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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Proceedings ArticleDOI
Credibility Assessment of User Generated health information of the Bengali language in microblogging sites employing NLP techniques
Afsara Benazir,Sadia Sharmin +1 more
TL;DR: In this paper, a novel dataset of Bangla health news, collected from Twitter, was created and fed to a fake news detection system, which employed state-of-the-art machine learning classifiers and neural network models to predict a label in a fixed category, thus achieving a maximum accuracy of 91% using CNN with fasttext embeddings.
Journal ArticleDOI
Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review
TL;DR: A taxonomy of machine learning and deep learning models and features adopted in content-based fake news detection is proposed and their performance is compared over the analysed works as discussed by the authors , which is the first attempt at identifying, on average, the best-performing models and feature over multiple datasets/topics tested in all the reviewed works.
MonographDOI
The Language of Fake News
TL;DR: The authors define fake news as news that is meant to deceive as opposed to inform and argue that there should be systematic differences between real and fake news that reflect this basic difference in communicative purpose.
Journal ArticleDOI
Is my stance the same as your stance? A cross validation study of stance detection datasets
TL;DR: In this paper , a cross-dataset model generalization of stance classification is investigated using stance classification models on 7 publicly available English Twitter datasets ranging from 297 to 48,284 instances.
Book ChapterDOI
Supervised Classification Methods for Fake News Identification
TL;DR: In this article, the authors present a comprehensive performance evaluation of eleven supervised algorithms on three datasets for fake news classification and compare them with the real and the false news, and show that fake news can propagate with an uncontrollable speed without verification and can cause severe damages.
References
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