Search results
Results: 139
Number of items: 139
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Willcox, M., Rogers, R., & Koronska, K. (2026). From Fact Checking to Content Moderation: Inside a Newsroom Faced with Generative AI. In S. Papadopoulos, K. Bontcheva, V. Mezaris, & R. Rogers (Eds.), Countering Disinformation in the Era of Generative AI (pp. 49-65). Springer Nature. https://doi.org/10.1007/978-3-032-11782-3_3 -
Rogers, R. (2026). Online groundedness. In A. K. Madsen, & A. K. Munk (Eds.), Handbook of Digital and Computational Research Methods: in the social sciences and humanities (pp. 177–189). Edward Elgar Publishing. https://doi.org/10.4337/9781802208993.00019 -
Rogers, R., & Zhang, X. (2025, March 9). How does alignment affect classification? On LLM guardrail sensitivity [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14994330
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de Keulenaar, E., & Rogers, R. (2025). After Deplatforming: The Return of Trace Research for the Study of Content Moderation. In T. Venturini, A. Acker, J.-C. Plantin, & T. Walford (Eds.), The Sage Handbook of Data and Society (pp. 392-413). Sage. https://doi.org/10.4135/9781529674699.n22
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Rogers, R., & Righetti, N. (2025). Coordinated inauthentic behaviour on Facebook? A typology of manufactured attention. Platforms & Society, 2. https://doi.org/10.1177/29768624251369784 -
Rogers, R., & Zhang, X. (2025). A bias towards neutrality? How LLM guardrail sensitivity affects classification. Communication and Change, 1, Article 13. https://doi.org/10.1007/s44382-025-00013-0 -
Leidinger, A., & Rogers, R. (2024). How Are LLMs Mitigating Stereotyping Harms? Learning from Search Engine Studies. In S. Das, B. P. Green, K. Varshney, M. Ganapini, & A. Renda (Eds.), Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society: AIES-24 (pp. 839-854). AAAI Press. https://doi.org/10.1609/aies.v7i1.31684 -
Rogers, R., & Giorgi, G. (2024). What is a meme, technically speaking? Information, Communication & Society, 27(1), 73-91. https://doi.org/10.1080/1369118X.2023.2174790
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