## 2 Related Works
[21] proposed a technique called SVM-TS that employs time series of aggregated news attributes to detect fake news. Linguistic-based features can also be extracted from each tweet. Furthermore, features extracted from the topic using topic modeling approaches, such as Latent Dirichlet Allocation (LDA) [21]. Credibility features can also be extracted for posts in order to assess the reliability degree [22]. Using user-based clues could help differentiate between fake and real news. This is due to the fact that individuals who are more likely to spread incorrect information have distinct characteristics and attributes than those who are not [23]. These findings prompted researchers to look into user-based features for detecting fake news. For example, in [23], the authors examined user profiles and extracted features to distinguish between fake and real news. [PERSON] [12] uses user profile features as input to a hybrid CNN model for fake news detection. In [24], the authors concluded that the idea of incorporating a speaker profile could noticeably enhance performance. Most importantly, social propagation features, such as followers and retweet counts, can be used as useful insights for determining how fake news can spread in social media [19]. In recent research, many useful methods for detecting fake news have relied on sequential neural networks for encoding news content and social context information, in which text sequences were analyzed unidirectionally [25]. With this bidirectional training approach, long-distance and semantic dependencies can be captured in the given sentences. A deep learning approach (FakeBERT) is proposed in [25] that uses BERT (Bidirectional Encoder Representations from Transformers) and the parallel blocks of a deep Convolutional Neural Network (CNN) with varying kernel sizes and filters combined with BERT. They found that this combination is useful in dealing with ambiguity, which is the most difficult challenge in natural language understanding. However, the authors overlooked the benefit of modeling user behavior clues, which could improve classification performance. Using the LIAR dataset, a machine learning framework for automatic fake news detection using BERT\({}_{base}\) was presented by [PERSON] et al. [26]. In order to extract local features, the input text was encoded using BERT\({}_{base}\), and then the output sequence was fed into a CNN network. Then, a CNN followed by a BiLSTM was used to encode the user profile features. The final output of the two components is fed into a classification layer. The authors concluded that user behavior features are important for fake news detection. A comparative study using different machine learning and deep learning models was conducted [27]; the authors showed the power of BERT and its variations in improving detection performance across different datasets. In a study conducted by [PERSON] et al. [28], the researchers analyzed social media data and developed a hybrid deep learning model that showed an accuracy of 0.892 on Twitter data and 0.953 on Weibo data. The researchers concluded that capturing the temporal behavior of articles and learning source characteristics about users' behavior was important for detecting fake news. Exploiting useful clues from user behavior on social media is of great importance to detect fake news, and as such, in this work, we investigate the impact of user posting behavior for fake news detection using the FakeNewsNet dataset. In addition, we assess the effectiveness of a different set of user posting behavior features. A majority of existing studies used features extracted from a given news article to model fake news detection. This approach is reasonable since news text is most commonly provided in isolation in public datasets; however, content-based features are often insufficient since malicious entities are frequently manipulating content, following success at detection, to mimic trustworthy content.