# Detection of Hidden Communities in Twitter Discussions of Varying Volumes
## 1 Introduction  
With the growth of the Internet, its complexity has also grown substantially. In particular, social networking has provided for formation of user clusters corresponding to an increasingly big number of communities at various levels, up to the cross-platform one. As previous research states, the formation of communities is based on user traits (e.g., group belonging, identity, spoken language, or location), discussion patterns (e.g., liking, commenting, mentioning, or reposting), and content features (e.g., political views, values, or cultural differences). Communities may also be situational, or ad hoc [1], such as hashtag-based ones, or calculated, created by special curation [2]; in such cases, their borders are seen well enough. However, there are cases when hidden communities gradually accumulate online, e.g., within hashtagged discussions, and remain latent for non-involved observers; or, they are hidden on purpose, such as those created by terrorist groups. In any case, the detection of latent communities may serve not only as a metric for assessing the complexity of online discussions but also as a measure that can add to resolving burning social issues of security, political polarization, or cultural intolerance.  
Despite the growing number of works dedicated to detection of intentionally hidden and/or latent self-cumulative communities on social media [3, 4], a cumulation of communities in real-world discussions has so far mostly been an object of 'one case/one model' studies where tests on real data bear an illustrative function only, if at all. There is almost no comparative assessment of community detection models. Furthermore, the sizeand traits of discovered communities found by one model are not judged against those detected by other models, which, though, seems natural to do. Rare exceptions include works on several spectral algorithms for directed graphs [5] which consider clustering algorithms based on cyclic and acyclic patterns, as well as comparative evaluation of community detection algorithms [6] in which only two algorithms, Infomap and Louvain, are considered (also assessed in our paper--see below). However, in these works, the authors do not assess the possibility of using methods for identifying communities on graphs of different sizes, especially large (around 100,000 nodes) and extra-large (more than 500,000 nodes), and also do not investigate user interactions in discussions. Another shortcoming of the current research on hidden communities is that the models are not compared on the datasets of varying size, despite the fact that not all the models work with large-scale data well enough.  
Our research aims at partly covering the three aforementioned research gaps. The first gap is absence of comparative assessment of community detection models. The second one is selecting (or creating) the best model for real-world discussion data from social media. The third one is testing the models for the datasets belonging to one platform but significantly differing in size. Thus, the research design of this paper is the following: we test nine existing models of community detection in social media data on four datasets collected in 2013 to 2016 on Twitter by seven quality metrics that are considered precise in the previous literature. The old enough datasets are used, as they are virtually clean, in terms of botization and other types of computational propaganda, and are free from algorithmic impact upon the discussions cast by the platform itself.  
The remaining part of this paper is organized as follows. Section 2 describes the difference between various clustering algorithms for networks seen as directed/undirected graphs. Section 3 provides the selection of community detection methods and metrics for their quality evaluation, while Section 4 tells of our research design. We demonstrate the results for four various datasets in Section 5 and discuss them in the Conclusion.