The anatomy of Reddit: An overview of academic research <s> Discussion <s> Twitter is a social media giant famous for the exchange of short, 140-character messages called "tweets". In the scientific community, the microblogging site is known for openness in sharing its data. It provides a glance into its millions of users and billions of tweets through a "Streaming API" which provides a sample of all tweets matching some parameters preset by the API user. The API service has been used by many researchers, companies, and governmental institutions that want to extract knowledge in accordance with a diverse array of questions pertaining to social media. The essential drawback of the Twitter API is the lack of documentation concerning what and how much data users get. This leads researchers to question whether the sampled data is a valid representation of the overall activity on Twitter. In this work we embark on answering this question by comparing data collected using Twitter's sampled API service with data collected using the full, albeit costly, Firehose stream that includes every single published tweet. We compare both datasets using common statistical metrics as well as metrics that allow us to compare topics, networks, and locations of tweets. The results of our work will help researchers and practitioners understand the implications of using the Streaming API. <s> BIB001 </s> The anatomy of Reddit: An overview of academic research <s> Discussion <s> A growing portion of offline and online human activities leave digital footprints in electronic databases. Resulting big social data offers unprecedented insights into population-wide patterns and detailed characteristics of the individuals. The goal of this paper is to review the literature showing how pervasive records of digital footprints, such as Facebook profile, or mobile device logs, can be used to infer personality, a major psychological framework describing differences in individual behavior. We briefly introduce personality and present a range of works focusing on predicting it from digital footprints and conclude with a discussion of the implications of these results in terms of privacy, data ownership, and opportunities for future research in computational social science. <s> BIB002
This survey does not aim at giving a comprehensive listing of all Reddit-related works, but rather at providing a representative sampling that illustrates the richness of datasets related to online discussion platforms, with Reddit as a dominant example. The richness is clear in terms of quantity as well as quality -in principle the entire dataset can be harvested, while other platforms, e.g. Facebook may offer exhaustivity only at the cost of selecting a small sample of volunteers BIB002 , and studies of Twitter are known to be limited by the volume and bias of their API BIB001 . The richness also arises from the diversity of the data, featuring an inherent social network between users, texts constitutive of posts and comments, social appreciation (score), tree structure of posts and comments, all that unfolding in time for years. The richness of the data translates in a variety of topics of investigation. On the theoretical level, it allows to observe an ecosystem of users discussing, agreeing or not, and organizing in communities. Besides fundamental sociological questions, it also allows to investigate a range of Internet-specific questions, such as trolling, echo chambers, polarization, social manipulation, etc. The data also offers material to shape solutions for applied questions. From a platform designer viewpoint, it could help to improve the experience of a user, but also to design more efficient algorithms for the identification of high-quality posts. The design of the commenting system is also expected to affect the dynamics and structure of conversations. In this direction, important problems include the detection and automatic removal of trolling or attacks, as well as ways to stimulate the activity of a forum. The richness of the data and problems calls for a range of computational methods, which may be explicit statistical models, or black-box machine learning tools, in order to classify or predict the behaviors of users, posts, communities. Overall we observe that the structure of discussion trees is relatively well understood. However, mixing the dynamics and structure with textual features is an important step that has only been studied by means of black box machine learning, such as neural networks techniques, showing a good performance in predicting community appreciation. A challenge remains the fact that many basic statistics of the data (activity of users, popularity of communities, success of a post, etc.) exhibit heavy tails, which may introduce sampling issues as, for instance, a random sampling may fail to observe extreme points (e.g. high activity users) while they carry a large influence in the structure and dynamics of the system. This caveat must be kept in mind when using techniques such as neural networks. This review is a testimony of the richness and dynamism of academic research on social platforms, in general, and Reddit, in particular. Despite the many progresses overviewed above, we would like to conclude with a list of what we believe to be promising research directions. In our opinion, fruitful avenues of research include a more detailed study of the activity patterns of users, of the influence of platform structure on user experience, and of the inherent networked effects behind posts, comments and votes. To be more precise, the dynamics of posting and commenting shape and define the life of a community. The two processes are coupled, but not necessarily proportional, as can be seen in Figure 6 . Important questions include the identification of dynamical and structural features that ensure the growth or resilience of online communities over time. The dynamics of discussions is another interesting, yet mostly unexplored, aspect of research, especially the possible relation between the structure and the dynamics of discussion trees. This question could be explored by means of machine learning. Finally, the huge volume of new posts and comments makes the design of efficient ranking and recommendation algorithms vital, in order to allow users to identify relevant information and improve their online experience.