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Amharic Fake News Detection on Social Media Using Feature Fusion

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Advances of Science and Technology (ICAST 2021)

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Abstract

These days, many people use social media as a source of information and medium of communication due to its easy to access, fast to disseminate and low-cost platform. However, it also enables the wide propagation of fake news which causes economic, political, and social crises to the society. As a result, many researchers have been working towards detecting fake news. Most of the researches concerned on linguistic analysis of news content to identify its credibility, however fake news is also written intentionally to mislead users by mimicking true news. Beside this, Amharic is one of the under-resourced language that suffer from the benefits of fake news detection. To overcome the problem of fake news using content feature and under-resourced language, this study uses a feature fusion of linguistic and social context feature of the publisher information to detect Amharic fake news. For this, a total of 4,590 instance has been collected from different Facebook pages in different domain. Each article have been annotated by professional journalists and linguist for the purposes of doing experiments. The experimental result of feature fusion-based experiment shows at least 94.13% and at most 98.7% with a high relative error reduction over the content-based approaches. The result obtained from the experiment shows that, it is promising to detect fake news using fusion feature. We are now working towards incorporating intentionally edited pictures to the news content as part of the fake news detection.

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References

  1. Perrin, A.: Social Media Usage: 2005–2015. Pew Research Center, Washington (2015)

    Google Scholar 

  2. Howell, L.: Digital wildfres in a hyperconnected world. WEF Report 3, 15–94 (2013)

    Google Scholar 

  3. Abeselom, D.K.: The impacts of fake news on peace and development in the world: In the case of Ethiopia. Internal J. Curr. Res. 10, 71356–71365 (2018)

    Google Scholar 

  4. Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)

    Article  Google Scholar 

  5. Ahmed, H., Traore, I., Saad, S.: Detection of online fake news using n-gram analysis and machine learning techniques. In: Traore, I., Woungang, I., Awad, A. (eds.) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, pp. 127–138. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-69155-8_9

    Chapter  Google Scholar 

  6. Riedel, B., Augenstein, I., Spithourakis, G.P., Riedel, S.: A simple but tough-to-beat baseline for the Fake News Challenge stance detection task. arXiv preprint arXiv:1707.03264 (2017)

  7. Ozbay, F., Alatas, B.: Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A Stat. Mech. Appl. 540, 123174 (2020)

    Article  Google Scholar 

  8. Tacchini, E., Ballarin, G., Vedova, M.L.D., Moret, S., Alfaro, L.D.: Some like it hoax: automated fake news detection in social networks. In: Proceedings of the Second Workshop on Data Science for Social Good (SoGood), Skopje, Macedonia, 2017. CEUR Workshop Proceedings, vol. 1960 (2017)

    Google Scholar 

  9. Tlahun, A., Beshah, D.: Fake news detection model using machine learning approach: the caseof Amharic news on social media. unpublished masters thesis ,University of Gondar, Department of Information systems (2020)

    Google Scholar 

  10. Shu, K., Wang, S., Liu, H.: Exploiting Tri-Relationship for Fake News Detection (2017). arXiv:1712.07709 [cs]

  11. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, A.H.: Fake news detection on social media: a data mining perspective. In: KDD Exploration Newsletter (2017)

    Google Scholar 

  12. Zhou, X., Zafarani, R., Shu, K., Liu, A.H.: Fake news: fundamental theories, detection strategies and challenges. In: WSDM (2019)

    Google Scholar 

  13. Mahid, Z.I., Manickam, S., Karuppayah, S.: Fake news on social media: brief review on detection techniques. In: 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACC). IEEE, Subang Jaya (2018)

    Google Scholar 

  14. Shu, K., Mahudeswaran, D., Liu, H.: FakeNewsTracker: a tool for fake news collection, detection, and visualization. Comput. Math. Organ. Theory 25(1), 60–71 (2018). https://doi.org/10.1007/s10588-018-09280-3

    Article  Google Scholar 

  15. Tacchini, E., Ballarin, G., Vedova, M., Della, S.M., Alfato, L.D.: Some like it hoax: automated fake news detection in social networks (2017). arXiv preprint arXiv:1704.07506

  16. Vedova, D., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M., de Alfaro, L.: Automatic online fake news detection combining content and social signals. In: 22nd Conference of Open Innovations Association (FRUCT), pp. 272–279 (2018)

    Google Scholar 

  17. Ahmed, H., Traore, I., Saad, S.: Detection of online fake news using n-gram analysis and machine learning techniques. In: Traore, I., Woungang, I., Awad, A. (eds.) ISDDC 2017. LNCS, vol. 10618, pp. 127–138. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69155-8_9

    Chapter  Google Scholar 

  18. Shubham, B., Vijay, B., Jain, P., Chawla, M.: Natural language processing based hybrid model fordetecting fake news using content-based features and social features. Inf. Eng. Electron. Bus. 4, 1–10 (2019)

    Google Scholar 

  19. Getahun, B.A., Fekade.: Event modeling from amharic news article. Unpublished Master’s Thesis, Department of Computer Science, Addis Ababa University (2018)

    Google Scholar 

  20. ethiopiaonlinevisa (2020). https://www.ethiopiaonlinevisa.com/amharic-the-ethiopian-language/

  21. Morrison, G.R., Ross, S.M.: Experimental Research Methods. Researchgate (2003)

    Google Scholar 

  22. Gereme, F., Zhu, W., Ayall, T., Alemu, D.: Combating fake news in “low-resource” languages: amharicfake news detection accompanied by resource crafting. Information 12, 20 (2021)

    Article  Google Scholar 

  23. Michael, G.: Hornmorpho 2.5 User’s Guide. Indiana University, School of Informatics and Computing (2012)

    Google Scholar 

  24. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. In: KDD Exploration Newsletter (2017)

    Google Scholar 

  25. Potthast, M., Kiese, J., Reinart, K., Bevendorff, J., Stein, B.: A Stylometric Inquiry into Hyper partisan and Fake News (2017). arXiv preprint arXiv:1702.05638

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Acknowledgment

We would like to thank college of Informatics, University of Gondar for supporting the research work and Impact Amplifier Online Safety Project for sponsoring conference participation.

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Authors and Affiliations

  1. University of Gondar, Gondar, Ethiopia

    Menbere Hailu Worku

  2. Addis Ababa University, Addis Ababa, Ethiopia

    Michael Melese Woldeyohannis

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  1. Menbere Hailu Worku
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  2. Michael Melese Woldeyohannis
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Correspondence to Menbere Hailu Worku .

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Editors and Affiliations

  1. Bahir Dar Institute of Technology, Faculty of Civil and Water Resource Engineering, Bahir Dar University, Bahir Dar, Ethiopia

    Mulatu Liyew Berihun

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Worku, M.H., Woldeyohannis, M.M. (2022). Amharic Fake News Detection on Social Media Using Feature Fusion. In: Berihun, M.L. (eds) Advances of Science and Technology. ICAST 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 411. Springer, Cham. https://doi.org/10.1007/978-3-030-93709-6_31

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  • DOI: https://doi.org/10.1007/978-3-030-93709-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93708-9

  • Online ISBN: 978-3-030-93709-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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