# Mimicking Human Verification Behavior for News Media Credibility Evaluation
## 3 Methodology
### Feature Extraction Intra Module  
In this work, we use two different feature extractors intra modules to mine the latent semantics of the title, content, search news results, and history similar news, which are written as _TEF_, _CEF_, _SsEF_, and _HsEF_ respectively. Specifically, the feature extractor has one attention layer and, at most, two Recurrent Neural Network (RNN) layers.  
Due to the fact that all textual articles consist of sentences and words, we naturally write the title and content as \(\{T\}_{i=1}^{I},\ \{C\}_{j=1}^{I}\), where \(I\) and \(J\) are the number of title and content sentences, and \(C_{j}=\left\{w_{1}^{j},w_{2}^{j},\cdots,w_{N_{j}}^{j}\right\}\) is a sentence with \(N_{j}\) words. Similarly, \(T_{i}\) represents a sentence containing \(N_{i}\) words. In this work, we limit the title length to one, i.e., \(I=1\). In addition, we introduce Glove [25] to vectorize each word with \(d\) dimension.  
Many studies have shown the significant ability of the hierarchical structure to extract features [9; 26], so we constructed a hierarchical attention network in order to capture the internal semantic information from words to sentences, namely, _TEF_, _CEF_, _SsEF_, and _HsEF_, and, just like when humans read a news text, it reads the entire text from local words to the whole sentence.  
Taking content elements as an instance and considering information loss during long sentence input, we use two Bi-GRU [27] layer to encode words and sentences separately. The first layer is used for word-level encoding and can be mimicked as the human forward and reverse reading process:  
\[h_{c}^{j}=\text{Bi}-\text{GRU}_{1}\Big{(}w_{c}^{j}\Big{)},\ c\in\{1,\cdots,N_ {j}\}, \tag{1}\]  
where \(c\) represents the \(c\)-th sentence of content, and where \(h_{c}^{j}\) is the output feature of Bi-GRU in both directions before and after the word \(w_{c}^{j}\in R^{d}\).  
Due to the strong ability of the attention mechanism in natural language processing [26; 28], we use an attention layer to redistribute the weight of words according to their importance. Each word's weight can be calculated as follows:  
\[u_{c}^{i}=tanh\Big{(}W_{c}^{j}h_{c}^{j}+b_{c}^{j}\Big{)}, \tag{2}\]\[\alpha_{c}^{j}=\frac{\exp\left(u_{c}^{j}U_{c}\right)}{\Sigma_{n=1}^{N_{c}}\exp \left(u_{n}^{j}U_{c}\right)}, \tag{3}\]  
where \(u_{c}\), \(W_{c}^{j}\), \(b_{c}^{j}\) are the learnable parameters, \(\alpha_{c}^{j}\) is the attention weight of the word \(w_{c}^{j}\), and \(u_{c}^{i}\) is a hidden representation of \(h_{c}^{j}\) achieved from a fully connected layer with _tanh_. Therefore, the weighted sentence embedding \(s^{j}\in R^{2d\times 1}\) can be calculated as follows:  
\[s^{j}=\Sigma_{c=1}^{N_{j}}\alpha_{c}^{j}h_{c}^{j}, \tag{4}\]  
After the attention layer, we apply the second layer to explore the contextual information of sentences by simulating the human reading process. Therefore, the feature of the \(j\)-th sentence can be expressed as follows:  
\[h^{j}=\text{Bi}-\text{GRU}_{2}\Big{(}s^{j}\Big{)},\ j\in\{1,\cdots,J\}, \tag{5}\]  
where \(h^{j}\in R^{2d\times 1}\) is the output of Bi-GRU in two directions around sentence \(s^{j}\).  
The structures of \(SsEF\) and \(HsEF\) are the same with \(CEF\). \(TEF\) can be considered as a variant of \(CEF\). For providing the human with a direct understanding of the article, a news editor often needs to describe the entire story in language that is as concise as possible. Therefore, a news title usually contains the most critical semantic information, which is a highly condensed form of the overall content. Therefore, for a news title, we use Bi-LSTM instead of Bi-GRU because the LSTM unit contains more parameters.