Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
Reads0
Chats0
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
Proceedings ArticleDOI
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
TL;DR: In this paper, 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
Online Conspiracy Theories, Digital Platforms and Secondary Orality: Toward a Sociology of Online Monsters
TL;DR: This article conceptualized online conspiracism as a creative, if monstrous, response to the attention economy of social media and developed a program of research into its features and its surprising adaptation to the Attention regime of digital media.
Proceedings ArticleDOI
TRANSFAKE: Multi-task Transformer for Multimodal Enhanced Fake News Detection
TL;DR: Transfake as discussed by the authors is a Transformer-based model that jointly models the body content and comments of news systemically, and detects fake news with multi-task learning framework, which takes different modalities as input and employs multiple tasks, such as rumor score prediction and event classification, as intermediate tasks for extracting useful hidden relationships across various modalities.
Posted Content
Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda
TL;DR: This paper performs a comprehensive survey of algorithmic threats on social media and uses it as a lens to set a challenging but important research agenda for effective and real-time user nudging.
Proceedings Article
Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure.
TL;DR: A new approach to this task, where the use of conversation-based and affective-based features, covering different facets of affect, has been explored, showing the effectiveness of the feature set proposed.
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.