2019
DOI: 10.1016/j.procs.2020.01.035
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Analysis of Classifiers for Fake News Detection

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Cited by 78 publications
(31 citation statements)
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“…Both datasets are publicly available and easily accessible on the web. Categorization of news as "fake news" can be "a very challenging and time-consuming task" [34]. Hence, existing datasets are used in this study.…”
Section: Methodsmentioning
confidence: 99%
“…Both datasets are publicly available and easily accessible on the web. Categorization of news as "fake news" can be "a very challenging and time-consuming task" [34]. Hence, existing datasets are used in this study.…”
Section: Methodsmentioning
confidence: 99%
“…Despite the systematic and significant response efforts and fact-checking against misinformation mobilised by both social media and media companies, fake news still persists due to the vast volume of online content, which leads people to see and share information that is partly, or completely, misleading. Previous and recent studies have almost exclusively focused on data from social media (e.g., Twitter) [8], fact-checking or reliable websites (e.g., snopes.com and politifact.com) [9], or existing datasets [10] which have the benefit to be cost-efficient. Due to the current difficult and unprecedented situation with the COVID-19 pandemic, never seen in the modern era [11], people have asked many questions about the novel coronavirus, such as the origin of the disease, treatment, prevention, cure, and transmission from or to pets, to face these challenges while staying informed and safe.…”
Section: Introductionmentioning
confidence: 99%
“…It has proven to be beneficial in detecting fake news for reducing misinformation risks [8]. Various classifiers have been applied on social media articles to classify news as fake using NLP techniques such as N-gram and CNN [9] or bag-of-words [10]. It has been especially worthwhile to apply NLP on human rights related social media articles [11].…”
Section: A Nlp Approaches To Recent Problemsmentioning
confidence: 99%