Shi, Chongyang, Yin, Yijun, Zhang, Qi, Xiao, Liang, Naseem, Usman, Wang, Shoujin, and Hu, Liang (2023) Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference. In: Findings of the Association for Computational Linguistics: EMNLP 2023. pp. 11807-11816. From: EMNLP 2023: Conference on Empirical Methods in Natural Language Processing, 6-10 December 2023, Singapore.

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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. Therefore, to bridge such a significant gap, 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.

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