Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference (2023)

Abstract: Clickbait posts tend to spread inaccurate or misleading information to manipulate people’s attention and emotions, which greatly harms the credibility of social media. Existing clickbait detection models rely on analyzing the objective semantics in posts or correlating posts with article content only. However, these models fail to identify and exploit the manipulation intention of clickbait from a user’s subjective perspective, leading to limited capability to explore comprehensive clues of clickbait. To address such a issue, we propose a multiview clickbait detection model, named MCDM, to model subjective and objective preferences simultaneously. MCDM introduces two novel complementary modules for modeling subjective feeling and objective content relevance, respectively. The subjective feeling module adopts a user-centric approach to capture subjective features of posts, such as language patterns and emotional inclinations. The objective module explores news elements from posts and models article content correlations to capture objective clues for clickbait detection. Extensive experimental results on two real-world datasets show that our proposed MCDM outperforms state-of-the-art approaches for clickbait detection, verifying the effectiveness of integrating subjective and objective preferences for detecting clickbait.

## introduction clickbait often creates inaccurate or misleading posts through the use of exaggeration or sensationalism to manipulate and exploit people's attention and emotions, rather than to inform or educate them @xcite . however, these posts often do not match their articles and can even be deceptive, misleading users' judgment of articles and harming the credibility of social media platforms. accordingly, clickbait detection has become an emerging task for controlling the quality of social media content. ## related work clickbait detection has been constantly evolving and improving, from traditional methods based on manual features @xcite to automatic feature extraction using deep learning, as well as interaction and fusion of multiple features @xcite @xcite . the primary objective of clickbait detection is to extract crucial linguistic features from textual content. most of the current research is focused on modeling article content and detecting the correlation between posts and articles @xcite @xcite , with the use of deep learning algorithms @xcite @xcite increasingly employed to enhance the accuracy and efficiency of clickbait detection. chen et al. @xcite proposed the use of text and stylistic patterns for detection. potthast et al. @xcite focused primarily on parts-of-speech features of posts, proposing a machine-learning model based on textual, structural, emotional, topical, and image features. building upon previous work, chakraborty et al. @xcite analyzed the social sharing patterns of clickbait and non-clickbait posts and utilized sentiment analysis tools to determine the emotional information possessed by each post. zhou et al. @xcite employed a combination of bi-gru network and self-attention mechanism to learn the post representation posted by users. however, relying solely on post information is insufficient, as longer articles can provide additional valuable information. in recent years, more studies have focused on the correlation between posts and articles. biyani et al. @xcite detected the correlation by using the similarity between the title and the first five sentences of the corresponding article. zheng et al. @xcite proposed to recalculate the output of the traditional model according to user behavior. wu et al. @xcite ) considered the interaction between the context of posts and articles as well as the style patterns of posts. wei et al. @xcite proposed for the first time to use human semantic knowledge in an attention model and used language knowledge graphs to guide the attention mechanism. however, these methods only focus on considering the content correlation and neglect emotional and stylistic features that are crucial for clickbait detection. in contrast, our approach detects posts from subjective and objective perspectives jointly, facilitating accurate and quick clickbait detection. furthermore, it can enhance representation by focusing on the objective correlation between posts and articles. preference graph layer subjective preference crf tool & lexicon subjective feeling perspective module objective perspective content relevance module + article post … 2 1 objective preference preference graph layer represented as p @xmath0, w 2 , ..., w n } with n representing the number of tokens, the corresponding article is represented as d @xmath1, d 2 , ..., d m } where m is the number of tokens. from post p i , subjective feelings and objective entity knowledge are extracted to obtain a subjective preference map m @xmath2, with the i th fraction representing the model's degree of attention and preference for token i. the post text p is then used to extract its underlying emotional tendency feature e p , writing style feature s p , and initial semantic representation h p . subsequently, the post, objective preference map, and corresponding article d i are fed into the objective content association learning module to learn their interaction characteristics on the objective content, resulting in the objective content relation feature g obj . finally, the shared feature module is used to obtain the clickbait representation @xmath3, g obj ], which is classified into a predefined category (i.e. clickbait/non-clickbait). ## framework in this section, we introduce the framework of our proposed mcdm is shown in fig. 2 . it consists of four modules, i.e., a preference graph layer, a subjective feeling perspective modeling, an objective perspective content relevance modeling, and a perspectives fusion and output layer. ## conclusion in this paper, we propose mcdm, a clickbait detection framework guided by subjective and objective perspectives. mcdm takes into account the emotional and stylistic characteristics of posts from the subjective viewpoint and the clickbait information from the objective perspective by combining both post and article content. extensive experiments on two real-world datasets demonstrate that mcdm effectively improves the detection performance of clickbait by combining the features of posts from the subjective level and the interaction between posts and articles on the objective entity level.