Emotion-Aware Clickbait Attack in Social Media

arXiv cs.CL / 5/1/2026

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Key Points

The paper argues that clickbait is not just a static text pattern, but can be generated and optimized by explicitly targeting emotional dynamics beyond surface linguistic cues.
It introduces an emotion-aware clickbait generation attack framework using the Valence-Arousal-Dominance (VAD) model to steer stylistic transformations toward higher emotional impact.
To create realistic social media attack conditions, the authors align clickbait headlines with semantically similar posts using Sentence-BERT and produce multiple stylistic rewrites with LLMs.
They define a Curiosity Gap (CG) function to quantify how headline variations relative to the current post can increase curiosity while helping evade existing social media detection systems.
Experiments show that emotion-aware stylization can significantly reduce the accuracy of state-of-the-art classifiers, producing misclassification rate increases up to 2.58%–30.63%.

Abstract

Clickbait is characterized by disproportionately high emotional intensity relative to informational content, often reinforced by specific structural patterns. However, current research considers clickbait as a static textual phenomenon characterized by linguistic patterns and structural cues. Additionally, existing detection systems primarily rely on surface-level features of clickbait. This paper introduces an emotion-aware clickbait generation attack, where stylistic transformations are used to optimize emotional impact. We propose an emotion-aware framework based on the Valence-Arousal-Dominance (VAD) space to model the emotional dynamics underlying clickbait generation for optimal user engagement. To simulate realistic attack scenarios, we align clickbait headlines with semantically similar social media posts using Sentence-BERT and generate multiple stylistic rewrites via Large Language Models (LLMs). Building on this, we define a Curiosity Gap (CG) function that computes clickbait's headline variation to the current post to quantify how emotional activation will contribute to user curiosity and evade the existing system found on social media. Experimental results demonstrate that emotion-aware stylization significantly degrades the performance of state-of-the-art classifiers, leading to misclassification rates of up to 2.58% to 30.63% on the base system.