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

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