Toward a standard approach for echo chamber detection: Reddit case study

Featured Application The framework proposed in this paper could be used to detect echo chambers in a standard way across multiple online social networks (i.e., leveraging features they commonly share). Such application then allows for comparative analysis between different platforms, thus discovering if some are more polarized than others. Further, a standard echo chamber characterization could be a starting point for designing a Recommendation System able to recognize and mitigate such a phenomenon. In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach for its detection, is still lacking. This paper proposes a general framework to identify echo chambers on online social networks built on top of features they commonly share. Our approach is based on a four-step pipeline that involves (i) the identification of a controversial issue; (ii) the inference of users' ideology on the controversy; (iii) the construction of users' debate network; and (iv) the detection of homogeneous meso-scale communities. We further apply our framework in a detailed case study on Reddit, covering the first two and a half years of Donald Trump's presidency. Our main purpose is to assess the existence of Pro-Trump and Anti-Trump echo chambers among three sociopolitical issues, as well as to analyze their stability and consistency over time. Even if users appear strongly polarized with respect to their ideology, most tend not to insulate themselves in echo chambers. However, the found polarized communities were proven to be definitely stable over time.