MONITOR: A Multimodal Fusion Framework to Assess Message Veracity in Social Networks - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

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

Résumé

Users of social networks tend to post and share content with little restraint. Hence, rumors and fake news can quickly spread on a huge scale. This may pose a threat to the credibility of social media and can cause serious consequences in real life. Therefore, the task of rumor detection and verification has become extremely important. Assessing the veracity of a social media message (e.g., by fact checkers) involves analyzing the text of the message, its context and any multimedia attachment. This is a very time-consuming task that can be much helped by machine learning. In the literature, most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. In this paper, we second the hypothesis that exploiting all of the components of a social media post enhances the accuracy of veracity detection. To further the state of the art, we first propose using a set of advanced image features that are inspired from the field of image quality assessment, which effectively contributes to rumor detection. These metrics are good indicators for the detection of fake images, even for those generated by advanced techniques like generative adversarial networks (GANs). Then, we introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features (i.e., text, social context, and image features) by supervised machine learning. Such algorithms provide interpretability and explainability in the decisions taken, which we believe is particularly important in the context of rumor verification. Experimental results show that MONITOR can detect rumors with an accuracy of 96% and 89% on the MediaEval benchmark and the FakeNewsNet dataset, respectively. These results are significantly better than those of state-of-the-art machine learning baselines.
Fichier principal
Vignette du fichier
ADBIS2021.pdf (650.16 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03224965 , version 1 (03-09-2021)

Licence

Paternité

Identifiants

Citer

Abderrazek Azri, Cécile Favre, Nouria Harbi, Jérôme Darmont, Camille Noûs. MONITOR: A Multimodal Fusion Framework to Assess Message Veracity in Social Networks. 25th European Conference on Advances in Databases and Information Systems (ADBIS 2021), Aug 2021, Tartu, Estonia. pp.73-87, ⟨10.1007/978-3-030-82472-3_7⟩. ⟨hal-03224965⟩
79 Consultations
28 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More