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
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Journal ArticleDOI
Deep Learning for Fake News Detection in a Pairwise Textual Input Schema
TL;DR: In this article, a novel approach to the automatic detection of fake news on Twitter that involves pairwise text input, a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and various input modes, like word embeddings and both linguistic and network account features.
Journal ArticleDOI
A Deep Learning Framework for Detection of COVID-19 Fake News on Social Media Platforms
TL;DR: This study investigates the ability of deep neural networks, namely, Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Network (CNN), and a hybrid of CNN and L STM networks, to automatically classify and identify fake news content related to the COVID-19 pandemic posted on social media platforms.
Proceedings ArticleDOI
Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
TL;DR: This paper proposes an entity debiasing framework (ENDEF) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective and demonstrates that the proposed framework can largely improve the performance of base fake news detectors, and online tests verify its superiority in practice.
Journal ArticleDOI
Investigating Effects of Visual Anchors on Decision-Making about Misinformation
TL;DR: Analysis of the effects of visual anchors and strategy cues using a visual analytics system suggests that such interventions affect user activity, speed, confidence, and, under certain circumstances, accuracy.
Proceedings ArticleDOI
Detecting Fake News Articles
TL;DR: A framework which extracts 134 features and builds traditional known machine learning models like Random Forest and XGBoost is proposed and a deep learning based model (LSTM with self-attention mechanism) is proposed to see which one performs better in the fake news article detection in both political news and celebrity news domains.
References
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Prospect theory: analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
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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.
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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.