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
More filters
Journal ArticleDOI
Memory-Guided Multi-View Multi-Domain Fake News Detection
TL;DR: A Memory-guided Multi-view Multi-domain Fake News Detection Framework to simultaneously detect fake news of multiple domains and poses two challenges in multi-domain fake news detection.
Proceedings ArticleDOI
Domain Adaptive Fake News Detection via Reinforcement Learning
TL;DR: This work addresses the limitations of existing automated fake news detection models by incorporating auxiliary information into a novel reinforcement learning-based model called REinforced Adaptive Learning Fake News Detection (REAL-FND), which exploits cross-domain and within-domain knowledge that makes it robust in a target domain, despite being trained in a different source domain.
Journal ArticleDOI
Detection of fake news using deep learning CNN–RNN based methods
TL;DR: The authors used a deep learning method with several architectures such as CNN, Bidirectional LSTM, and ResNet, combined with pre-trained word embedding, trained using four different datasets.
Journal ArticleDOI
Knowledge graph informed fake news classification via heterogeneous representation ensembles
TL;DR: This article explored different document representations, ranging from simple symbolic bag-of-words, to contextual, neural language model-based ones, for efficient fake news identification, and showed that knowledge graph-based representations can achieve competitive performance to conventional representation learners.
Journal ArticleDOI
Detection of fake news using deep learning CNN–RNN based methods
TL;DR: The authors used a deep learning method with several architectures such as CNN, Bidirectional LSTM, and ResNet, combined with pre-trained word embedding, trained using four different datasets.
References
More filters
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book ChapterDOI
Prospect theory: an analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
TL;DR: In this paper, the authors present a critique of expected utility theory as a descriptive model of decision making under risk, and develop an alternative model, called prospect theory, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights.
Journal ArticleDOI
Prospect theory: analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
Book ChapterDOI
The social identity theory of intergroup behavior
Henri Tajfel,John C. Turner +1 more
TL;DR: A theory of intergroup conflict and some preliminary data relating to the theory is presented in this article. But the analysis is limited to the case where the salient dimensions of the intergroup differentiation are those involving scarce resources.
Journal ArticleDOI
Advances in prospect theory: cumulative representation of uncertainty
Amos Tversky,Daniel Kahneman +1 more
TL;DR: Cumulative prospect theory as discussed by the authors applies to uncertain as well as to risky prospects with any number of outcomes, and it allows different weighting functions for gains and for losses, and two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting function.