Automatic Fake News Detection: A Review
Keywords:
Fake News, Automatic Detection, Unsupervised, multi-class classification, digital technologies, natural languageAbstract
Misinformation has exploded in the last two decades with the development of digital technologies. The damage caused by it to social harmony, peace, social trust, justice, and democracy has increased the necessity of automatic identification of bogus news and intervention. As more and more content is being available, a person may not deliver much attention to a news piece and may assume it to be reliable just from reading headlines. Also, as customer attention is divided, news portals are tending to publish clickbait or catchy headlines to seek more attention. This vicious cycle has resulted in furthering spread and damage of fake news. This study reviews and discusses methodology to detect fake news and suggest possible limitations along with appreciating their contribution. This survey reviews selected papers from 2017 to 2021 which have focused on developing state of the art models, benchmarks, or new datasets for fake news detection. We think that this survey will provide a good inception point for various technologies related to fake news and a more comprehensive understanding for anyone interested in researching it as well.
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