FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection

TL;DR

FineFake is a comprehensive, multi-domain, knowledge-enriched dataset for fake news detection that includes fine-grained annotations, multi-modal content, and social context, addressing limitations of existing single-domain benchmarks.

Contribution

The paper introduces FineFake, a novel multi-domain benchmark with fine-grained annotations and knowledge enrichment, enabling more realistic fake news detection research.

Findings

Knowledge-enhanced domain adaptation improves detection accuracy.

FineFake covers diverse topics and platforms, reflecting real-world scenarios.

Extensive experiments establish reliable benchmarks for future research.

Abstract

Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content. However, these benchmarks typically focus solely on news pertaining to a single semantic topic or originating from a single platform, thereby failing to capture the diversity of multi-domain news in real scenarios. In order to understand fake news across various domains, the external knowledge and fine-grained annotations are indispensable to provide precise evidence and uncover the diverse underlying strategies for fabrication, which are also ignored by existing benchmarks. To address this gap, we introduce a novel multi-domain knowledge-enhanced benchmark with fine-grained annotations, named \textbf{FineFake}. FineFake encompasses 16,909 data samples spanning six semantic topics and eight platforms. Each news item is enriched with multi-modal content, potential social context, semi-manually verified common knowledge, and fine-grained annotations that surpass conventional binary labels. Furthermore, we formulate three challenging tasks based on FineFake and propose a knowledge-enhanced domain adaptation network. Extensive experiments are conducted on FineFake under various scenarios, providing accurate and reliable benchmarks for future endeavors. The entire FineFake project is publicly accessible as an open-source repository at \url{https://github.com/Accuser907/FineFake}.