# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 2 Related Work  
### Echo Chamber Detection  
Detecting and characterizing the echo chamber phenomenon is of utmost importance since it is the first step toward the deployment of actionable strategies to mitigate its effects. Although attempts to identify such a phenomenon have existed for several years now, there has been an exceptional growth of research efforts in the last decade that span over a wide range of different digital services (e.g., blogosphere [9], movie recommendation services [12], E-commerce platforms [11], and music streaming applications [10]). For the sake of this paper, we will focus on approaches proposed to address this issue on online social networks. Since a formal definition of echo chamber, as well as a standard methodology to assess its existence, is still missing, previous works follow different strategies to deal with it. We attempt to categorize them first by considering their focus on the _echo_ or _chamber_ dimension of the phenomenon, i.e., respectively the debated content/opinion and the network which allows them to echo among users. The _content-based_ approach relies on the assumption that polarized environments are detectable by looking at the leaning of content shared or consumed by a user as well as analyzing its sentiment on the controversy, regardless of its interactions with others. For instance, [PERSON] et al. [16] explore the US debate between Liberals and Conservatives on Facebook and Twitter, looking for partisan users, i.e., users who share news articles conforming to their political beliefs. [PERSON] et al. [14] follow a similar strategy on Facebook, but they also consider users' exposure to crosscutting contents from the news feed or friends. On the other hand, the _network-based_ strategy mainly focuses on finding clustered topologies in users' interactions rather than on their content homophily. In dealing with it, the authors of [17], first define the conversational network of Facebook users discussing the 2014 Thai election and then partition it into close-knit communities from a topological point of view. Nevertheless, most researchers have deployed a hybrid methodology to detect ECs, taking into account both users' ideology as well as their interactions with each other. One of the first works in this direction is attributable to [PERSON] et al. [18] who explored whether online communication resembles an EC, collecting several million tweets concerning twelve political and nonpolitical issues. Authors infer users' ideology relying on their using _follow_ for popular controversial accounts then define their interaction network via _retweet_. Similarly, [PERSON] et al. [19] first estimate users' leaning on political controversy based on the media slant that they share and consume and thus define the debate network through the _follow_ relationship. To the best of our knowledge, only [PERSON] et al. [20] have tackled such a task on Reddit, focusing on the debate between Republicans and Democrats during the 2016 elections. Different approaches have also led to a difference in scale in the detected echo chambers. We define _micro-scale ECs_ the outputs of those approaches in which EC detection is accomplished, relying on the online behavior of single users, thus losing their aggregate dimension [14,16]. On the contrary, _macro-scale ECs_ are identified looking at the users' interaction network on an aggregated level, not taking into account differences within certain areas of the network. For instance, [20,21] check if the overall network is strongly characterized by two insulated groups of users, i.e., the two sides of the controversy. In addition, [PERSON] et al. [22] search for a similar output, using a community detection approach but forcing the algorithm to find exactly two communities. Differently, a _mesoscale EC_ can be considered as a subset of nodes in the overall network that resembles an echo chamber. This implies that in the overall debate network, it is possible to identify multiple ECs having the same ideological leaning.