Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning

Abderrazek Azri (), Cécile Favre (), Nouria Harbi (), Jérôme Darmont () and Camille Noûs ()
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Abderrazek Azri: Université de Lyon, Lyon 2, UR ERIC
Cécile Favre: Université de Lyon, Lyon 2, UR ERIC
Nouria Harbi: Université de Lyon, Lyon 2, UR ERIC
Jérôme Darmont: Université de Lyon, Lyon 2, UR ERIC
Camille Noûs: Université de Lyon, Lyon 2

Information Systems Frontiers, 2023, vol. 25, issue 5, No 9, 1795-1810

Abstract: Abstract The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. 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. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR’s performance.

Keywords: Social networks; Rumor verification; Image features; Machine learning; Ensemble learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10796-022-10315-z

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