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
Citations
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Proceedings ArticleDOI
An Efficient Algorithm for Influence Blocking Maximization based on Community Detection
TL;DR: A community based algorithm called FC_IBM algorithm is proposed using fuzzy clustering and centrality measures for finding a good candidate subset of nodes for diffusion of positive information in order to minimizing the IBM problem.
Journal ArticleDOI
Deep learning for fake news detection on Twitter regarding the 2019 Hong Kong protests
Alexandros Zervopoulos,Aikaterini Georgia Alvanou,Konstantinos Bezas,Asterios Papamichail,Manolis Maragoudakis,Katia Lida Kermanidis +5 more
TL;DR: Experiments showed that the deep learning algorithms outperformed the traditional approaches, reaching scores as high as 99.3% F1 Score, with the multilingual state-of-the-art model XLM-RoBERTa outperforming other algorithms using raw untranslated text.
Journal ArticleDOI
Multi-Level Multi-Modal Cross-Attention Network for Fake News Detection
TL;DR: In this article, a multi-level multi-modal cross-attention network (MMCN) is proposed to exploit the multilevel semantics of textual content and jointly integrate the relationships of duplicate and different modalities (textual and visual modality) of social multimedia posts in a unified framework.
Peer Review
A Working Definition of Fake News
Pedro Baptista,Anabela Gradim +1 more
TL;DR: Fake news is a type of online disinformation with misleading and/or false statements that may or may not be associated with real events, intentionally designed to mislead or manipulate a specific or imagined public through the appearance of a news format with an opportunistic structure (title, image, content) to attract the reader's attention as mentioned in this paper .
Proceedings ArticleDOI
Online misinformation: from the deceiver to the victim
Anu Shrestha,Francesca Spezzano +1 more
TL;DR: Preliminary results show that there is a correlation between fake news publisher bias and its credibility and social network properties help in identifying active fake news spreaders.
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