Emotion-Aware Clickbait Attack in Social Media

TL;DR

This paper presents an emotion-aware clickbait attack framework that uses stylistic transformations based on emotional modeling to evade detection systems and increase user engagement.

Contribution

It introduces an emotion-aware framework utilizing VAD space, Sentence-BERT, and LLMs to generate clickbait headlines that deceive classifiers effectively.

Findings

Emotion-aware stylization reduces detection accuracy to 2.58%-30.63%.

The framework models emotional dynamics to optimize clickbait impact.

Stylistic transformations significantly degrade classifier performance.

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.