## 1 Introduction  
Online social networks (OSNs) have gained importance due to their easy accessibility. They are tools used for exchanging information and influencing public opinion, rather than just a means of communicating between individuals [1]. In recent years, fake news has spread more widely due to the ease with which it can be created and distributed online. This type of news is not actual news, it is fake news that is made real for a specific purpose [2].  
Twitter is a common social media platform that people use to express their opinions and share their ideas with others. User-generated content (UGC) on OSNs, especially Twitter, is gaining more importance in the research community owing to its value in discovering patterns that significantly benefit various applications. According to the fake news velocity study, tweets that include falsified information reach people six times faster than tweets that include trustworthy information [3]. This indicates how terribly fake news disseminates and how it can have adverse social effects. The concern lies in the quick reactions, such as retweets, likes, and shares of a tweet (fake news story) received on Twitter without pre-thinking, aggravating the problem even more. According to [4], false news (particularly political news) on Twitter is usually retweeted by more users and spreads extremely rapidly. As [PERSON] et al. [5] pointed out, the proliferation of fake news on social media begins with user-posting behaviors.  
Much of the current work on fake news detection focuses on textual news content (see Section 2 for more details), ignoring user behavior clues that could have the potential to advance fake news detection performance. Moreover, most of these studies are based on extensions of the bag-of-words representation or other context-independent embeddings,which cannot capture deep semantically contextualized information. The ability to capture and understand semantics and contextualized information about the input text is crucial to identifying fake news.  
Given the benefits of user behavior clues in identifying fake news, we argue that the interplay between social user engagements (via feature engineering) and feature learning derived from state-of-the-art (SOTA) deep context-aware representation methods (such as BERT) has the potential to boost fake news classification performance. Given the power of user-behavior cues and the characteristics of SOTA pre-trained language models (PLMs) in generating deep-semantical contextual patterns of a given input text (i.e., news article), their complementary effects need to be investigated.  
As such, in this study, we investigated the impacts of user posting behaviors on fake news detection using the FakeNewsNet dataset and explored two research questions: (1) Does modeling both news content and user-posted behavioral cues improve fake news detection performance? and (2) How effective are user-posted behavioral cues in improving the detection performance of the proposed deep learning frameworks?  
The key contributions of this paper are as follows:  
* An examination of the performance of several deep learning algorithms and the current state-of-the-art word embeddings, such as BERT, on a benchmark dataset of fake news.
* For detecting fake news, new hybrid CNN-RNN architectures using attention modules were developed.
* Extensive experiments on two real-world fake news datasets demonstrate the effectiveness of the proposed frameworks for detecting fake news.  
The rest of this paper is organized as follows. Section 2 summarises the literature related to the detection of fake news. Section 3 provides an overall background of the models we used. We describe the problem statement in Section 4. The proposed deep-learning approaches are described in Section 5. Experiments on the performance of the predictive models are presented in Section 6. The experimental results are discussed in Section 7. Finally, Section 8 concludes the paper.