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Automatic Fake News Detection: A Review Article on State of the Art

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Key Digital Trends in Artificial Intelligence and Robotics (ICDLAIR 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 670))

Abstract

Fake news is a term used to describe incorrect information released to the public to hurt or profit others. People have used conventional media to distribute fake news and generate propaganda to sway public opinion since the dawn. However, new obstacles in detecting fake news have evolved with modern social media. Moreover, false news dissemination is becoming more like a business, with individuals being paid to generate bogus information, making old methods of detecting fake news inefficient. As a result, and because of the threat of fake news on societies, several academics are working to differentiate between false and authentic news by automating the detection process using machine learning. This paper addresses several known machine learning-based works for detecting bogus material based on context or content features. We will be explaining different methods for detecting fake news. We also describe various publicly available datasets for detecting false news. Finally, we conclude our paper by discussing the ongoing difficulties in detecting fake news.

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Notes

  1. 1.

    uvic.ca/engineering/ece/isot/datasets/fake-news/index.php.

  2. 2.

    kaggle.com/clmentbisaillon/fake-and-real-news-dataset.

  3. 3.

    kaggle.com/c/fake-news/overview.

  4. 4.

    Subset of the wikidata knowledge graph.

  5. 5.

    groups.csail.mit.edu/sls/downloads/factchecking/index.cgi.

  6. 6.

    networkx.org.

  7. 7.

    https://www.politifact.com/.

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Author information

Authors and Affiliations

  1. LRDSI laboratory, Sciences Faculty, Saad Dahlab Blida University, PB 270 Soumaa Road, 09000, Blida, Algeria

    Karim Hemina, Fatima Boumahdi & Amina Madani

Authors
  1. Karim Hemina
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  2. Fatima Boumahdi
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  3. Amina Madani
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Corresponding author

Correspondence to Fatima Boumahdi .

Editor information

Editors and Affiliations

  1. Department of Management and Innovation Systems, University of Salerno, Salerno, Italy

    Luigi Troiano

  2. Department of Engineering, University of Sannio, Benevento, Italy

    Alfredo Vaccaro

  3. Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, India

    Nishtha Kesswani

  4. Department of Computer Science, University of Oviedo, Gijón, Spain

    Irene Díaz Rodriguez

  5. EMLYON Business School, Écully, France

    Imene Brigui

  6. University College London, London, UK

    David Pastor-Escuredo

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Hemina, K., Boumahdi, F., Madani, A. (2023). Automatic Fake News Detection: A Review Article on State of the Art. In: Troiano, L., Vaccaro, A., Kesswani, N., Díaz Rodriguez, I., Brigui, I., Pastor-Escuredo, D. (eds) Key Digital Trends in Artificial Intelligence and Robotics. ICDLAIR 2022. Lecture Notes in Networks and Systems, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-30396-8_8

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