# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 1 Introduction  
During the last decade, the ease of access and ubiquity of online social media and online social networks (OSNs) has rapidly changed how we are used to searching, gathering, and discussing any kind of information. Such a revolution, as the promise of equality carried by the World Wide Web since its first appearance, has enriched us all. It has made geographical distances vanish and empowered all internet users, letting their voices be heard [1, 2]. However, at the same time, it has also increased our chances of encountering misleading behaviors. Indeed, the unlimited freedom of generating contents and the unprecedented information flooding we are used today are seeds that, if improperly managed, can make online platforms into fertile grounds for polluted realities [3, 4]. Among them, several studies [5, 6, 7] claim that overpersonalization enhanced by OSNs, leveraging the human tendency to interact with like-minded individuals, might lead to a self-reinforcing loop confining users in echo chambers. Although a formal definition of the phenomenon is still missing, an echo chamber (EC) is commonly defined as a polarized situation in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Such phenomenon might prevent the dialectic process of \"thesis-antithesis-synthesis\" that stands at the basis of a democratic flow of opinions, fostering several related alarming episodes [8] (e.g., hate speech, misinformation, and ethnic stigmatization). Furthermore, since discussions, campaigns, and movements taking place in online platforms also resonate in the physical world, it is no more possible to relegate their effect only to the virtual realm. For such reasons, a large body of scientific works [9; 10; 11; 12; 13; 14] has addressed the issue of echo chamber detection over the last decade, moving from content-only characterization toward a structural/topological analysis of the phenomenon. However, the lack of an actionable definition of echo chambers and a standard strategy to support their identification has led to conflicting experimental observations [15]. Further, most of the abovementioned works leverage platform-specific features or resources to identify ECs, thus strongly limiting their generalizability to the entire range of social media platforms. Moving from such literature, the purpose of this paper is twofold. Firstly, we propose a general framework to identify echo chamber on OSNs built on top of features they commonly share. Secondly, we aim to enrich the body of knowledge on EC detection, presenting a detailed case study on Reddit (i.e., the least explored social platform from an EC point of view). Starting from online social platform data, the approach to detect echo chambers presented in this paper can be summarized into a four-stage pipeline. _(i)_ Since opinion polarization generally arises in the presence of topics that trigger a significant difference of opinions, we propose starting from _controversial issue identification_. _(ii)_ Then, because an EC key feature is the homogeneous group thinking, the second step consists of _inferring users' ideology on the controversy_ from posts shared on the platform. _(iii)_ People inside an EC tend to interact with like-minded individuals, thus insulating themselves from rebuttal. To assess this requirement, we propose to _define the users debate network_ retrieving all posts' comments and labeling users with their leaning on the controversy. _(iv)_ Lastly, the fourth step consists of _homogeneous meso-scale users' clusters identification_. In other words, we look for areas of the network that are homogeneous from an ideological and topological point of view. Subsequently, we provide a case study of the proposed framework on Reddit.