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
Spread and reception of fake news promoting hate speech against migrants and refugees in social media: Research Plan for the Doctoral Programme Education in the Knowledge Society
TL;DR: This research will triangulate three methods to study how fake contents in social media contribute to the spread of hate speech against migrants and refugees, combining some of the most relevant and urgent topics of current Western societies.
Proceedings ArticleDOI
FakeFinder: Twitter Fake News Detection on Mobile
Lin Tian,Xiuzhen Zhang,Min Peng +2 more
TL;DR: A fake news detection mobile app with a device-based prediction model based on the small language model ALBERT that can achieve real-time, accurate detection of fake news is designed and developed.
Proceedings ArticleDOI
Detecting Toxicity in News Articles: Application to Bulgarian
TL;DR: In this paper, a news toxicity detector that can recognize various types of toxic content was proposed and developed to help limit the spread and impact of fake news and toxic content in Bulgarian news.
Journal ArticleDOI
Detecting Health-Related Rumors on Twitter using Machine Learning Methods
TL;DR: This paper is dealing with detecting health-related rumors focusing on cancer treatment information that are spread over social media using Arabic language, and presents the process of creating a dataset that is called Health-Related Rumors Dataset (HRRD) which will be available and beneficial for further studies in health- related research.
Book ChapterDOI
Incorporating Relational Knowledge in Explainable Fake News Detection
TL;DR: In this article, a knowledge graph enhanced framework for detecting fake news while providing relational explanation is proposed, which first builds a credential-based multi-relation knowledge graph by extracting entity relation tuples from the training data and then applies a compositional graph convolutional network to learn the node and relation embeddings accordingly.
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