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Analysis of institutional authors

Tretiakov, ArseniiAuthorD'Antonio-Maceiras, SergioAuthorMartin, AlejandroCorresponding Author

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July 31, 2025
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Article
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Toxic Discourse in the Digital Battlefield: Analysing Telegram Channels During the Russia-Ukraine 'Conflict'

Publicated to: Expert Systems. 42 (7): e70081- - 2025-07-01 42(7), DOI: 10.1111/exsy.70081

Authors:

Tretiakov, Arsenii; D'Antonio-Maceiras, Sergio; Hernandez, Aurea Anguera de Sojo; Martin, Alejandro
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Affiliations

Univ Politecn Madrid, Dept Comp Syst Engn, Madrid, Spain - Author

Abstract

Instant messenger Telegram has emerged as a favoured platform for far-right activism, conspiracy theories, political propaganda, and misinformation, which has its own target audience. This study explores the application of multilingual pre-trained language models to detect and measure toxicity in political content on Telegram channels. The proposed techniques have shown notable advancements in identifying toxic information using a fine-tuned RoBERTa model. Through the combination of data analysis, time-series analysis, and BERTopic modelling, the research demonstrates how toxicity varies by topic, country, and time period, using metadata. The study identified key topics in the dataset, which includes 23.6 million messages from 1491 Telegram channels, including the Russian-Ukrainian conflict and political tensions in Europe and the United States from 2016 to 1 July 2023. Despite these achievements, challenges such as the dominance of Russian language content and a focus on specific topics were highlighted. This research advances the understanding of how toxic language and propaganda are disseminated across different languages and political narratives, contributing to the study of digital communication and information warfare.
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Keywords

MisinformationSocial networksTelegramTopic modellingToxic informationTransformerTransformers

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Expert 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, 2025, it was in position 59/147, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Science, Theory & Methods. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría .

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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 2025-12-20:

  • 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).

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.
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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 (TRETIAKOV, ARSENII) and Last Author (MARTIN GARCIA, ALEJANDRO).

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

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Awards linked to the item

This work was supported by Ministerio de Ciencia, Innovacion y Universidades, European Commission, Comunidad de Madrid, Calouste Gulbenkian Foundation, and TUAI Project.
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