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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Proceedings ArticleDOI
Fake News, Disinformation, Propaganda, Media Bias, and Flattening the Curve of the COVID-19 Infodemic
TL;DR: The authors provide an overview of the emerging and interconnected research areas of fact-checking, misinformation, disinformation, propaganda, and media bias detection, with focus on text and computational approaches.
Journal ArticleDOI
Consumers' willingness to share digital footprints on social media: the role of affective trust
TL;DR: In this article , the authors examined consumers' cognitive and affective attitudes simultaneously to examine consumers' behavioral intention to share digital footprints on social media and found that both attitudes jointly influence consumers' behavioural intentions with trust as a key construct mediating the relationship between attitudinal antecedents.
Journal ArticleDOI
Dynamic Probabilistic Graphical Model for Progressive Fake News Detection on Social Media Platform
TL;DR: This article adaptively improves the Kalman Filter to the Labeled Variable Dimension Kalman filter that learns two universal patterns from true and fake news, respectively, which can capture the temporal information of time-series data that arrive unevenly.
Posted Content
Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection.
TL;DR: Li et al. as discussed by the authors proposed a scale-dot product attention mechanism to capture the relationship between text features and visual features, which performed better than the state-of-the-art method on a public Twitter dataset by 3.1% accuracy.
Journal ArticleDOI
SciLens News Platform: A System for Real-Time Evaluation of News Articles
TL;DR: The SciLens News Platform is evaluated on the emerging topic of COVID-19 where the discrepancies between low and high-quality news outlets are highlighted based on three axes, namely their newsroom activity, evidence seeking and social engagement.
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