2024

 

Research article in proceedings (conference)

Bossek, J., & Grimme, C. (2024). Generalised Kruskal Mutation for the Multi-Objective Minimum Spanning Tree Problem. In Xiaodong, L., & Julia, H. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (pp. 133–141-133–141). GECCO '24. New York, NY, USA: Association for Computing Machinery.
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Stampe, L., Lütke-Stockdiek, J., Grimme, B., & Grimme, C. (2024). Benchmarking Sentence Embeddings in Textual Stream Clustering with Applications to Campaign Detection. In Proceedings of the IEEE World Congress on Computational Intelligence, Yokohama. (accepted / in press (not yet published))
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Research article (journal)

Bossek, J., & Grimme, C. (2024). On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. Evolutionary Computation, 32(2), 143–175.
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2023

 

Research article in proceedings (conference)

Grimme, B., Pohl, J., Winkelmann, H., Stampe, L., & Grimme, C. (2023). Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media. In Ceolin, D., Caselli, T., & Tulin, M. (Eds.), Disinformation in Open Online Media (pp. 72–87). Lecture Notes in Computer Science: Vol. 14397. Amsterdam, Niederlande: Springer.
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Klapproth, J., Unger, S., Pohl, J., Boberg, S., Grimme, C., & Quandt, T. (2023). Immunize the Public against Disinformation Campaigns: Developing a Framework for Analyzing the Macrosocial Effects of Prebunking Interventions. In Bui, T. X. (Ed.), Proceedings of the 56th Hawaii International Conference on System Sciences (HICSS) (pp. 2411–2420). Honolulu, HI, USA: ScholarSpace.
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Pohl, J. S., Markmann, S., Assenmacher, D., & Grimme, C. (2023). Invasion@Ukraine: Providing and describing a Twitter streaming dataset that captures the outbreak of war between Russia and Ukraine in 2022. In Lin, Y.-R., Cha, M., & Quercia, D. (Eds.), Proceedings of the Seventeenth International AAAI Conference on Web and Social Media (pp. 1093–1101). Palo Alto, CA, USA: AAAI Press.
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Schäpermeier, L., Kerschke, P., Grimme, C., & Trautmann, H. (2023). Peak-A-Boo! Generating Multi-Objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In Li, K., & Wang, H. (Eds.), Proceedings of the International Conference Series on Evolutionary Multi-Criterion Optimization (pp. 291–304). Lecture Notes in Computer Science: Vol. 13970. Cham: Springer.
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Stampe, L., Pohl, J., & Grimme, C. (2023). Towards Multimodal Campaign Detection: Including Image Information in Stream Clustering to Detect Social Media Campaigns. In Ceolin, D., Caselli, T., & Tulin, M. (Eds.), Disinformation in Open Online Media (pp. 144–159). Lecture Notes in Computer Science: Vol. 14397. Amsterdam: Springer.
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2022

 

Research article in proceedings (conference)

Grimme, C., Pohl, J., Cresci, S., Lüling, R., & Preuss, M. (2022). New Automation for Social Bots: From Trivial Behavior to AI-Powered Communication. In Spezzano, F., Amaral, A., Ceolin, D., Fazio, L., & Serra, E. (Eds.), Proceedings of the 4th Multidisciplinary International Symposium on Disinformation in Open Online Media (MISDOOM) (1st ed., pp. 79–99). Lecture Notes in Computer Science: Vol. 4. Cham, Switzerland: Springer Nature.
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Pohl, J. S., Assenmacher, D., Seiler, M. V., Trautmann, H., & Grimme, C. (2022). Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In Association, f. t. A. o. A. I. (. (Ed.), Workshop Proceedings of the 16th International Conference on Web and Social Media (ICWSM) (pp. 1–10). Palo Alto, CA, USA: AAAI Press.
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Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In Fieldsend, J., & Wagner, M. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 356–359-356–359). GECCO '22. New York, NY, USA: Association for Computing Machinery.
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Schäpermeier, L., Grimme, C., & Kerschke, P. (2022). MOLE: Digging Tunnels Through Multimodal Multi-Objective Landscapes. In Fieldsend, J. E. (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (pp. 592–600). New York, NY, USA: Association for Computing Machinery, Inc.
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Research article (journal)

Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P., Trautmann, H., & Grimme, C. (2022). The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. Natural Computing, 22(2), 271–285.
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Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(8), 1–44.
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Schäpermeier, L., Grimme, C., & Kerschke, P. (2022). Plotting Impossible? Surveying Visualization Methods for Continuous Multi-Objective Benchmark Problems. IEEE Transactions on Evolutionary Computation, 26(6), 1306–1320.
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Abstract in edited proceedings (conference)

Leszkiewicz, A., Bucur, D., Grimme, C., Michalski, R., Clever, L., Pohl, J. S., Rook, J., Bossek, J., Preuss, M., Squillero, G., Quer, S., Calabrese, A., Iacca, G., Kizgin, H., & Trautmann, H. (2022). Social Influence Analysis (SIA) in Online Social Networks.
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Forschungsartikel in Online-Sammlung

Pohl, J. S., Seiler, M. V., Assenmacher, D., & Grimme, C. (2022). A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022.
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