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Semi-supervised self-training for COVID-19 misinformation detection: analyzing Twitter data and alternative news media on Norwegian Twitter

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  • Siri Frisli

    (Oslo Metropolitan University)

Abstract

This paper investigates the dissemination of COVID-19 misinformation on Twitter within the context of the Norwegian media landscape, characterized by high levels of trust in the media, yet experiencing an increasing influence of alternative news sources. Using a semi-supervised self-training approach for text classification, a dataset of 426,262 tweets is analyzed, identifying approximately 5.11% as misinformation. The study reveals that misinformation tweets receive heightened engagement, particularly in retweets, and originate predominantly from a small group of users. Furthermore, while misinformation tweets are more likely to link to alternative news media sites, these sites represent only a minor fraction of the overall links shared. The analysis highlights distinct temporal patterns, with misinformation activity spiking during significant events such as the arrival of COVID-19 vaccines in Norway and the emergence of the Omicron variant. This research underscores the complexity of misinformation dynamics in a high-trust media environment and emphasizes the need for effective strategies to combat misinformation, particularly from alternative news media that challenge conventional narratives while often propagating falsehoods. Overall, the findings contribute valuable insights into the interplay between social media, alternative news sources, and misinformation dissemination during a global pandemic.

Suggested Citation

  • Siri Frisli, 2025. "Semi-supervised self-training for COVID-19 misinformation detection: analyzing Twitter data and alternative news media on Norwegian Twitter," Journal of Computational Social Science, Springer, vol. 8(2), pages 1-34, May.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-025-00367-x
    DOI: 10.1007/s42001-025-00367-x
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    References listed on IDEAS

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    1. Salman Bin Naeem & Maged N. Kamel Boulos, 2021. "COVID-19 Misinformation Online and Health Literacy: A Brief Overview," IJERPH, MDPI, vol. 18(15), pages 1-12, July.
    2. Cantay Caliskan & Alaz Kilicaslan, 2023. "Varieties of corona news: a cross-national study on the foundations of online misinformation production during the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 6(1), pages 191-243, April.
    3. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    4. Lisa Singh & Leticia Bode & Ceren Budak & Kornraphop Kawintiranon & Colton Padden & Emily Vraga, 2020. "Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams," Journal of Computational Social Science, Springer, vol. 3(2), pages 343-366, November.
    5. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    Full references (including those not matched with items on IDEAS)

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