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
Citations
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Posted Content
FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network
TL;DR: Li et al. as mentioned in this paper proposed a fake news credibility inference model based on a set of explicit and latent features extracted from the textual information, which can learn the representations of news articles, creators and subjects simultaneously.
Book ChapterDOI
Evaluating Deep Learning Approaches for Covid19 Fake News Detection
TL;DR: In this paper, the authors evaluate different supervised text classification algorithms on Contraint@AAAI 2021 Covid-19 Fake news detection dataset and evaluate the importance of unsupervised learning in the form of language model pre-training and distributed word representations using unlabeled covid tweets corpus.
Proceedings ArticleDOI
Deep Headline Generation for Clickbait Detection
TL;DR: A novel solution, named as Stylized Headline Generation (SHG), that can not only generate readable and realistic headlines to enlarge original training data, but also help improve the classification capacity of supervised learning is proposed.
Journal ArticleDOI
A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers
TL;DR: Ensemble Voting Classifier based, an intelligent detection system is proposed to deal with news classification both real and fake tasks, which can effectively find the most important highlights of the news.
Book ChapterDOI
Evaluating Deep Learning Approaches for Covid19 Fake News Detection
TL;DR: A look at automated techniques for fake news detection from a data mining perspective, and the importance of unsupervised learning in the form of language model pre-training and distributed word representations using unlabelled covid tweets corpus.
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