Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
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
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Book ChapterDOI
Satirical News Detection with Semantic Feature Extraction and Game-Theoretic Rough Sets
Yue Zhou,Yan Zhang,JingTao Yao +2 more
TL;DR: This work applies game-theoretic rough set model to detect satirical news, in which probabilistic thresholds are derived by game equilibrium and repetition learning mechanism, and shows the robustness and improvement of the proposed approach compared with Pawlak rough set models and SVM.
Journal ArticleDOI
Analysing Misinformation Sharing Amongst College Students in India During COVID-19
TL;DR: In this article , a Google Form ques-tionnaire containing various questions about demography, motivations, information characteristics and post-pandemic effects of misinformation sharing was distributed among students of various colleges to collect the data.
Proceedings ArticleDOI
Team LRL_NC at SemEval-2022 Task 4: Binary and Multi-label Classification of PCL using Fine-tuned Transformer-based Models
Kushagri Tandon,S. Chatterjee +1 more
TL;DR: The aim of this paper is to present systems that can be used to classify a text as containing PCL or not, and toPresent systems that assign the different categories of PCL present in text.
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