Proceedings ArticleDOI
Differences in Health News from Reliable and Unreliable Media
Sameer Dhoju,Main Uddin Rony,Muhammad Ashad Kabir,Naeemul Hassan +3 more
- pp 981-987
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TLDR
This study examines a collection of health-related news articles published by reliable and unreliable media outlets and uses machine learning to identify the source (reliable or unreliable) of a health- related news article.Abstract:
The spread of ‘fake’ health news is a big problem with even bigger consequences. In this study, we examine a collection of health-related news articles published by reliable and unreliable media outlets. Our analysis shows that there are structural, topical, and semantic patterns which are different in contents from reliable and unreliable media outlets. Using machine learning, we leverage these patterns and build classification models to identify the source (reliable or unreliable) of a health-related news article. Our model can predict the source of an article with an F-measure of 96%. We argue that the findings from this study will be useful for combating the health disinformation problem.read more
Citations
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CoAID: COVID-19 Healthcare Misinformation Dataset
Limeng Cui,Dongwon Lee +1 more
TL;DR: This work presents CoAID (Covid-19 heAlthcare mIsinformation Dataset), with diverse COVID-19 healthcare misinformation, including fake news on websites and social platforms, along with users' social engagement about such news.
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Drink bleach or do what now? Covid-HeRA: A dataset for risk-informed health decision making in the presence of COVID19 misinformation.
TL;DR: Covid-HeRA, a dataset for health risk assessment of COVID-19-related social media posts, is released and the severity of each misinformation story is studied, i.e., how harmful a message believed by the audience can be and what type of signals can be used to discover high malicious fake news and detect refuted claims.
Journal ArticleDOI
Leveraging volunteer fact checking to identify misinformation about COVID-19 in social media
Hyunuk Kim,Dylan Walker +1 more
TL;DR: A strategy that detects emerging health misinformation by tracking replies that seem to provide accurate information is implemented, which is more efficient than keyword-based search in identifying COVID-19 misinformation about antibiotics and a cure.
Journal ArticleDOI
Analysis and Detection of Health-Related Misinformation on Chinese Social Media
TL;DR: This study focuses on analyzing common characteristics of reliable and unreliable health-related information on Chinese online social media, and exploring possible detection method using machine learning algorithms.
Journal ArticleDOI
Survey of Text-based Epidemic Intelligence: A Computational Linguistics Perspective
TL;DR: This survey discusses approaches for epidemic intelligence that use textual datasets, referring to it as “text-based epidemic intelligence,” view past work in terms of two broad categories: health mention classification and health event detection.
References
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Journal ArticleDOI
The science of fake news
David Lazer,Matthew A. Baum,Yochai Benkler,Adam J. Berinsky,Kelly M. Greenhill,Filippo Menczer,Miriam J. Metzger,Brendan Nyhan,Gordon Pennycook,David Rothschild,Michael Schudson,Steven A. Sloman,Cass R. Sunstein,Emily A. Thorson,Duncan J. Watts,Jonathan L. Zittrain +15 more
TL;DR: The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age as discussed by the authors. But much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors.
Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
TL;DR: 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.
Posted Content
Fake News Detection on Social Media: A Data Mining Perspective
TL;DR: This survey presents 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, and future research directions for fake news detection on socialMedia.
Proceedings ArticleDOI
Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy
TL;DR: The role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster was highlighted, and automated techniques can be used in identifying real images from fake images posted on Twitter.
Journal ArticleDOI
Addressing Health-Related Misinformation on Social Media.
TL;DR: The ubiquitous social media landscape has created an information ecosystem populated by a cacophony of opinion, true and false information, and an unprecedented quantity of data on many topics, which can have adverse effects on public health.