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
The Application of Blockchain in Social Media: A Systematic Literature Review
TL;DR: The findings show that previous studies on the applications of Blockchain in social media are focused mainly on blocking fake news and enhancing data privacy, and this is the first systematic literature review that elucidates the combination of Blockchain and social media.
Proceedings ArticleDOI
BERT-Based Mental Model, a Better Fake News Detector
Jia Ding,Yongjun Hu,Huiyou Chang +2 more
TL;DR: This paper is the first to present a method to build up a BERT-based mental model to capture the mental feature in fake news detection and shows significant improvement over the state-of-art model based on the LIAR dataset by 16.71% in accuracy.
Proceedings ArticleDOI
Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings
TL;DR: This work compared 4 multitask learning models for this task and found that a model trained with English BERT achieves the best results for English, and multilingual BERT succeeds in detecting misinformation on social media in three languages: English, Bulgarian, and Arabic.
Book ChapterDOI
Beyond Fact-Checking: Network Analysis Tools for Monitoring Disinformation in Social Media
TL;DR: A Twitter dataset of more than 1.3M tweets focused on the Italian 2016 constitutional referendum is considered and the DisInfoNet Toolbox, designed to help a wide spectrum of users understand the dynamics of (fake) news dissemination in social networks, is considered.
Posted Content
Mining Social Media for Newsgathering: A Review
TL;DR: In this paper, the authors provide an overview of research in data mining and natural language processing for mining social media for newsgathering and discuss five different areas that researchers have worked on to mitigate the challenges inherent to social media news gathering: news discovery, curation of news, validation and verification of content, news gathering dashboards, and other tasks.
References
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Daniel Kahneman,Amos Tversky +1 more
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Journal ArticleDOI
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