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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.

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Citations
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Journal ArticleDOI

A Hybrid Linguistic and Knowledge-Based Analysis Approach for Fake News Detection on Social Media

- 01 Jan 2022 - 
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid fake news detection system that combines linguistic and knowledge-based approaches and inherits their advantages, by employing two different sets of features: (1) linguistic features (i.e., title, number of words, reading ease, lexical diversity, and sentiment), and (2) a novel set of knowledgebased features, called fact-verification features that comprise three types of information namely, (i>reputation of the website where the news is published, (ii) coverage, and (iii) fact-check), i.e.
Book ChapterDOI

Automatic Fake News Detection with Pre-trained Transformer Models.

TL;DR: In this article, a binary content-based classification approach for detecting fake news automatically, with several recently published pre-trained language models based on the Transformer architecture, was presented.
Journal ArticleDOI

Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning

TL;DR: This paper proposes an adversarial contrastive learning framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low- Resourced regime, and develops a adversarial augmentation mechanism to further enhance the robustness of low-resource rumor representation.
Proceedings ArticleDOI

PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models

TL;DR: Li et al. as discussed by the authors proposed an end-to-end personalized text generation attack model, called PETGEN, that simultaneously reduces the efficacy of the detection model and generates posts that have several key desirable properties.
Proceedings ArticleDOI

Subjective Evaluation of Media Consumer Vulnerability to Fake Audiovisual Content

TL;DR: The authors' results show that the participants failed to detect two different types of fake videos, but participants’ detection performance improves when they know of the displayed individual or when a biometric reference video is available to them during the test.
References
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Issue of fake news

The paper discusses the issue of fake news on social media and its potential negative impacts on individuals and society.