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Users of social networks tend to post and share content with littlerestraint. Hence, rumors and fake news can quickly spread on a huge scale. Thismay pose a threat to the credibility of social media and can cause seriousconsequences in real life. Therefore, the task of rumor detection andverification has become extremely important. Assessing the veracity of a socialmedia message (e.g., by fact checkers) involves analyzing the text of themessage, its context and any multimedia attachment. This is a verytime-consuming task that can be much helped by machine learning. In theliterature, most message veracity verification methods only exploit textualcontents and metadata. Very few take both textual and visual contents, and moreparticularly images, into account. In this paper, we second the hypothesis thatexploiting all of the components of a social media post enhances the accuracyof veracity detection. To further the state of the art, we first propose usinga set of advanced image features that are inspired from the field of imagequality assessment, which effectively contributes to rumor detection. Thesemetrics are good indicators for the detection of fake images, even for thosegenerated by advanced techniques like generative adversarial networks (GANs).Then, we introduce the Multimodal fusiON framework to assess message veracItyin social neTwORks (MONITOR), which exploits all message features (i.e., text,social context, and image features) by supervised machine learning. Suchalgorithms provide interpretability and explainability in the decisions taken,which we believe is particularly important in the context of rumorverification. Experimental results show that MONITOR can detect rumors with anaccuracy of 96% and 89% on the MediaEval benchmark and the FakeNewsNet dataset,respectively. These results are significantly better than those ofstate-of-the-art machine learning baselines.

MONITOR: A Multimodal Fusion Framework to Assess Message Veracity in Social Networks