Stoll, A. (2024). Machine Learning for the Automated Content Analysis of Incivility in Online Discussions [Doctoral dissertation, University of Düsseldorf, Germany]. ULB Düsseldorf. https://docserv.uni-duesseldorf.de/servlets/DerivateServlet/Derivate-70121/Stoll_MachineLearning_Incivility_Dissertation_2024%20.pdf

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Wilms, L., Gerl, K., Stoll, A., & Ziegele, M. (2024). Technology Acceptance and Transparency Demands for Automated Detection of Toxic Language – Interviews with Moderators of Public Online Discussion Fora. Human-Computer Interaction. https://doi.org/10.1080/07370024.2024.2307610

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Stoll, A. (2023). The Accuracy Trap or How to Build a Phony Classifier. In C. Strippel, S. Paasch-Colberg, M. Emmer, & J. Trebbe (Eds.), Challenges and perspectives of hate speech analysis (pp. 371-381). Digital Communication Research. https://doi.org/10.48541/dcr.v12.22

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Stoll, A., Wilms, L., & Ziegele, M. (2023). Developing an Incivility-Dictionary for German Online Discussions - A Semi-Automated Approach Combining Human and Artificial Knowledge. Communication Methods and Measures, 17(2), 131-149. https://doi.org/10.1080/19312458.2023.2166028

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Küchler, C., Stoll, A., Ziegele, M., & Naab, T. (2022). Gender-related Differences in Online Comment Sections: Findings from a Large-Scale Content Analysis of Commenting Behavior. Social Science Computer Review, 41(3), 728–747. https://journals.sagepub.com/doi/10.1177/08944393211052042

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Risch, J., Stoll, A., Wilms, L., & Wiegand, M. (2021). Overview of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments. In J. Risch, A. Stoll, L. Wilms, & M. Wiegand (Eds.), Proceedings ofthe GermEval 2021 SharedTask on the Identification of Toxic, Engaging, and Fact-Claiming Comments (pp. 1-12). Association for Computational Linguistics. https://aclanthology.org/2021.germeval-1.1

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Stoll, A. (2020). Supervised Machine Learning mit Nutzergenerierten Inhalten: Oversampling für nicht balancierte Trainingsdaten [Supervised Machine Learning with User-Generated Content: Oversampling for imbalanced training data.]. Publizistik, 65(2), 233-251. https://doi.org/10.1007/s11616-020-00573-9

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Stoll, A., Ziegele, M., & Quiring, O. (2020). Detecting Impoliteness and Incivility in Online Discussions: Classification Approaches for German User Comments. Computational Communication Research, 2(1), 109-134. https://doi.org/10.5117/CCR2020.1.005.KATH

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Risch, J., Stoll, A., Ziegele, M., & Krestel, R. (2019). hpiDEDIS at GermEval 2019: Offensive Language Identification using a German BERT model. In S. Evert (Ed.), Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019) (pp. 403-408). German Society for Computational Linguistics & Language Technology.

https://hpi.de/fileadmin/user_upload/fachgebiete/naumann/publications/PDFs/2019_risch_hpidedis.pdf

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Oberstrass, A., Romberg, J., Stoll, A., & Conrad, S. (2019). HHU at SemEval-2019 Task 6: Context does matter-tackling offensive language identification and categorization with ELMo. In May, J., Shutova, E., Herbelot, A., Zhu, X., Apidianaki, M. & Mohammad, S. M. (Eds.), Proceedings of the 13th international workshop on semantic evaluation (pp. 628-634). Association for Computational Linguistics. https://doi.org/10.18653/v1/S19-2112