KG-MFEND: an efficient knowledge graph-based model for multi-domain fake news detection

Abstract

The widespread dissemination of fake news on social media brings adverse effects on the public and social development. Most existing techniques are limited to a single domain (e.g., medicine or politics) to identify fake news. However, many differences exist commonly across domains, such as word usage, which lead to those methods performing poorly in other domains. In the real world, social media releases millions of news pieces in diverse domains every day. Therefore, it is of significant practical importance to propose a fake news detection model that can be applied to multiple domains. In this paper, we propose a novel framework based on knowledge graphs (KG) for multi-domain fake news detection, named KG-MFEND. The model’s performance is enhanced by improving the BERT and integrating external knowledge to alleviate domain differences at the word level. Specifically, we construct a new KG that encompasses multi-domain knowledge and injects entity triples to build a sentence tree to enrich the news background knowledge. To solve the problem of embedding space and knowledge noise, we use the soft position and visible matrix in knowledge embedding. To reduce the influence of label noise, we add label smoothing to the training. Extensive experiments are conducted on real Chinese datasets. And the results show that KG-MFEND has a strong generalization capability in single, mixed, and multiple domains and outperforms the current state-of-the-art methods for multi-domain fake news detection.

Soft-Label for Multi-Domain Fake News Detection

“…Zhu et al [31] modeled the semantics, emotion, and style of news and proposed a memory-guided multi-view multi-domain fake news detection framework (M3FEND) to enrich in-domain information. The knowledge graph-based multi-domain fake news detection framework (KG-MFEND) proposed by Fu et al [32] mitigates the domain differences at the word level by integrat- [33] models the dynamics in news dissemination and the background knowledge dynamics in the knowledge graph, and then combines with the time fusion unit to capture the cascade effect, thereby improving accuracy. In order to pursue a more reasonable and efficient multi-domain fake news detection technology, Nan et al [13] had experts in several domains (TextCNN) score one news item and aggregate the findings through the 'domain gate' to get the final conclusion.…”