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: Scholar Space.
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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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Research article (journal)

Bossek, J., & Grimme, C. (2023). On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. Evolutionary Computation, 0, 1–35.
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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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2021

 

Research article in proceedings (conference)

Aspar, P., Kerschke, P., Steinhoff, V., Trautmann, H., & Grimme, C. (2021). Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization. In Ishibuchi, H. e. a. (Ed.), Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings (pp. 311–322). Heidelberg, Berlin: Springer.
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Research article (journal)

Assenmacher, D., Weber, D., Preuss, M., Calero, V. A., Bradshaw, A., Ross, B., Cresci, S., Trautmann, H., Neumann, F., & Grimme, C. (2021). Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. Social Science Computer Review, online first.
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Coombs, C., Stacey, P., Kawalek, P., Simeonova, B., Becker, J., Bergener, K., Carvalho, J. Á., Fantinato, M., Garmann-Johnsen, N. F., Grimme, C., Stein, A., & Trautmann, H. (2021). What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. International Journal of Information Management, 58.
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Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A., Wang, H., & Emmerich, M. (2021). Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. Computers & Operations Research, 136, 105489.
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