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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Journal ArticleDOI
A survey of Big Data dimensions vs Social Networks analysis.
TL;DR: This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s).
Journal ArticleDOI
Toward Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection
TL;DR: The authors developed an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics, and annotators than are previous approaches, achieving an F1 score of 0.83 with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank.
Proceedings ArticleDOI
A2Text-Net: A Novel Deep Neural Network for Sarcasm Detection
TL;DR: A new deep neural network is employed: A2Text-Net to mimic the face-to-face speech, which integrates auxiliary variables such as punctuations, part of speech (POS), numeral, emoji, etc. to increase classification performance.
Proceedings ArticleDOI
Fake News Detection Enhancement with Data Imputation
TL;DR: Experimental results show that Multi-Layer Perceptron (MLP) classifier with the proposed data preprocessing method outperforms baselines and improves the prediction accuracy by more than 15%.
Posted Content
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity.
Penghui Wei,Nan Xu,Wenji Mao +2 more
TL;DR: Experimental results on two benchmark datasets show that the proposed hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter outperforms previous methods in both rumor stance classification andVeracity prediction.
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Amos Tversky,Daniel Kahneman +1 more
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