Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
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TLDR
Wang et al. as discussed by the authors presented a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets.Abstract:
Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.read more
Citations
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Journal ArticleDOI
A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media
TL;DR: In this article , a theory-based, novel deep-learning approach (called TRNN) is proposed to detect disinformation in financial social media, which uses deep learning and data-centric augmentation.
Journal ArticleDOI
A Multi-label Classification System to Distinguish among Fake, Satirical, Objective and Legitimate News in Brazilian Portuguese
Janaina Ignacio de Morais,Hugo Queiroz Abonizio,Gabriel Marques Tavares,André Azevedo da Fonseca,Sylvio Barbon +4 more
TL;DR: A DSS (Decision Support System) based on a Text Mining pipeline with a set of novel textual features using multi-label methods for classifying news articles on these two domains is proposed to address the differences between objectivity and legitimacy of news documents.
Posted ContentDOI
Assessing disinformation through the dynamics of supply and demand in the news ecosystem
TL;DR: This investigation focuses on the temporal and semantic interplay of news, fake news, and searches in several domains, including the virus SARS-CoV-2 pandemic, and contends these results can be a powerful asset in informing campaigns against disinformation and providing news outlets and institutions with potentially relevant strategies.
Posted Content
A Multi-Platform Analysis of Political News Discussion and Sharing on Web Communities.
Yuping Wang,Savvas Zannettou,Jeremy Blackburn,Barry Bradlyn,Emiliano De Cristofaro,Gianluca Stringhini +5 more
TL;DR: In this article, the authors present a multi-platform measurement of the news ecosystem and find that fringe communities often have a disproportionate influence on other platforms w.r.t. pushing narratives around certain news, such as political elections, immigration, or foreign policy.
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ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation
TL;DR: In this paper, the authors proposed ScoreGAN for fraud review detection that makes use of both review text and review rating scores in the generation and detection process for improving the stability and scalability of the GAN.
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