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Fake news detection within online social media using supervised artificial intelligence algorithms

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  • Ozbay, Feyza Altunbey
  • Alatas, Bilal

Abstract

Along with the development of the Internet, the emergence and widespread adoption of the social media concept have changed the way news is formed and published. News has become faster, less costly and easily accessible with social media. This change has come along with some disadvantages as well. In particular, beguiling content, such as fake news made by social media users, is becoming increasingly dangerous. The fake news problem, despite being introduced for the first time very recently, has become an important research topic due to the high content of social media. Writing fake comments and news on social media is easy for users. The main challenge is to determine the difference between real and fake news. In this paper, a two-step method for identifying fake news on social media has been proposed, focusing on fake news. In the first step of the method, a number of pre-processing is applied to the data set to convert un-structured data sets into the structured data set. The texts in the data set containing the news are represented by vectors using the obtained TF weighting method and Document-Term Matrix. In the second step, twenty-three supervised artificial intelligence algorithms have been implemented in the data set transformed into the structured format with the text mining methods. In this work, an experimental evaluation of the twenty-three intelligent classification methods has been performed within existing public data sets and these classification models have been compared depending on four evaluation metrics.

Suggested Citation

  • Ozbay, Feyza Altunbey & Alatas, Bilal, 2020. "Fake news detection within online social media using supervised artificial intelligence algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119317546
    DOI: 10.1016/j.physa.2019.123174
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    References listed on IDEAS

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    1. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    2. Zhu, Hui & Wu, Heng & Cao, Jin & Fu, Gang & Li, Hui, 2018. "Information dissemination model for social media with constant updates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 469-482.
    3. Zhang, Yaming & Su, Yanyuan & Weigang, Li & Liu, Haiou, 2018. "Rumor and authoritative information propagation model considering super spreading in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 395-411.
    4. Bessi, Alessandro, 2017. "On the statistical properties of viral misinformation in online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 459-470.
    5. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
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    Cited by:

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    2. Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sanchez-Alonso, Salvador, 2023. "The power of big data analytics over fake news: A scientometric review of Twitter as a predictive system in healthcare," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    3. Muhammad Mudassar Yamin & Mohib Ullah & Habib Ullah & Basel Katt & Mohammad Hijji & Khan Muhammad, 2022. "Mapping Tools for Open Source Intelligence with Cyber Kill Chain for Adversarial Aware Security," Mathematics, MDPI, vol. 10(12), pages 1-25, June.
    4. Balasubramanian Palani & Sivasankar Elango, 2023. "CTrL-FND: content-based transfer learning approach for fake news detection on social media," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(3), pages 903-918, June.
    5. Mohammad Zubair Khan & Omar Hussain Alhazmi, 0. "Study and analysis of unreliable news based on content acquired using ensemble learning (prevalence of fake news on social media)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-9.
    6. Andrea Stevens Karnyoto & Chengjie Sun & Bingquan Liu & Xiaolong Wang, 2022. "TB-BCG: Topic-Based BART Counterfeit Generator for Fake News Detection," Mathematics, MDPI, vol. 10(4), pages 1-17, February.

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