Calling to CNN-LSTM for Rumor Detection: A Deep Multi-channel Model for Message Veracity Classification in Microblogs

TL;DR

This paper introduces deepMONITOR, a deep neural network model that combines text, images, and sentiment analysis to improve rumor detection accuracy on social media, especially when social features are unavailable.

Contribution

The paper presents a novel multimodal deep learning model that integrates textual, visual, and sentiment features for rumor verification, addressing limitations of previous approaches.

Findings

deepMONITOR outperforms existing methods in accuracy on real-world datasets.

Incorporating images and sentiment improves rumor detection performance.

The model is effective even when social metadata is missing.

Abstract

Reputed by their low-cost, easy-access, real-time and valuable information, social media also wildly spread unverified or fake news. Rumors can notably cause severe damage on individuals and the society. Therefore, rumor detection on social media has recently attracted tremendous attention. Most rumor detection approaches focus on rumor feature analysis and social features, i.e., metadata in social media. Unfortunately, these features are data-specific and may not always be available, e.g., when the rumor has just popped up and not yet propagated. In contrast, post contents (including images or videos) play an important role and can indicate the diffusion purpose of a rumor. Furthermore, rumor classification is also closely related to opinion mining and sentiment analysis. Yet, to the best of our knowledge, exploiting images and sentiments is little investigated.Considering the available multimodal features from microblogs, notably, we propose in this paper an end-to-end model called deepMONITOR that is based on deep neural networks and allows quite accurate automated rumor verification, by utilizing all three characteristics: post textual and image contents, as well as sentiment. deepMONITOR concatenates image features with the joint text and sentiment features to produce a reliable, fused classification. We conduct extensive experiments on two large-scale, real-world datasets. The results show that deepMONITOR achieves a higher accuracy than state-of-the-art methods.