Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data

TL;DR

This paper presents a novel framework for cross-domain fake news detection using multi-modal data, combining domain-specific and cross-domain knowledge, and an unsupervised method for selecting informative unlabelled news for efficient training.

Contribution

It introduces a joint knowledge preservation framework and an unsupervised data selection technique to improve fake news detection across diverse and unseen domains.

Findings

Achieves state-of-the-art cross-domain detection performance

Improves detection accuracy for rarely-seen domains

Reduces manual labelling costs significantly

Abstract

With the rapid evolution of social media, fake news has become a significant social problem, which cannot be addressed in a timely manner using manual investigation. This has motivated numerous studies on automating fake news detection. Most studies explore supervised training models with different modalities (e.g., text, images, and propagation networks) of news records to identify fake news. However, the performance of such techniques generally drops if news records are coming from different domains (e.g., politics, entertainment), especially for domains that are unseen or rarely-seen during training. As motivation, we empirically show that news records from different domains have significantly different word usage and propagation patterns. Furthermore, due to the sheer volume of unlabelled news records, it is challenging to select news records for manual labelling so that the domain-coverage of the labelled dataset is maximized. Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost. Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets, while yielding notable improvements for rarely-appearing domains in news datasets.