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Fake News Detection on Social Networks with Artificial Intelligence Tools: Systematic Literature Review

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10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019 (ICSCCW 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1095))

Abstract

Rapid advances in technology have enabled print media to be published online and the emergence of Facebook, Twitter, YouTube and other social networks. Social networks have become an important way for people to communicate with each other and share their ideas. The most important feature of social networks is the rapid information sharing. In this context, the accuracy of the news or information published is very important. The spread of fake news in social networks has recently become one of the biggest problems. Fake news affects people’s daily life and social order and may cause some negativity. In this study, the most comprehensive and prestigious electronic databases have been examined in order to find the latest articles about the detection of fake news in social networks by systematic literature review method. The main aim of the study is to reveal the benefits of artificial intelligence tools used in the detection of fake news and their success levels in different applications. As a result of the study, it was concluded that the success levels of artificial intelligence tools are over 90%. This study is thought to be a guide both researchers and individuals related to this field.

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Author information

Authors and Affiliations

  1. Department of Computer Information Systems, Near East University, 99138, Nicosia, Cyprus, Mersin 10, Turkey

    Murat Goksu & Nadire Cavus

Authors
  1. Murat Goksu
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  2. Nadire Cavus
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Corresponding author

Correspondence to Nadire Cavus .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Rafik A. Aliev

  2. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Mo Jamshidi

  5. Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Mustafa B. Babanli

  6. Department of Mechatronics, Near East University, Nicosia, Turkey

    Fahreddin M. Sadikoglu

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Goksu, M., Cavus, N. (2020). Fake News Detection on Social Networks with Artificial Intelligence Tools: Systematic Literature Review. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham. https://doi.org/10.1007/978-3-030-35249-3_5

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