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
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Book ChapterDOI
The CLEF-2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News
Preslav Nakov,Giovanni Da San Martino,Tamer Elsayed,Alberto Barrón-Cedeño,Rubén Míguez,Shaden Shaar,Firoj Alam,Fatima Haouari,Maram Hasanain,Nikolay Babulkov,Alex Nikolov,Gautam Kishore Shahi,Julia Maria Struß,Thomas Mandl +13 more
TL;DR: The CheckThat! Lab as mentioned in this paper evaluates technology supporting various tasks related to factuality, such as predicting which tweets in a Twitter stream are worth fact-checking and whether a claim in a tweet can be verified using a set of previously fact-checked claims.
Proceedings ArticleDOI
Stance Detection in Fake News A Combined Feature Representation
TL;DR: This paper presents an approach that combines lexical, word embeddings and n-gram features to detect the stance in fake news and investigates the importance of different lexicons in the detection of the classification labels.
Book ChapterDOI
Early detection of rumours on Twitter via stance transfer learning
TL;DR: This paper proposes convolutional neural network (CNN) CNN and BERT neural network language models to learn attitude representation for user comments without human annotation via transfer learning based on external data sources for stance classification and proposes CNN-BiLSTM- and Bert-based deep neural models to combine attitude representation and content representation for early rumour detection.
Proceedings ArticleDOI
Hierarchical Multi-modal Contextual Attention Network for Fake News Detection
TL;DR: Wang et al. as discussed by the authors proposed a hierarchical multi-modal contextual attention network (HMCAN) for fake news detection by jointly modeling the multidomain context information and the hierarchical semantics of text in a unified deep model.
Journal ArticleDOI
Fake news, ¿amenaza u oportunidad para los profesionales de la información y la documentación?
TL;DR: In this article, aproximación contextual del fenomeno de las noticias falsas in relation with el campo de la informacion y la documentacion and the papel que los profesionales del sector podemos ejercer eficaz y eficientemente.
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