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
Posted Content
People Still Care About Facts: Twitter Users Engage More with Factual Discourse than Misinformation--A Comparison Between COVID and General Narratives on Twitter
Mirela Silva,Fabrício Ceschin,Prakash Shrestha,Christopher Brant,Shlok Gilda,Juliana Fernandes,Cátia Silva,André Grégio,Daniela Oliveira,Luiz Giovanini +9 more
TL;DR: This paper examined 2.1M COVID-19 misinformation tweets to understand misinformation as a function of engagement, tweet content, and veracity (misleading or factual) and found that factual tweets were more engaging than misinformation tweets; and features that most heavily correlated with engagement varied depending on the veracity and content of the tweet.
Posted Content
Leveraging Distributed Ledger Technologies and Blockchain to Combat Fake News.
TL;DR: The potential of DLTs and blockchain to combat fake news is explored, reviewing initiatives that are currently under development and identifying their main current challenges.
Journal ArticleDOI
Fake News Detection using Machine Learning Approach Multinomial Naive Bayes Classifier
Proceedings ArticleDOI
Local Perceptions and Practices of News Sharing and Fake News
Gionnieve Lim,Simon T. Perrault +1 more
TL;DR: This paper conducted a survey with 75 participants in Singapore to understand people's perceptions of and practices with news (real and fake) and found that fake news was more pervasive in instant messaging apps than in social media, with the problem attributed more strongly to sharing than to creation.
Posted Content
Towards Causal Understanding of Fake News Dissemination.
TL;DR: This work first proposes a principled approach to unbiased modelings of fake news dissemination under selection bias, then considers the learned fake news sharing behavior as the measured confounder and identifies the user attributes that potentially cause users to spread fake news.
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.