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This work has been supported by the research project CIVIC: Intelligent characterisation of the veracity of the information related to COVID-19, granted by BBVA FOUNDATION GRANTS FOR SCIENTIFIC RESEARCH TEAMS SARS-CoV-2 and COVID-19, by the Spanish Ministry of Science and Innovation under Fight-DIS (PID2020-117263GB-100) and XAI-Disinfodemics (PLEC2021-007681) grants, by Comunidad Autonoma de Madrid, Spain under S2018/TCS-4566 grant, by European Commission under IBERIFIER -Iberian Digital Media Research and Fact-Checking Hub (2020-EU-IA-0252), by "Convenio Plurianual with the Universidad Politecnica de Madrid in the actuation line of Programa de Excelencia para el Profesorado Universitario'' and by the research project DisTrack: Tracking disinformation in Online Social Networks through Deep Natural Language Processing, granted by Barcelona Mobile World Capital Foundation.

Analysis of institutional authors

Martin, ACorresponding AuthorHuertas-Tato, JAuthorHuertas-Garcia, AAuthorVillar-Rodriguez, GAuthorCamacho, DAuthor

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August 1, 2022
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FacTeR-Check: Semi-automated fact-checking through semantic similarity and natural language inference

Publicated to:Knowledge-Based Systems. 251 - 2022-01-01 251(), DOI: 10.1016/j.knosys.2022.109265

Authors: Martin, Alejandro; Huertas-Tato, Javier; Huertas-Garcia, Alvaro; Villar-Rodriguez, Guillermo; Camacho, David;

Affiliations

Univ Politecn Madrid, Madrid, Spain - Author

Abstract

Our society produces and shares overwhelming amounts of information through Online Social Net-works (OSNs). Within this environment, misinformation and disinformation have proliferated, becom-ing a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidence about the factual veracity of a claim is a crucial step to mitigate this harmful spread. To this end, we propose FacTeR-Check, a multilingual architecture for semi-automated fact-checking and hoaxes propagation analysis that can be used to implement applications designed for both the general public and for fact-checking organisations. FacTeR-Check implements three different modules relying on the XLM-RoBERTa Transformer architecture to evaluate semantic similarity, to calculate natural language inference and to build search queries through automatic keywords extraction and Named-Entity Recognition. The three modules have been validated using state-of-the-art benchmark datasets, exhibiting good performance in all of them. Besides, FacTeR-Check is employed to collect and label a dataset, called NLI19-SP, composed of more than 40,000 tweets supporting or denying 60 hoaxes related to COVID-19, released publicly. Finally, an analysis of the data collected in this dataset is provided, which allows to obtain a deep insight of how disinformation operated during the COVID-19 pandemic in Spanish-speaking countries. (c) 2022 The Author(s). Published by Elsevier B.V.

Keywords

Amount of informationAutomationBenchmarkingCovid-19HoaxLanguage inferenceMisinformationNatural language inferenceNatural languagesNetwork architecturePublic safetySafety concernsSemantic similaritySemanticsSocial networking (online)TransformerTransformers

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Knowledge-Based Systems 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, 2022, it was in position 19/145, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 2.16, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 14.16 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-26, the following number of citations:

  • WoS: 4
  • Scopus: 30
  • Google Scholar: 72

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 2025-07-26:

  • 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: 131 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/88876/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 30
  • Downloads: 15

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 (MARTIN GARCIA, ALEJANDRO) and Last Author (CAMACHO FERNANDEZ, DAVID).

the author responsible for correspondence tasks has been MARTIN GARCIA, ALEJANDRO.