# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 5 Discussion, Conclusions and Future Works
Indeed, authors have identified ECs looking at the users' interaction network on an aggregated level, thus not considering differences within specific meso-scale network regions. **Approach weaknesses and limitations. ** As with all frameworks, our proposal suffers of a few known limitations and weaknesses that need to be carefully taken into account while instantiating it. A first limitation lies in the loss of contextual details derivable from platform-specific features. Indeed, we define and identify echo chambers by means of common features and resources shared by multiple platforms, thus providing, in some sense, a high-level representation of the EC phenomenon. However, generalizability does not come for free and, further, we believe that _posts_ and _comments_ are good proxies for respectively inferring users' ideology and defining the debate network. Moreover, we acknowledge that different data sources, although possessing the set of features required by our framework, can require context-specific tuning for each proposed step. In particular, to infer the political leaning from textual data, we assume that ad hoc model selection and fine-tuning have to be performed to account for data source peculiarities. Political leaning classifiers are hardly transferable among different contexts, and we can assume that a \"no free lunch\" solution exists to this challenging task. Finally, another limitation of the proposed framework lies in the absence of a general rule to select the proper community discovery algorithm to identify ECs. Community Discovery is an ill-posed problem, and alternative algorithmic solutions are known to optimize different quality function differences that highly affect the resulting node clustering. In this paper, we opted for a model, Eva, designed to balance both topological and semantic information while partitioning the social graph. Indeed, such a choice, although reasonable, is not the only valid alternative to address node cluster identification. On the other hand, the major weakness of the proposed framework lies in the structural absence of a strong result validation strategy. Such an issue--affecting the majority of EC detection and political polarization studies--lies in the absence of reliable ground truth for individuals' political leaning labels. As in other works [13; 16; 18; 19; 20; 21], leaning labels are assigned making strong assumptions on the political orientation of users that take part to the discussions of polarized clusters--not separating real supporters from their opponents and trolls, nor relying on users' provided information. Indeed, the absence of a ground truth annotation does not allow making a final observation and, rather, to underline that--considering the chosen methodological proxy--ECs emerge. As future research directions, we have both short- and long-term plans. Firstly, to further support our claim of framework generalizability, we are currently designing other case studies on popular OSNs (e.g., Twitter, Facebook, Gab). Consequently, we would like to perform a comparative analysis of obtained results in such a way as to assess if some environments are more polarized than others. Secondly, given the information retrieved in the previous step, we would like to characterize ECs by describing their DNA (e.g., the characteristics of the users composing them in terms of online activities) and use such footprints to design an echo chamber-aware Recommendation System able to foster pluralistic viewpoints in suggestions. Conceptualization, V.M., G.R. and L.P.; methodology, V.M. ; software, V.M. ; validation, V.M., G.R. and L.P.; formal analysis, V.M. ; investigation, V.M., G.R. and L.P.; data curation, V.M. ; writing--original draft preparation, V.M., G.R. and L.P.; writing--review and editing, V.M., G.R. and L.P.; visualization, V.M. ; supervision, G.R. and L.P. All authors have read and agreed to the published version of the manuscript.