# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 3 Detecting Echo Chambers in Online Social Platforms
### Step 2: Users' Ideology Inference  
Throughout the slightly different definitions of echo chambers given over the years, the concept of homogeneous group thinking always emerges. Indeed, once we have identified a controversial issue and users discussing it, the second step of our approach consists of estimating users' leaning on the controversy. Following the rationale explained in Section 2.2, we rely on users' posts and comments to infer users' ideology. Indeed, all online social platforms provide some sort of text publishing as a basic functionality to their users (e.g., Twitter, Facebook, Reddit, Gab). Thus, we model the task of predicting the ideology of a user on a controversy as a text classification problem. In other words, given a controversial topic (e.g., the debate around Gun Control), we encode the textual content of users' posts into a low-dimensional vector representation and use it to train a classifier aimed at predicting users' ideology (e.g., _Anti-Gun_, _Pro-Gun_). Notice that the selection of one text classification model over the other strictly depends on data source peculiarities. Accordingly, it is of utmost importance to perform ad hoc model selection and fine-tuning to consider context-specific features. For instance, the size of available data is a crucial part of selecting a model. Usually, Deep Learning models need a huge amount of data to learn model parameters as well as impart generalization. However, pre-trained models such as Transformers are able to target the classification task even with low data resources thanks to their extensive pre-training. Another aspect to consider is whether we are interested in capturing sentence-level semantics or, rather, in extracting specific information from the sentence. In the first case, methods able to model long-range dependencies are the most suitable (e.g., LSTM, Transformer models), while in the second, architecture able to extract position-invariant features (e.g., CNN) are preferable [47].  
Moreover, a controversy may not necessarily induce binary opinions about it, e.g., wrong/right, pro/against. Suppose, for instance, that we are interested in exploring the political debate in the US. Beyond the simple Republican-Democrat analysis, we can identify other popular ideologies such as Liberalism, Conservatism, Libertarianism, and Populism. Accordingly, in such a scenario, our framework can be instantiated with a multi-class text classifier.  
Since we tackle a supervised approach, it requires a ground truth of sample posts labeled with respect to their opinion on the controversy. Even if it seems a tricky step in our pipeline, it is not so difficult to find among different online social platforms, publicly known polarized user collectors in which subscribed users support a specific leaning. Forinstance, we may rely on Twitter lists, Facebook pages, Reddit subreddits, or Gab groups. Further, on those online platforms in which the majority of users do not use a nickname (e.g., Twitter and Facebook), we may also rely on public figures supporting a specific idea to define a ground truth. For example, following the previous example on the US political debate, we may retrieve all posts shared by the most prominent exponents of those parties.