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
Fake News Detection with Integration of Embedded Text Cues and Image Features
Deepak Mangal,Dilip Kumar Sharma +1 more
TL;DR: A novel approach using Convolution neural Network (CNN) and Long short-term memory (LSTM) has been proposed to find the reliability of the news and the result implies that the novel methodology is better than the state-of-the-art method.
Journal ArticleDOI
Effect of Conformity on Perceived Trustworthiness of News in Social Media
TL;DR: This article investigates whether critical and supportive comments can induce conformity in how readers perceive trustworthiness of news articles and respond to them and finds that individuals who conform are more inclined to take action: to report articles they perceive as fake, and to comment on and share articles they perception as real.
Proceedings ArticleDOI
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
TL;DR: In this article, the authors address the challenge of automatically classifying fake news versus satire, and train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics.
Journal ArticleDOI
A Hybrid Linguistic and Knowledge-Based Analysis Approach for Fake News Detection on Social Media
Noureddine Seddari,Abdelouahid Derhab,Mohamed Belaoued,Waleed Halboob,Jalal Al-Muhtadi,Abdelghani Bouras +5 more
TL;DR: This paper proposes a novel hybrid fake news detection system that combines linguistic and knowledge-based approaches and inherits their advantages, by employing two different sets of features: linguistic features and fact-verification features that comprise three types of information.
Journal ArticleDOI
A real-time hostile activities analyses and detection system
TL;DR: The proposed system in this paper provides a complete visual data analytics methods to assist users in achieving a comprehensive understanding of malicious activities at multiple levels such as adversary’s behaviour, victim's behaviour, content, and context level.
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