Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection

Fan Xu, Pinyun Fu, Qi Huang, Bowei Zou, AiTi Aw, Mingwen Wang

Submission Type: Regular Long Paper

Submission Track: NLP Applications

Keywords: Rumor detection, Contrastive learning, Knowledge distillation, Incomplete modality

TL;DR: This paper introduces CLKD-IMRD, a novel framework leveraging contrastive learning and knowledge distillation to address the challenges of incomplete modality in rumor detection.

Abstract: Rumors spread rapidly through online social microblogs at a relatively low cost, causing substantial economic losses and negative consequences in our daily lives. Existing rumor detection models often neglect the underlying semantic coherence between text and image components in multimodal posts, as well as the challenges posed by incomplete modalities in single modal posts, such as missing text or images. This paper presents CLKD-IMRD, a novel framework for Incomplete Modality Rumor Detection. CLKD-IMRD employs Contrastive Learning and Knowledge Distillation to capture the semantic consistency between text and image pairs, while also enhancing model generalization to incomplete modalities within individual posts. Extensive experimental results demonstrate that our CLKD-IMRD outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media.

Submission Number: 502