Credibility-Based Fake News Detection

Sitaula, Niraj; Mohan, Chilukuri K.; Grygiel, Jennifer; Zhou, Xinyi; Zafarani, Reza

doi:10.1007/978-3-030-42699-6_9

Cited by 56 publications

(30 citation statements)

References 28 publications

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“…Content-based fake news detection aims to detect fake news by analyzing the content of news articles. Within a machine learning framework, researchers often detect fake news relying on either latent (via neural networks) [58,64] or non-latent (usually hand-crafted) features [12,40,48,53] of the content (see Section 2 for details). Nevertheless, in all such techniques, fundamental theories in social and forensic psychology have not played a significant role.…”

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confidence: 99%

Fake News Early Detection

et al. 2020

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Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theorydriven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information. CCS Concepts: • Human-centered computing → Collaborative and social computing theory, concepts and paradigms; • Computing methodologies → Natural language processing; Machine learning; • Security and privacy → Social aspects of security and privacy; • Applied computing → Sociology; Computer forensics;

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“…Note that some news articles might have fictional author names. Author information is valuable in evaluating news credibility by either investigating the collaboration network of authors [15] or exploring its relationships with news publishers and content [21]; (C6-7) News Title and Bodytext as the main textual information;…”

Section: Crawling Newsmentioning

confidence: 99%

“…https://www.naturalnews.com15 https://www.npr.org/sections/coronavirus-live-updates/2020/05/17/857512288/ob ama-malala-jonas-brothers-send-off-class-of-2020-in-virtual-graduation16 https://humansarefree.com/2020/05/researchers-100-covid-19-cure-rate-usingintravenous-chlorine-dioxide.html…”

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ReCOVery

Zhou

Mulay

Ferrara

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19. We first broadly search and investigate ∼2,000 news publishers, from which 60 are identified with extreme [high or low] levels of credibility. By inheriting the credibility of the media on which they were published, a total of 2,029 news articles on coronavirus, published from January to May 2020, are collected in the repository, along with 140,820 tweets that reveal how these news articles have spread on the Twitter social network. The repository provides multimodal information of news articles on coronavirus, including textual, visual, temporal, and network information. The way that news credibility is obtained allows a trade-off between dataset scalability and label accuracy. Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be compared. Our repository is available at http://coronavirus-fakenews.com. CCS CONCEPTS • Information systems → Collaborative and social computing systems and tools; Clustering and classification; • Security and privacy → Social aspects of security and privacy.

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“…Our economies are not immune to the spread of fake news, either, with fake news being connected to stock market fluctuations and massive trades [6,14]. The goal of this special issue is to promote the exchange of research and studies that (1) aim to understand and characterize fake news and its patterns and how it can be differentiated from other similar concepts, such as false/satire news, misinformation, disinformation, among others, which helps deepen our understanding of fake news, and (2) systematically detect fake news by determining its credibility [7], verifying its facts, assessing its writing style [5,15], or identifying its propagation [8,13]. To facilitate further research in fake news, this special issue presents recent research on misinformation and fake news.Popularity of social media [3, 10] and recent events align perfectly to the focus of this special issue as (1) the rise of social media has resulted in wider reachability across geographic regions and different ethnic and socio-economic groups, and (2) we are experiencing an "infodemic" of information with low credibility such as fake news and conspiracies on 9,12], presenting opportunities for state and individual actors to manipulate news for malicious gains through fake news.Here, we categorize accepted papers into two types: those that study the media and those that focus on techniques.…”

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confidence: 99%

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confidence: 99%

Inroduction on Recent Trends and Perspectives in Fake News Research

Zafarani¹,

Liu²,

Phoha³

et al. 2021

Digital Threats

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Fake news, especially on social media, is now viewed as one of the main digital threats to democracy, journalism, and freedom of expression [1,4,11,15]. Our economies are not immune to the spread of fake news, either, with fake news being connected to stock market fluctuations and massive trades [6,14]. The goal of this special issue is to promote the exchange of research and studies that (1) aim to understand and characterize fake news and its patterns and how it can be differentiated from other similar concepts, such as false/satire news, misinformation, disinformation, among others, which helps deepen our understanding of fake news, and (2) systematically detect fake news by determining its credibility [7], verifying its facts, assessing its writing style [5,15], or identifying its propagation [8,13]. To facilitate further research in fake news, this special issue presents recent research on misinformation and fake news.Popularity of social media [3, 10] and recent events align perfectly to the focus of this special issue as (1) the rise of social media has resulted in wider reachability across geographic regions and different ethnic and socio-economic groups, and (2) we are experiencing an "infodemic" of information with low credibility such as fake news and conspiracies on 9,12], presenting opportunities for state and individual actors to manipulate news for malicious gains through fake news.Here, we categorize accepted papers into two types: those that study the media and those that focus on techniques. The media, the main vehicle to spread fake news, has evolved from traditional forms such as newspapers and TV to becoming dominant social networks, such as Facebook, Twitter, and TikTok. Similarly, advances in machine learning and automated methods have led to techniques that can automatically spread fake news through social bots and can adapt to varying conditions and multiply the effect of fake news.This special issue presents significant findings on the analysis of fake news data and means to spread it. For instance, it shows research language-theoretic fact checking to determine the veracity of claims; reliable expert identification for fact checking and means to check the credibility of the data as well as of the credibility of the social media user; and analysis of fake news sharing through Zika virus spread that analyze threat, severity cues, and the resulting coping cues. CCS Concepts: • Human-centered computing → Collaborative and social computing theory, concepts and paradigms; Empirical studies in collaborative and social computing; • Computing methodologies → Natural language processing; Machine learning; • Security and privacy → Social aspects of security and privacy; • Applied computing → Sociology; Computer forensics;

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Credibility-Based Fake News Detection

Cited by 56 publications

References 28 publications

Fake News Early Detection

Fake News Early Detection

ReCOVery

Inroduction on Recent Trends and Perspectives in Fake News Research

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