## 2 Related Works  
Current research in fake news detection can be generally classified into content- and context-based fake news detection. According to [6], the former takes into account text-based features, such as general features, which are catalysts for describing content style from four linguistic levels: lexicon, syntax, discourse, and semantics. Some statistical techniques are applied to the lexicon level, such as the bag-of-words (BoW) model [7]. At the syntax level, part of speech (POS) (e.g., nouns and verbs) frequency is assessed using POS taggers [7]. A rhetorical structure theory and rhetorical parsing tool can be used at the discourse level to capture the resulting frequency of rhetorical relations among sentences as features [7]. On the semantic level, these frequencies correspond to lexicons or expressions assigned to each psycholinguistic category [8] (e.g., LIWC [9]). A second type of text-based feature is called a latent textual feature, which is used for news text embedding. These features can be conducted either at the word level [10], sentence-level [11], or document-level [11], which result in dense vector representations that can then be processed further. Some of the studies on detecting fake news are mainly based on the content; in [12], to detect fake news, the authors applied CNN and BiLSTM to embed textual and speaker metadata. In [13], the authors presented twenty-six linguistic-based textual features to detect fake news. In [9], the authors employed linguistic features (e.g., punctuation, readability, syntax, and psycholinguistic features) to distinguish between true and fraudulent news items, while the authors of [14] developed an enhanced set of linguistic features for distinguishing fake from true news. In [15], a Chinese WeChat clickbait dataset was created, and a multi-featured method of detecting WeChat clickbait was proposed, using semantic, syntactic, and auxiliary information. MFWCD-BERT and MFWCD-BiLSTM are models based on the MFWCDframework with varying parameter scales, which use Bidirectional Encoder Representation from Transformers (BERT) and lightweight Bidirectional Long Short-Term Memory (Bi-LSTM) networks with attention mechanisms to encode headline semantics, respectively. The researchers also presented an improved Graph Attention Network (GAT) with attention mechanisms to capture valuable structures in titles based on local syntactic structures. MFWCD performs better than compared baseline methods in clickbait detection, proving its effectiveness and interpretability. For other related research conducted for clickbait detection, see [16; 17]. It is inherently difficult to detect fake news because fake news is usually written intentionally to mislead the reader. Among the potential clues to the detection is the context-based features (aka auxiliary contextual information), such as user interactions on online social networks. Researchers combined content- and context-based features for characterizing fake news. For example, [PERSON] et al. [18] combined network account features with linguistic features. [PERSON] et al. [19] developed the social article fusion (SAF) model, which combines social engagement features with linguistic aspects using the FakeNewsNet dataset. Authors in [20] presented a deep learning model based on the hierarchical attention network using news content and user comments on Twitter for detecting fake news, and their framework achieved state-of-the-art results in the FakeNewsNet dataset. In the context of Twitter, post-based features refer to those retrieved from source tweets. Several studies used contextual information from tweets, such as temporal patterns embedded in a series of replies (i.e., comments) posted by users on social media and other elements representing their interactions and engagements. Several studies have used similar temporal patterns to identify useful patterns for fake news detection. For example, [PERSON] et al.