RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social Media

Wang, Ran; Du, Kehan; Chen, Qianhe; Zhao, Yifei; Tang, Mojie; Tao, Hongxi; Wang, Shipan; Li, Yiyao; Wang, Yong

doi:10.1145/3491101.3519712

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…The issue of trust in news providers has been gaining huge popularity in recent years, and the community is trying to find new approaches to study the news providers' trustworthiness [25]. This is due to the fact that non-verified news media have exploited technological development to spread misleading or fraudulent news [26], [27]. In general, trust in news providers is at historically low levels and new mechanisms to measure their trust are essential to overcome this age of uncertainty [28].…”

Section: Trustworthiness On News Providersmentioning

confidence: 99%

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Marche,

Cabiddu,

Castangia

et al. 2023

IEEE Trans. Serv. Comput.

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Technological development combined with the evolution of the Internet has made it possible to reach an increasing number of people over the years and given them the opportunity to access information published on the network. The growth in the number of fake news generated daily, combined with the simplicity with which it is possible to share them, has created such a large phenomenon that it has become immediately uncontrollable. Furthermore, the quality with which malicious content is made is increasingly high so even professional experts, such as journalists, have difficulty recognizing which news is fake and which is real. This paper aims to implement an architecture that provides a service to final users that assures the reliability of news providers and the quality of news based on innovative tools. The proposed models take advantage of several Machine Learning approaches for fake news detection tasks and take into account well-known attacks on trust. Finally, the implemented architecture is tested with a well-known dataset and shows how the proposed models can effectively identify fake news and isolate malicious sources.

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Section: Trustworthiness On News Providersmentioning

confidence: 99%

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Marche,

Cabiddu,

Castangia

et al. 2023

IEEE Trans. Serv. Comput.

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Who Does Not Benefit from Fact-checking Websites?

Tanaka

Inuzuka

Arai

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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The Landscape of User-centered Misinformation Interventions - A Systematic Literature Review

Hartwig,

Doell,

Reuter

2024

ACM Comput. Surv.

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Misinformation is one of the key challenges facing society today. User-centered misinformation interventions as digital countermeasures that exert a direct influence on users represent a promising means to deal with the large amounts of information available. While an extensive body of research on this topic exists, researchers are confronted with a diverse research landscape spanning multiple disciplines. This review systematizes the landscape of user-centered misinformation interventions to facilitate knowledge transfer, identify trends, and enable informed decision-making. Over 6,000 scholarly publications were screened, and a systematic literature review ( N = 172) was conducted. A taxonomy was derived regarding intervention design (e.g., labels, showing indicators of misinformation, corrections, removal, or visibility reduction of content), user interaction (active or passive), and timing (e.g., pre or post exposure to misinformation or on request of the user). We provide a structured overview of approaches across multiple disciplines and derive six overarching challenges for future research regarding transferability of approaches to (1) novel platforms and (2) emerging video- and image-based misinformation, the sensible combination of automated mechanisms with (3) human experts and (4) user-centered feedback to facilitate comprehensibility, (5) encouraging media literacy without misinformation exposure, and (6) adequately addressing particularly vulnerable users such as older people or adolescents.

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RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social Media

Cited by 3 publications

References 36 publications

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Who Does Not Benefit from Fact-checking Websites?

The Landscape of User-centered Misinformation Interventions - A Systematic Literature Review

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