Abstract

It is difficult to distinguish between fake and real information on social media networks due to the ease of access and information's exponential expansion. The rapid expansion of information fraud has been facilitated by the simple distribution of knowledge through sharing. Where the spread of false information is widespread, the credibility of social media networks is also at risk. Therefore, it has become a research problem to automatically identify information as accurate or false based on its source, substance, and publisher. Despite its limits, machine learning has been crucial in the classification of data. This research examines various machine learning techniques for the identification of fake news and the existing approaches and the new methods proposed by researchers have been summarized.

Keywords:
Credibility Fake news Social media Identification (biology) The Internet Social network (sociolinguistics) Simple (philosophy)

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Topics

Genetic and Environmental Crop Studies
Life Sciences →  Agricultural and Biological Sciences →  Plant Science
Pasture and Agricultural Systems
Life Sciences →  Agricultural and Biological Sciences →  Forestry
Legume Nitrogen Fixing Symbiosis
Life Sciences →  Agricultural and Biological Sciences →  Plant Science

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