ClickGuard: A Trustworthy Adaptive Fusion Framework for Clickbait Detection

TL;DR

ClickGuard introduces an adaptive fusion framework combining BERT embeddings and structural features, achieving high accuracy in clickbait detection while emphasizing interpretability and robustness.

Contribution

The paper presents a novel adaptive fusion framework with SSAFB and hybrid CNN-BiLSTM for improved clickbait detection performance.

Findings

Achieved 96.93% testing accuracy, outperforming existing methods.

Validated SSAFB's effectiveness through ablation studies.

Demonstrated robustness and interpretability using LIME and PFI.

Abstract

The widespread use of clickbait headlines, crafted to mislead and maximize engagement, poses a significant challenge to online credibility. These headlines employ sensationalism, misleading claims, and vague language, underscoring the need for effective detection to ensure trustworthy digital content. The paper introduces, ClickGuard: a trustworthy adaptive fusion framework for clickbait detection. It combines BERT embeddings and structural features using a Syntactic-Semantic Adaptive Fusion Block (SSAFB) for dynamic integration. The framework incorporates a hybrid CNN-BiLSTM to capture patterns and dependencies. The model achieved 96.93% testing accuracy, outperforming state-of-the-art approaches. The model's trustworthiness is evaluated using LIME and Permutation Feature Importance (PFI) for interpretability and perturbation analysis. These methods assess the model's robustness and sensitivity to feature changes by measuring the average prediction variation. Ablation studies validated the SSAFB's effectiveness in optimizing feature fusion. The model demonstrated robust performance across diverse datasets, providing a scalable, reliable solution for enhancing online content credibility by addressing syntactic-semantic modelling challenges. Code of the work is available at: https://github.com/palindromeRice/ClickBait_Detection_Architecture