# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 5 Discussion, Conclusions and Future Works  
In this work, we proposed a formal definition of echo chamber and a general framework to assess their existence in online social networks. In doing so, we performed two choices that make our study quite different from the previous. Firstly, we handle echo chambers as meso-scale topologies. In other words, to detect ECs we do not rely on a single user's digital traces as in [14; 16] (i.e., following a _micro-scale approach_), nor suppose that all users in the network belong to a polarized community as in [18; 19; 20; 21; 22] (i.e., assuming a _macro-scale approach_). We focus on such peculiar EC since we are interested in the role  
Figure 5: Minorities discrimination (**a**) and Political Sphere (**b**) transition probabilities for echo chamber (\(EC\)) and communities (\(C\)) over contiguous semesters. that group dynamics play in increasing polarization. Further, we believe that it is quite unrealistic that all the users involved in a controversial debate insulate themselves in an echo chamber. Secondly, our framework is built upon features and resources commonly shared by most social networks, thus allowing its applicability across different OSNs and domains. In detail, such an approach consists of four main steps, namely: (i) controversial issue identification; (ii) users' ideology inference; (iii) debate network construction; (iv) homogeneous meso-scale users' clusters identification. Consequently, the basic bricks that we should find on OSNs to apply such an approach are the presence of discussions about controversial topics; the _post_ feature in order to infer users ideology on the controversy based on how they write about it; the _comment_ feature to define users debate network. To the best of our knowledge, all of these requirements are satisfied by the most popular OSNs (e.g., Twitter, Facebook, Reddit, Gab). Moreover, we applied our framework in a detailed case study on Reddit covering the first two and a half years of [PERSON]'s presidency (January 2017-July 2019). In such settings, our main aim was to assess the existence of Pro-Trump and Anti-Trump ECs among three sociopolitical issues. As concerns users' ideology, we have found that in the overall period, users tend to assume strongly polarized positions on the controversy rather than moderate ones across all topics. However, analyzing the stability of users' ideology over contiguous semesters, we noticed that users are not so rooted in their positions as expected. Indeed, an exception is made for Anti-Trump users discussing political issues, for which the majority of users have an \(\sim\)30/40% probability of changing their leaning. Regarding echo chambers, we have found that for Gun Control, minorities discrimination, and Political Sphere only 6.7%, 53.8%, 23.3% of total users fall in polarized communities. Among them, we observed that both the first and the second topics have a stronger tendency toward Pro-Trump beliefs, while the third to Anti-Trump ones. Additionally, we also assess ECs' stability and consistency over contiguous semesters, finding that EC members have a higher probability of interacting with like-minded individuals with respect to the other communities in the overall network. Comparing our results to the ones obtained by the only EC detection work on Reddit (20), we find both commonalities and differences. Even if the authors focus on a different period (i.e., 2016 presidential elections) and on a slightly different controversy (i.e., Republicans vs. Democrats), we also noticed that Reddit users, compared to those of other OSNs, show a lower tendency to insulate themselves from opposite viewpoints. This attitude could be attributable to the Reddit structure, which is more a social forum than a traditional social network (e.g., Twitter, Facebook). However, differently from us, they conclude that Reddit political interactions do not resemble an echo chamber at all. Such a difference could be imputable to the difference in scale between approaches.