictions are more accurate for submissions for which attention decays quickly, whereas predictions for evergreen content will be prone to larger errors. <s> BIB003 </s> The anatomy of Reddit: An overview of academic research <s> From the perspective of posts <s> We propose a general framework which can be used for modeling and predicting the popularity of online contents. The aim of our modeling is not inferring the precise popularity value of a content, but inferring the likelihood with which the content will be popular. Our approach is rooted in survival analysis which deals with the survival time until an event of a failure or death. Survival analysis assumes that predicting the precise lifetime of an instance is very hard but predicting the likelihood of the lifetime of an instance is possible based on its hazard distribution. Additionally we position ourselves in the standpoint of an external observer who has to model the popularity of contents only with publicly available information. Thus, the goal of our proposed methodology is to model a certain popularity metric, such as the lifetime of a content and the number of comments which a content receives, with a set of explanatory factors, which are observable by the external observer. Among various parametric and non-parametric approaches for the survival analysis, we use the Cox proportional hazard regression model, which divides the distribution function of a certain popularity metric into two components: one which is explained by a set of explanatory factors, called risk factors, and another, a baseline survival distribution function, which integrates all the factors not taken into account. In order to validate our proposed methodology, we use two datasets crawled from two different discussion forums, forum.dpreview.com and forums.myspace.com, which are one of the largest discussion forum dealing various issues on digital cameras and a discussion forum provided by a representative social networks. We model two difference popularity metrics, the lifetime of threads and the number of comments, and we show that the models can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5-6days (24h, respectively) and the number of comments of Dpreview threads by observing a thread during first 2-3days. <s> BIB004 </s> The anatomy of Reddit: An overview of academic research <s> From the perspective of posts <s> News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web. <s> BIB005 </s> The anatomy of Reddit: An overview of academic research <s> From the perspective of posts <s> This paper addresses the question of how language use affects community reaction to comments in online discussion forums, and the relative importance of the message vs. the messenger. A new comment ranking task is proposed based on community annotated karma in Reddit discussions, which controls for topic and timing of comments. Experimental work with discussion threads from six subreddits shows that the importance of different types of language features varies with the community of interest. <s> BIB006 </s> The anatomy of Reddit: An overview of academic research <s> From the perspective of posts <s> Increasingly people form opinions based on information they consume on online social media. As a result, it is crucial to understand what type of content attracts people's attention on social media and drive discussions. In this paper we focus on online discussions. Can we predict which comments and what content gets the highest attention in an online discussion? How does this content differ from community to community? To accomplish this, we undertake a unique study of Reddit involving a large sample comments from 11 popular subreddits with different properties. We introduce a large number of sentiment, relevance, content analysis features including some novel features customized to reddit. Through a comparative analysis of the chosen subreddits, we show that our models are correctly able to retrieve top replies under a post with great precision. In addition, we explain our findings with a detailed analysis of what distinguishes high scoring posts in different communities that differ along the dimensions of the specificity of topic and style, audience and level of moderation. <s> BIB007 </s> The anatomy of Reddit: An overview of academic research <s> From the perspective of posts <s> This paper presents a novel approach for modeling threaded discussions on social media using a graph-structured bidirectional LSTM (long-short term memory) which represents both hierarchical and te... <s> BIB008
Posts are at the heart of the platform structure and dynamics. Once posted, they may gain attention and receive feedback in the form of votes and comments, thereby obtaining a good ranking, and even more attention. They may also go quickly unnoticed in the avalanche of newer posts. Popularity prediction. Anyone who has ever used online social networks is familiar with the concept of "likes" and "dislikes" -the way of expressing attitude towards a piece of content on a binary scale. If "page views" have long been the dominant measure of the success of a content, more and more platforms have moved to voting systems where the number of positive votes is the measure of popularity. Discussion platforms use systems of upvotes and downvotes for different purposes, ranging from the automatic discovery of appreciated items and its delivery them to a wider audience, to the moderation of discussions to protect from spam or malicious content. For these reasons, good models of popularity prediction are of interest for both content creators and platform curators. In general, the problem of popularity prediction has been considered in various online social systems. Early studies, e.g. in YouTube and Digg, found a direct relation between content's initial popularity (in terms of views and upvotes) and its future counts BIB003 , but more sophisticated models have been proposed since then. For instance, Lee et al. BIB004 have modelled the lifetime of discussions on Myspace with the Cox proportional hazard regression model. They selected the number of "risk" factors, fitted from the data for each thread, which were further used as a predictor of threads hitting a threshold number of comments. Mishne and Glance BIB001 analysed the corpus of comments in weblogs, and the relation between weblog popularity and commenting patterns in it. Tsagkias et al. BIB002 analyse the corpus of comments under news stories in regional Internet news agents. The authors propose a model that predicts the commenting popularity prior to article publication in two cases: first if there is a potential to receive comments, and second if the article receives "low" or "high" comment volume. Bandari et al. BIB005 also investigated if popularity of news articles can be estimated even before their posting online. The Reddit dataset shows a proportional relation between the score of a post and the size of its discussion tree on average, as shown in Figure 3 . This may shed some light on general aspects of the posts' popularity, however, in each particular case, it is more important to make a more tailored estimation of submissions' score. A number of works has been dedicated to the prediction of scores on Reddit. Horne et al. BIB007 found a number of textual and temporal features of high score comments by considering the discussion threads from 11 popular subreddits appearing in a 6 month period of 2013. The authors proposed a machine learning model for predicting comments' score and pointed the differences in users' preferences in subreddits. In particular, they claimed that timing of the comment, its relevancy and novelty have positive impact, but stale memes or high user ranking (overall number of positive comments in a user's history) does not affect or even pushes down the average comment score. It was observed that moderation does not always impact proper behavior in the community and may shorten the life of a discussion thread. Recurrent neural networks (RNN) have also been employed to measure community endorsement. Fang et al.  constructed an RNN trained to predict comment scores. Instead of controlling for the submission context, the model learns latent modes of submission context and examines how the context relates to different levels of community endorsement. On a dataset of three popular subreddits, they achieve a good performance, on average, and show that high score comments are usually harder to predict than lower ones. High scoring comments tend to be submitted early in the discussion, and the number of direct replies is not smaller than the height of its hanging discussion subtree. Low and medium score comments have a number of direct replies less than the height of a discussion subtree, indicating the presence of a further discussion. Low score comments tend to come later in the discussion overall, but also later in terms of the group of responses to a parent comment. While structural features have been shown to be good predictors, researchers have started working on extracting textual linguistic features in order to gain predictive power. In this direction, Jaech et al. BIB006 have reported general improvement of machine learning classifiers in the problem of ranking comments that appear in a fixed time window in a discussion thread. Note, however, that the gain was reported to be marginal. Later, Zayats and Ostendorf BIB008 constructed a specific type of RNN called LSTM (long short term memory) for the same purpose of predicting comment scores. The proposed model uses structural and temporal comment features, as well as textual linguistic features of the comments. The authors achieved a slightly better performance (increase in average F1 score from 50 to 54 on average) on a dataset of three subreddits studied earlier in  . They found that controversial comments (that further generate a wide discussion in terms of a discussion tree) tend to be overpredicted (with lower score than predicted) and jokes and funny comments, on the contrary, were mostly underpredicted (with a higher score than predicted). Linguistic context was found to be helpful in prediction tasks, and words of underpredicted comments were aligned with comments of positive score, but words associated with overpredicted comments did not show any significant correlation. In another work, Predict popularity of resubmitted content Reddit, unique dataset of resubmitted images