Countering Misinformation Through Semantic-Aware Multilingual Models

Huertas-García, Álvaro; Huertas‐Tato, Javier; Martín, Alfonso Jiménez; Camacho, David

doi:10.1007/978-3-030-91608-4_31

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…This allows the model to consider the full sentence being transformed during training (Song et al 2020). Their work has enabled increased MPNet's use for semantic search and topic analysis (Huertas-García et al 2021), and we use MPNet in our work.…”

Section: Related Workmentioning

confidence: 99%

Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit

Hanley¹,

Kumar²,

Durumeric³

2022

Preprint

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In the buildup to and in the weeks following the Russian Federation's invasion of Ukraine, Russian disinformation outlets output torrents of misleading and outright false information.In this work, we study the coordinated information campaign to understand the most prominent disinformation narratives touted by the Russian government to English-speaking audiences. To do this, we first perform sentence-level topic analysis using the large-language model MPNet on articles published by nine different Russian disinformation websites and the new Russian "fact-checking" website waronfakes.com. We show that smaller websites like katehon.com were highly effective at producing topics that were later echoed by other disinformation sites. After analyzing the set of Russian information narratives, we analyze their correspondence with narratives and topics of discussion on the r/Russia and 10 other political subreddits. Using MPNet and a semantic search algorithm, we map these subreddits' comments to the set of topics extracted from our set of disinformation websites, finding that 39.6% of r/Russia comments corresponded to narratives from Russian disinformation websites, compared to 8.86% on r/politics.

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Section: Related Workmentioning

confidence: 99%

Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit

Hanley¹,

Kumar²,

Durumeric³

2022

Preprint

View full text Add to dashboard Cite

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“…Las consultas automáticas en combinación con el NLI utilizan como tecnología de base los transformers (Vaswani et al, 2017), un tipo de red neuronal que trabaja con representaciones vectoriales del texto que recogen las propiedades semánticas de las palabras y que tienen en cuenta el contexto en el que se producen. A partir de la similitud entre vectores (Huertas-García et al, 2021a;Huertas-García et al, 2021b) y la inferencia entre estos (Huertas-Tato et al, 2021), los transformers han sido aplicados de forma exitosa en diferentes aplicaciones para la detección de desinformación.…”

Section: Muestra Procedimiento E Instrumentosunclassified

Desinformación y vacunas en redes

Vivó

Grandío-Pérez

Villar-Rodríguez

et al. 2022

RLCS

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Introducción: Los bulos antivacunas son un tipo de desinformación sanitaria con gran peligro, dados sus efectos tangibles en la sociedad. Existen investigaciones relevantes sobre tipología de bulos, discursos negacionistas en redes o popularidad de las vacunas, pero este estudio aporta una visión complementaria y pionera, centrada en el discurso antivacunas de COVID-19 en Twitter desde la perspectiva del comportamiento de las cuentas que difunden desinformación. Metodología: A partir del método de FacTeR-Check, compuesto por cinco fases y una primera muestra basada en un centenar de bulos (diciembre de 2020 y septiembre de 2021) se descargaron 220.246 tuits, filtrados para trabajar con inteligencia artificial y técnicas de inferencia de lenguaje natural (NLI) sobre una segunda muestra de más de 36.000 tuits (N=36.292). Resultados: Los resultados ofrecen predominancias de algunos tipos de producción de desinformación, así como la eficacia de crear contenido original falso para agrupar seguidores o la identificación de un periodo (2013-2020) de más dominación de los usuarios que apoyan bulos, frente a los que los niegan. Discusión: El artículo muestra cómo la tipología o morfología de las cuentas puede ser un factor predictivo acerca del comportamiento de dichos usuarios respecto al caso particular de los bulos analizados. Conclusiones: Se ofrecen patrones de comportamiento del discurso antivacunas en Twitter, que pueden ayudar a gestionar futuros fenómenos similares. Dado el significativo tamaño de la muestra y de las técnicas empleadas, se puede concluir que este trabajo establece una base sólida para otros estudios comparativos sobre desinformación y salud en redes sociales.

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“…Semantics has many applications in a wide range of domains and tasks. Recent developments regarding Information Retrieval tasks [28,47,48] have demonstrated the potential of combining semantic-aware models along with traditional baseline algorithms (e.g., BM25) [49]. Moreover, the use of semantic-aware models has proven to be an excellent approach to counteract informational disorders (i.e., misinformation, disinformation, malinformation, misleading information, or any other kind of information pollution) [50,51,52,53] or to build automated fact-checking approaches [54].…”

Section: Importance Of Multilingual Semanticsmentioning

confidence: 99%

“…The multilingual extended STS Benchmark (mSTSb) [48] train set is used for fine-tuning the multilingual Transformers and fitting the variety of dimensional redcution techniques. This split consists of 16 languanges 2 combined in 31 mono and cross-lingual tasks with 5, 479 pair of sentences each one.…”

Section: Datamentioning

confidence: 99%

Exploring Dimensionality Reduction Techniques in Multilingual Transformers

Huertas-García¹,

Martín²,

Huertas‐Tato³

et al. 2022

Preprint

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Both in scientific literature and in industry, semantic and context-aware Natural Language Processingbased solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Human Language Understanding tasks is unquestionable, from conversational agents to the fight against disinformation in social networks. In addition, considerable attention is also being paid to developing multilingual models to tackle the language bottleneck. The growing need to provide more complex models implementing all these features has been accompanied by an increase in their size, without being conservative in the number of dimensions required. This paper aims to give a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual Siamese Transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques. In order to evaluate the effects of these techniques, we considered the multilingual extended version of Semantic Textual Similarity Benchmark (mSTSb) and two different baseline approaches, one using the pre-trained version of several models and another using their fine-tuned STS version. The results evidence that it is possible to achieve an average reduction in the number of dimensions of 91.58 % ± 2.59 % and 54.65 % ± 32.20 %, respectively. This work has also considered the consequences of dimensionality reduction for visualization purposes. The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for other highly demanding NLP tasks.

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Countering Misinformation Through Semantic-Aware Multilingual Models

Cited by 14 publications

References 16 publications

Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit

Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit

Desinformación y vacunas en redes

Exploring Dimensionality Reduction Techniques in Multilingual Transformers

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