# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 2 Related Work
### Ideology Detection  
Estimating user ideology on a controversy is challenging since online social platform users are rarely explicitly associated with a specific ideological label. Several works focusing on political EC [19; 21; 23] rely on the media slant consumed or shared by a user to infer its ideology. However, such a strategy is not feasible in other domains and, further, there is no guarantee that users only consume news outlets they agree with. On the other hand, the authors in [24; 25] have shown that different sides of a controversy are also detectable via the lexicon used. For instance, [PERSON] et al. [26] state that the language used in these contexts is often characterized by strongly biased terms as well as negative statements. Following this last line of reasoning, we estimate users' ideology on a controversy based on how they speak/write about it. Thus, we model such an issue as a text classification task.  
When dealing with textual data, it is of utmost importance to take into account both the suitable type of word representation and the proper type of classifier. Since traditional word representations (i.e., bag-of-words model) encode words as discrete symbols not directly comparable to others [27], they are not fully able to model semantic relations between words. Instead, word embeddings (e.g., Word2 vec [28] and Glove [29]), mapping words to a continuously valued low dimensional space, can capture their semantic and syntactic features. Moreover, their structure makes them suitable to be deployed with Deep Learning models, fruitfully used to address Natural Language Processing-related (NLP) classification tasks. Specifically, Recurrent Neural Networks (RNNs) have been proven to be extremely successful for sequence learning [30]. Among them, Long Short-Term Memory (LSTM) [31] network can maintain long-term dependencies through an elaborate gates mechanism, overcoming the vanishing gradient problem of standard RNNs. Another Deep Learning network dealing with NLP-related tasks is Convolutional Neural Network (CNN) [32]. Even though CNNs are primarily used in computer vision problems, e.g., image recognition [33; 34], they have also been proven to perform well with textual data, including generic NLP tasks, e.g., Part-Of-Speech Tagging, Chunking, Named Entity Recognition and Semantic Labeling [35], sentence modeling [36], search queries and document retrieval [37], and sentence classification [38]. Compared to the sequential architecture of RNNs, CNN has a hierarchical architecture able to extract position-invariant features instead of modeling context dependencies. Accordingly, they are most suitable in such tasks where keyword extraction is involved.  
More recently, in the literature have been introduced the so-called Transformer models that, differently from the previous ones, can process each word in a sentence simultaneously via the attention mechanism. In particular, autoencoding transformer models such as Bidirectional Encoder Representations from Transformers (BERT) [39] and the many BERT-based models spawning from it (e.g., RoBERTa [40], DistilBERT [41]), have proven that leveraging a bidirectional multi-head self-attention scheme yields state-of-the-art performances when dealing with sentence-level classification. However, also autoregressive monodirectional Transformer models like OpenGPT [42; 43] can be fruitfully leveraged for such kind of task [44]. Unlike BERT-based models, they leverage masked self-attention, which models the overall meaning of a sentence by only looking at the left context.