FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms

TL;DR

This paper introduces FakeSV, the largest Chinese short video fake news dataset, and proposes SV-FEND, a multimodal detection model leveraging cross-modal correlations and social context for improved fake news detection.

Contribution

The paper presents a large-scale Chinese short video dataset and a novel multimodal detection model that utilizes cross-modal features and social context information.

Findings

SV-FEND outperforms existing methods in fake news detection accuracy.

The FakeSV dataset enables comprehensive analysis of fake news characteristics.

Multimodal features and social context significantly improve detection performance.

Abstract

Short video platforms have become an important channel for news sharing, but also a new breeding ground for fake news. To mitigate this problem, research of fake news video detection has recently received a lot of attention. Existing works face two roadblocks: the scarcity of comprehensive and largescale datasets and insufficient utilization of multimodal information. Therefore, in this paper, we construct the largest Chinese short video dataset about fake news named FakeSV, which includes news content, user comments, and publisher profiles simultaneously. To understand the characteristics of fake news videos, we conduct exploratory analysis of FakeSV from different perspectives. Moreover, we provide a new multimodal detection model named SV-FEND, which exploits the cross-modal correlations to select the most informative features and utilizes the social context information for detection. Extensive experiments evaluate the superiority of the proposed method and provide detailed comparisons of different methods and modalities for future works.