A Federated Consensus-Based Model for Enhancing Fake News and Misleading Information Debunking

Published in Smart Innovation, Systems and Technologies, 2022

Recommended citation: Fiammetta Marulli, Laura Verde, Stefano Marrore, Lelio Campanile, "A Federated Consensus-Based Model for Enhancing Fake News and Misleading Information Debunking." Smart Innovation, Systems and Technologies, 2022. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135925429&doi=10.1007%2f978-981-19-3444-5_50&partnerID=40&md5=a567f27ff3e442d20cdc6c82a3ecefcf

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Abstract: Misinformation and Fake News are hard to dislodge. According to experts on this phenomenon, to fight disinformation a less credulous public is needed; so, current AI techniques can support misleading information debunking, given the human tendency to believe “facts” that confirm biases. Much effort has been recently spent by the research community on this plague: several AI-based approaches for automatic detection and classification of Fake News have been proposed; unfortunately, Fake News producers have refined their ability in eluding automatic ML and DL-based detection systems. So, debunking false news represents an effective weapon to contrast the users’ reliance on false information. In this work, we propose a preliminary study aiming to approach the design of effective fake news debunking systems, harnessing two complementary federated approaches. We propose, firstly, a federation of independent classification systems to accomplish a debunking process, by applying a distributed consensus mechanism. Secondly, a federated learning task, involving several cooperating nodes, is accomplished, to obtain a unique merged model, including features of single participants models, trained on different and independent data fragments. This study is a preliminary work aiming to to point out the feasibility and the comparability of these proposed approaches, thus paving the way to an experimental campaign that will be performed on effective real data, thus providing an evidence for an effective and feasible model for detecting potential heterogeneous fake news. Debunking misleading information is mission critical to increase the awareness of facts on the part of news consumers. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Author Keywords: Cooperative learning and networks; Fake news; Federated learning; Information debunking; Information literacy

Bibtex citation:

    author = "Marulli, Fiammetta and Verde, Laura and Marrore, Stefano and Campanile, Lelio",
    title = "A Federated Consensus-Based Model for Enhancing Fake News and Misleading Information Debunking",
    year = "2022",
    journal = "Smart Innovation, Systems and Technologies",
    volume = "309",
    pages = "587 – 596",
    doi = "10.1007/978-981-19-3444-5\_50",
    type = "Conference paper"

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