Multiverse: Multilingual Evidence for Fake News Detection

Dementieva, Daryna; Kuimov, Mikhail; Panchenko, Alexander

doi:10.3390/jimaging9040077

Cited by 3 publications

(3 citation statements)

References 57 publications

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“…Therefore, any information assessment system that aims to detect fake news must be language-agnostic. This means that the system must be capable of handling multiple languages, and its methods must be generalizable to other languages as well (Dementieva et al, 2023). Currently, there is limited support for multilingual fake news detection in existing techniques.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

“…Currently, there is limited support for multilingual fake news detection in existing techniques. However, some techniques (Ahuja & Kumar, 2023;Dementieva et al, 2023;Hammouchi & Ghogho, 2022) attempt to address this problem using a cross-lingual evidence approach. The basic idea behind this approach is that if a news item is true, it should be widespread in different languages and across media with different biases.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

“…Addressing multilingual capabilities is another critical aspect where EMIAS strives to meet user needs effectively. Given that disinformation is not restrained by language or geographical boundaries (Colomina et al, 2021), it is imperative for an effective solution like EMIAS to be capable of handling multiple languages while maintaining its efficacy across them, an issue highlighted by Dementieva et al (2023).…”

Section: How Does Emias Address Existing Issues?mentioning

confidence: 99%

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A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

Ali Khan,

Kauttonen,

Aunimo

et al. 2024

JITE:Research

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Aim/Purpose: The purpose of this paper is to address the challenges posed by disinformation in an educational context. The paper aims to review existing information assessment techniques, highlight their limitations, and propose a conceptual design for a multimodal, explainable information assessment system for higher education. The ultimate goal is to provide a roadmap for researchers that meets current requirements of information assessment in education. Background: The background of this paper is rooted in the growing concern over disinformation, especially in higher education, where it can impact critical thinking and decision-making. The issue is exacerbated by the rise of AI-based analytics on social media and their use in educational settings. Existing information assessment techniques have limitations, requiring a more comprehensive AI-based approach that considers a wide range of data types and multiple dimensions of disinformation. Methodology: Our approach involves an extensive literature review of current methods for information assessment, along with their limitations. We then establish theoretical foundations and design concepts for EMIAS based on AI techniques and knowledge graph theory. Contribution: We introduce a comprehensive theoretical framework for an AI-based multimodal information assessment system specifically designed for the education sector. It not only provides a novel approach to assessing information credibility but also proposes the use of explainable AI and a three-pronged approach to information evaluation, addressing a critical gap in the current literature. This research also serves as a guide for educational institutions considering the deployment of advanced AI-based systems for information evaluation. Findings: We uncover a critical need for robust information assessment systems in higher education to tackle disinformation. We propose an AI-based EMIAS system designed to evaluate the trustworthiness and quality of content while providing explanatory justifications. We underscore the challenges of integrating this system into educational infrastructures and emphasize its potential benefits, such as improved teaching quality and fostering critical thinking. Recommendations for Practitioners: Implement the proposed EMIAS system to enhance the credibility of information in educational settings and foster critical thinking among students and teachers. Recommendation for Researchers: Explore domain-specific adaptations of EMIAS, research on user feedback mechanisms, and investigate seamless integration techniques within existing academic infrastructure. Impact on Society: This paper’s findings could strengthen academic integrity and foster a more informed society by improving the quality of information in education. Future Research: Further research should investigate the practical implementation, effectiveness, and adaptation of EMIAS across various educational contexts.

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Section: Related Work and Research Gapsmentioning

confidence: 99%

Section: Related Work and Research Gapsmentioning

confidence: 99%

Section: How Does Emias Address Existing Issues?mentioning

confidence: 99%

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A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

Ali Khan,

Kauttonen,

Aunimo

et al. 2024

JITE:Research

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EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles

Leite,

Razuvayevskaya,

Bontcheva

et al. 2024

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

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Stance Detection in the Context of Fake News—A New Approach

Alsmadi,

Alazzam,

Al-Ramahi

et al. 2024

Future Internet

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Online social networks (OSNs) are inundated with an enormous daily influx of news shared by users worldwide. Information can originate from any OSN user and quickly spread, making the task of fact-checking news both time-consuming and resource-intensive. To address this challenge, researchers are exploring machine learning techniques to automate fake news detection. This paper specifically focuses on detecting the stance of content producers—whether they support or oppose the subject of the content. Our study aims to develop and evaluate advanced text-mining models that leverage pre-trained language models enhanced with meta features derived from headlines and article bodies. We sought to determine whether incorporating the cosine distance feature could improve model prediction accuracy. After analyzing and assessing several previous competition entries, we identified three key tasks for achieving high accuracy: (1) a multi-stage approach that integrates classical and neural network classifiers, (2) the extraction of additional text-based meta features from headline and article body columns, and (3) the utilization of recent pre-trained embeddings and transformer models.

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Multiverse: Multilingual Evidence for Fake News Detection

Cited by 3 publications

References 57 publications

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles

Stance Detection in the Context of Fake News—A New Approach

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