Check-It: A plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web

Paschalides, Demetris; Kornilakis, Alexandros; Christodoulou, Chrysovalantis; Andreou, Rafael; Pallis, George; Dikaiakos, Marios D.; Markatos, Evangelos P.

doi:10.1145/3350546.3352534

Cited by 22 publications

(10 citation statements)

References 25 publications

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“…Over the past few years, many studies aim to detect misinformation and examine its impact. [7] It is the need of the hour to educate the average reader to make them more aware while reading and responsible while sharing. [8], [9] Credibility Analysis based studies have been gaining momentum and are opening new avenues of tackling misinformation.…”

Section: A Importance Of Credibility Analysismentioning

confidence: 99%

Explainable AI for Multimodal Credibility Analysis: Case Study of Online Beauty Health (Mis)-Information

et al. 2021

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One person's data or experience is another person's information" this has become the golden rule of the 21st century which has resulted in a massive reservoir of data and immense amounts of information generation. However, there is no control over the source of this information, accessibility of this information, or the quality of it, which has given rise to the presence of "misinformation". The research community has reacted by proposing frameworks and difficulties, which are helpful for (different subtasks of) recognizing misinformation. Most of these frameworks, however, fail to consider all the aspects that can contribute to making information "credible". Furthermore, a valid explanation for each considered feature's contribution to the model's decision stands missing in most work. With this in mind, the authors have attempted to produce a system that yields highly accurate decisions, thus effectively separating credible health blogs from their non-credible counterparts while providing valid user-friendly explanations. The study proposes an Explainable AI-assisted Multimodal Credibility Assessment System that examines the credibility of the platform where the blog is hosted, the credibility of the author of the blog and the credibility of the images that contribute to the blog. This novel framework contributes to the existing body of knowledge by assessing the credibility of misleading beauty blogs using multiple crucial modalities which would lead to an insightful information consumption by the users. The proposed pipeline was successfully implemented on multiple carefully curated datasets and correctly identified 274 non credible blogs out of 321 blogs with an accuracy of 97.5%, Precision of 0.973 & F1score of 0.986. Further, the Explainable AI model, with the help of several visualizations displayed the feature contributions for each blog & it's impact and magnitude in a concise comprehensible format. The framework can be further customized and applied to various domains where presence of misinformation is of high concern such as pharmaceutical drug information, pandemic management, financial advisories, online healthcare services and cyber frauds.

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Section: A Importance Of Credibility Analysismentioning

confidence: 99%

Explainable AI for Multimodal Credibility Analysis: Case Study of Online Beauty Health (Mis)-Information

et al. 2021

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“…Although the concern was there, the editors usually managed to find ways to mitigate it and reduce the intentional misinformation to the minimum possible: after all, the amount of news that came over the wire and could potentially be misinformation was not that large. Unfortunately, the "tsunami" of social media engagement that has swept our lives over the past decade practically exploded in a proliferation of misinformation, including the associated distribution of fake news (Paschalides et al 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Characteristics of Fake News and Misinformation in Greece: The Rise of New Crowdsourcing-Based Journalistic Fact-Checking Models

Lamprou

Antonopoulos

Anomeritou

et al. 2021

Journalism and Media

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Fake news and misinformation are a menace to the public sphere, democracy, and society with sometimes irreversible consequences. Journalists in the new era seem not to be able or willing to play their traditional role of gatekeeper and social media have made the problem even more intense. The need for truth is unnegotiable in modern democracies. Nevertheless, non-true stories and misinformation dominate media outlets with severe consequences and negative impacts on societies all over the world. Fact-checking platforms based on crowdsourcing strategies or automated digital websites might be the answer to a problem that is escalating. Initially, in order to tackle such a severe problem, researchers and experts have to monitor its characteristics. Very few research attempts have been conducted in Greece on fake news, its characteristics, origin, and impact. This dissertation scopes to map the characteristics of fake news and misinformation in an EU country such as Greece, based on the findings of “Ellinika Hoaxes” a fact-checking platform that uses in combination professional fact-checkers and crowdsourcing strategies in collaboration with Facebook. The findings shape new perspectives on the nature of misinformation and fake news in Greece and focus on new communication and fact-checking models.

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“…There is, however, one example of a dataset that is of a size suitable for machine learning. The Fake News Corpus as used by [47] contains over 10 million articles, 3 million of which deemed appropriate for use in this domain by [47]. Although this dataset is significant in size, unlike FakeNewsNet the dataset is not manually labelled.…”

Section: Features (Rq11)mentioning

confidence: 99%

A Systematic Review on the Detection of Fake News Articles

Hoy¹,

Koulouri²

2021

Preprint

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It has been argued that fake news and the spread of false information pose a threat to societies throughout the world, from influencing the results of elections to hindering the efforts to manage the COVID-19 pandemic. To combat this threat, a number of Natural Language Processing (NLP) approaches have been developed. These leverage a number of datasets, feature extraction/selection techniques and machine learning (ML) algorithms to detect fake news before it spreads. While these methods are well-documented, there is less evidence regarding their efficacy in this domain. By systematically reviewing the literature, this paper aims to delineate the approaches for fake news detection that are most performant, identify limitations with existing approaches, and suggest ways these can be mitigated. The analysis of the results indicates that Ensemble Methods using a combination of news content and socially-based features are currently the most effective. Finally, it is proposed that future research should focus on developing approaches that address generalisability issues (which, in part, arise from limitations with current datasets), explainability and bias.

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Check-It: A plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web

Cited by 22 publications

References 25 publications

Explainable AI for Multimodal Credibility Analysis: Case Study of Online Beauty Health (Mis)-Information

Explainable AI for Multimodal Credibility Analysis: Case Study of Online Beauty Health (Mis)-Information

Characteristics of Fake News and Misinformation in Greece: The Rise of New Crowdsourcing-Based Journalistic Fact-Checking Models

A Systematic Review on the Detection of Fake News Articles

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