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Social Media Influence

  •  A large-scale study of social media as infrastructures of influence in national populations with a focus on false information

Social Media Influence Project (1/09/2023 - 29/02/2028)
Funding: DKK 6.2 million by Independent Research Fund Denmark


Participants: Bechmann, Anja (PI), Holt, Anton Elias, Wegmann, David Josias, Walter, Jessica Gabriele, Brems, Miriam, Nielbo, Kristoffer Laigaard (CoPI), Baglini, Rebekah Brita (CoPI)


Increased use of social media comes at a societal price when influential actors, and the patterns of how and who they influence are hidden. When such patterns and structures are hidden, it prevents effective mitigation against their potentially harmful impact on trust and well-being in democratic societies. The project creates novel knowledge on this topic by studying social media as infrastructures for influence in national populations. The focus of the project on false information serves as a critical case for understanding influence, which becomes especially relevant in times of crisis and with social media's increasing role as an information source. The major contributions of the project are reconfiguring theoretical concepts for the analysis of social media influence, along with novel and innovative scalable methods and code to analyze influence and influential actors. In addition, the project contributes with empirical findings for a better understanding of information disorders by analyzing two main sources: 1) Facebook trace data from 28 European countries at a national scale, and 2) the YouTube watch-histories of 1,000 Danish citizens over 4.5 years. By analyzing the data in relation to citizens’ socio-demographic backgrounds, psychological profiles, measured and perceived influence, the project advances our understanding of social media influence.

Subprojects:



Associated publications

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Jacobsen, M., Bizzoni, Y., Moreira, P. F. & Nielbo, K. L. (2024). Patterns of Quality: Comparing Reader Reception Across Fanfiction and Commercially Published Literature. In 2024 Computational Humanities Research Conference, CHR 2024 (Vol. 3834, pp. 718-739)
Wu, Y., Bizzoni, Y., Moreira, P. F. & Nielbo, K. L. (2024). Perplexing Canon: A study on GPT-based perplexity for canonical and non-canonical literary works. In Y. Bizzoni, S. Degaetano-Ortlieb, A. Kazantseva & S. Szpakowicz (Eds.), Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024) (pp. 172–184). Association for Computational Linguistics. https://aclanthology.org/2024.latechclfl-1.16.pdf
Nielbo, K. L., Karsdorp, F., Wevers, M., Lassche, A., Baglini, R. B., Kestemont, M. & Tahmasebi, N. (2024). Quantitative text analysis. Nature Reviews Methods Primers, 4(25), Article 25. https://doi.org/10.1038/s43586-024-00302-w
Feldkamp, P., Overgaard, E. L., Nielbo, K. L. & Bizzoni, Y. (2024). Sentiment Below the Surface: Omissive and Evocative Strategies in Literature and Beyond. In Computaitonal Humanities Research 2024: Proceedings of the Computational Humanities Research Conference 2024 (Vol. 3834, pp. 681-706). Article 98 https://ceur-ws.org/Vol-3834/paper98.pdf

Associated activities


Associated media apperances