VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text

TL;DR

VRoC is a novel rumor classification system that leverages variational autoencoders to improve detection, tracking, and verification of rumors on social media, especially for unseen rumors, outperforming existing methods.

Contribution

The paper introduces VRoC, a VAE-based multi-task rumor classifier with a co-train engine that enhances latent representations for better rumor detection and classification.

Findings

VRoC outperforms state-of-the-art methods by up to 26.9% in macro-F1 score.

VRoC effectively classifies unseen rumors with high accuracy.

The co-train engine improves VAE latent representations for rumor classification.

Abstract

Social media became popular and percolated almost all aspects of our daily lives. While online posting proves very convenient for individual users, it also fosters fast-spreading of various rumors. The rapid and wide percolation of rumors can cause persistent adverse or detrimental impacts. Therefore, researchers invest great efforts on reducing the negative impacts of rumors. Towards this end, the rumor classification system aims to detect, track, and verify rumors in social media. Such systems typically include four components: (i) a rumor detector, (ii) a rumor tracker, (iii) a stance classifier, and (iv) a veracity classifier. In order to improve the state-of-the-art in rumor detection, tracking, and verification, we propose VRoC, a tweet-level variational autoencoder-based rumor classification system. VRoC consists of a co-train engine that trains variational autoencoders (VAEs) and rumor classification components. The co-train engine helps the VAEs to tune their latent representations to be classifier-friendly. We also show that VRoC is able to classify unseen rumors with high levels of accuracy. For the PHEME dataset, VRoC consistently outperforms several state-of-the-art techniques, on both observed and unobserved rumors, by up to 26.9%, in terms of macro-F1 scores.