Systematic Literature Review of Detecting Topics and Communities in Social Networks

Abstract

In the recent past and in today’s world, the internet is advancing rapidly and is easily accessible; this growth has made the social media platforms such as Facebook, Instagram, Twitter, and LinkedIn widely used which produces big data. This requires both topic Detection applications in order to access the required information, as well as community detection practices in order to provide collective services to communities that can be referred to as individuals with similar interests and opinions over the same subject. Therefore, it is vital for researchers to conduct research on topic detection and community detection research areas in social networks and to develop methods and techniques for problem-solving. In this study, a systematic and in-depth literature review is provided on studies that conduct topic and community analysis on social media platforms to provide a comprehensive overview of the given areas. Most of the studies to be analyzed are selected from articles using machine learning-based models that are known to achieve successful results in practice. As a result of the analysis of these studies; it has been concluded that a single model cannot be proposed in the area of topic detection and that the appropriate model should only be selected or created in a problem-specific way, taking into account all the characteristics of the given problem, while the Louvain method seems to stand out with its results in terms of performance in the area of community detection.