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Grant support

This work has been funded by Grant PLEC2021-007681 (XAIDisInfodemics) and PID2020-117263GB-100 (FightDIS) funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe", by the "European Union" or by the "European Union NextGenerationEU/PRTR", by grant PCI2022-134990-2 (MARTINI) of the CHISTERA IV Cofund 2021 program, funded by MCIN/AEI/10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR", by Calouste Gulbenkian Foundation, under the project MuseAI - Detecting and matching suspicious claims with AI.

Analysis of institutional authors

Giron, AdrianCorresponding AuthorHuertas-Tato, JavierAuthorCamacho, DavidAuthor

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March 13, 2025
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Multimodal Visio-Lingual Content Analysis to Detect Fake Content on Reddit

Publicated to:Lecture Notes In Computer Science. 15346 143-154 - 2025-01-01 15346(), DOI: 10.1007/978-3-031-77731-8_14

Authors: Giron, Adrian; Huertas-Tato, Javier; Camacho, David

Affiliations

Univ Politecn Madrid, Madrid, Spain - Author

Abstract

The spread of misinformation across online social networks poses a significant threat to political stability, societal harmony, and economic integrity. This paper tackles the challenge of analyzing online posts, which often consist of diverse heterogeneous modalities like text or image. We introduce a multimodal architecture designed to accurately identify manipulated content. Our approach leverages a large-scale, multimodal dataset encompassing images, captions, comments, and metadata for each post. A novel encoding strategy is employed to capture both the semantic content and hierarchical structure of the comments. Based on CLIP, an early fusion technique is applied to process and merge the hidden representations. This method effectively combines multimodal and unimodal data according to their respective information channels. The performance is evaluated against the Fakeddit dataset, achieving a competent accuracy in binary classification tasks (0.9506 acc(test)) with significant hardware limitations, and outperforming the SotA in more complex categorization tasks, with 0.9509 acc(test) and 0.9371 acc(test) in 3-way and 6-way label classification, respectively.

Keywords

FakeddiMisinformationMultimodal

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Lecture Notes In Computer Science due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 13/61, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods. Notably, the journal is positioned above the 90th percentile.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-16:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 1 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (GIRON JIMENEZ, ADRIAN) and Last Author (CAMACHO FERNANDEZ, DAVID).

the author responsible for correspondence tasks has been GIRON JIMENEZ, ADRIAN.