# TChecker: A Content Enrichment Approach for Fake News Detection on Social Media
## 2 Related Work
### Content-Based Approach  
The most adopted approach in the task of identifying fake news is the content-based approach. In this approach, the textual features of the news are used in different models to identify the veracity of the news. This approach has been widely applied in detecting fake news from news posts and social posts. Fake News Detection from News Articles  
Verifying the truthfulness of news is a crucial step in the domain of publishing news, and checking the credibility of the source of information is an undeniable step in the publishing process. In the media domain, where journalists and others work in this domain, the verification of the news and its sources is their job. Journalists usually check the information against credible sources and verify this information is true before publishing it, that is, manual fact-checking. With the increase in the volume of data roaming the internet every second, automatic techniques stepped in to help in the fact-checking process. natural language processing and Information Retrieval techniques are applied to automatically identify fake news. A binary translating embedding (B-TransE) model was introduced in [18] to detect fake news based on a knowledge base graph; they evaluated their model by applying it to check the news in the dataset \"Getting real about fake news\" provided by Kaggle. CompareNet [19] is an end-to-end graph neural model that compares fake news against a knowledge base using entities. The style-based approach relies on the content of the news post to detect its truthfulness based on the style of writing in the post. The style of writing would reveal the user's intention to post false or true information. The style of the writing is represented as features to be fed to the model for detecting the truthfulness of the post. This approach was used by [20; 21; 22] in deception detection, where deception is defined as the bad intention of authors to post intentionally false information. Those features were used in detecting fake news from news articles in [23; 24]. Different machine learning algorithms have been applied to detect fake news from news posts through applying classification models on the textual content of the news. An analysis of different classifiers' performance on the LIAR dataset is presented in [25]; a comparison between the performance of Naive Bayes, SVM, Random Forest, Logistic regression, and stochastic gradient classifier is presented, showing that the classifiers obtained near results except for the stochastic gradient classifier, which performed worse than the others. Another comparison between the Naive Bayes, Random Forest, passive aggressive, and LSTM models is presented in [26]; they applied the three models on a dataset consisting of 11,000 English articles labeled as fake or real. They showed that the passive aggressive classifier with TF-IDF representation could achieve the highest accuracy and F1 score, while the LSTM model could achieve almost the same accuracy, but it obtained a higher precision compared to that achieved by the passive aggressive classifier. A classification model based on BiLSTM and self-attention layers is presented in [27] and applied on a dataset provided from Kaggle that consists of news articles labeled as fake or not. The articles are represented using GloVe [28] embeddings, then fed to the BiLSTM layer, followed by the self-attention layer, and then finally the classification layer. They compared their model to other models using different text representations, such as TF-IDF and BOW, and different neural networks, such as GRU, LSTM, and CNN. Upon the introduction of BERT in 2019 as a pre-trained language model using deep bidirectional transformers, a major change in performance in NLP tasks occurred. BERT differs from other deep learning embeddings like word embedding and sentence/document embeddings, in that it takes into consideration the context of the word from the two directions of the sentence.