2023

 

Research article in proceedings (conference)

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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Prager, R., & Trautmann, H. (2023). Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In Correia, J., Smith, S., & Qaddoura, R. (Eds.), Applications of Evolutionary Computation (pp. 411–425). Cham: Springer Nature Switzerland.
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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)

Frischlich, L., Clever, L., Wulf, T., Wildschut, T., & Sedikides, C. (2023). Populists’ Use of Nostalgia: A Supervised Machine Learning Approach. International Journal of Communication (Int J Commun), 17(March), 2113–2137.
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Prager, R. P., & Trautmann, H. (2023). Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evolutionary Computation. (accepted / in press (not yet published))
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2022

 

Research article (book contribution)

Clever, L., Klapproth, J., & Frischlich, L. (2022). Automatisierte (Gegen-)Rede? Social Bots als digitales Sprachrohr ihrer Nutzer*innen. In Ernst, J., Trompeta, M., & Roth, H.-J. (Eds.), Gegenrede digital (pp. 11–26).
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Niemann, M., Assenmacher, D., Brunk, J., Riehle, D. M., Becker, J., & Trautmann, H. (2022). (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In Weitzel, G., & Mündges, S. (Eds.), Hate Speech — Definitionen, Ausprägungen, Lösungen (pp. 249–274). Wiesbaden: VS Verlag für Sozialwissenschaften.
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Research article in proceedings (conference)

Assenmacher, D., & Trautmann, H. (2022). Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In Tran, T. e. a. (Ed.), Intelligent Information and Database Systems (pp. 3–16). Cham: Springer International Publishing.
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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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Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In Rudolph, G., Kononova, A., Aguirre, H., Kerschke, P., Ochoa, G., & Tu{š}ar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 192–206). Cham: Springer International Publishing.
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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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Prager, R. P., Seiler, M. V., Trautmann, H., & Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 3–17). Cham: Springer International Publishing.
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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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Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H., & Kerschke, P. (2022). HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 575–589). Cham: Springer International Publishing.
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Seiler, M. V., Prager, R. P., Kerschke, P., & Trautmann, H. (2022). A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In -, (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (pp. 657–665). New York, NY, USA: ACM Press.
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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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Frischlich, L., Kuhfeldt, L., Schatto-Eckrodt, T., & Clever, L. (2022). Alternative counter-news use and fake news recall during the covid-19 crisis. Digital Journalism, 00(00), 1–23.
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Heins, J., Bossek, J., Pohl, J. S., Seiler, M. V., Trautmann, H., & Kerschke, P. (2022). A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection. Theoretical Computer Science (Theoret. Comput. Sci.), 940.
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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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Assenmacher, D., Niemann, M., Müller, K., Seiler, M. V., Riehle, D. M., & Trautmann, H. (2021). RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), Virtual Event, 1–14.
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Bossek, J., Neumann, A., & Neumann, F. (2021). Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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Bossek, J., Neumann, A., & Neumann, F. (2021). Exact Counting and Sampling of Optima for the Knapsack Problem. In Proceedings of the Learning and Intelligent Optimization, Athens, Greece. (accepted / in press (not yet published))
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Bossek, J., & Neumann, F. (2021). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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Bossek, J., & Sudholt, D. (2021). Do Additional Optima Speed Up Evolutionary Algorithms?. In Proceedings of the 16th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XVI), Dornbirn, Austria. (accepted / in press (not yet published))
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Bossek, J., & Wagner, M. (2021). Generating Instances with Performance Differences for More Than Just Two Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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Clever, L., Schatto-Eckrodt, T., Clever, N., & Frischlich, L. (2021). Extremism on the Second Glance: Automated Content Analysis of Covert Propaganda on Instagram. In Proceedings of the The 3rd Multidisciplinary International Symposium on Disinformation in Open Online Media, Oxford, United Kingdom.
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Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In Association, f. C. M. (Ed.), Proceedings of the 16th ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) (pp. 1–15). Dornbirn, Austria: ACM Press.
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Markmann, S., & Grimme, C. (2021). Is YouTube Still a Radicalizer? An Exploratory Study on Autoplay and Recommendation. In Proceedings of the Multidisciplinary International Symposium on Disinformation in Open Online Media (MISDOOM), Oxford, UK, 50–65.
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Neumann, A., Bossek, J., & Neumann, F. (2021). Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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Niemann, M., Müller, K., Kelm, C., Assenmacher, D., & Becker, J. (2021). The German Comment Landscape: A Structured Overview of the Opportunities for Participatory Discourse on News Websites. In Proceedings of the 3rd Multidisciplinary International Symposium on Disinformation in Open Online Media, Oxford, United Kingdom.
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Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '21), Lille, France. (accepted / in press (not yet published))
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Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation. In Proceedings of the 16th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XVI), Dornbirn, Austria. (accepted / in press (not yet published))
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Prager, R. P., Moritz, V. H., & Pascal, (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Orlando, Florida, USA.
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Schäpermeier, L., Grimme, C., & Kerschke, P. (2021). To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes. In Proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Shenzhen, China, 632–644.
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Abstract in Online-Sammlung (Konferenz)

Clever, L., Schatto-Eckrodt, T., Clever, N., & Frischlich, L. (2021). Extremist Propaganda on Instagram. Poster session presented at the 7th International Conference on Computational Social Science, Zürich, Schweiz.
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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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Bossek, J., Peng, P., Neumann, F., & Sudholt, D. (2021). Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica, 2021. (accepted / in press (not yet published))
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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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Rodrigues, A., Kerschke, P., de B., P. C. A., Trautmann, H., Wagner, C., Hellingrath, B., & Polpo, A. (2021). Estimation of component reliability from superposed renewal processes by means of latent variables. Computational Statistics, 2021.
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Terveer, I., & Diepenbrock, F.-R. (2021). Warum ein Teil der Schwimmbad-Aufgabe im NRW-Abitur 2019 so nicht hätte gestellt werden dürfen. Stochastik in der Schule (SiS), 41(3).
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