{"department":[{"_id":"34"},{"_id":"819"}],"author":[{"full_name":"Pohl, Janina Susanne","first_name":"Janina Susanne","last_name":"Pohl"},{"first_name":"Dennis","last_name":"Assenmacher","full_name":"Assenmacher, Dennis"},{"first_name":"Moritz","last_name":"Seiler","id":"105520","full_name":"Seiler, Moritz"},{"full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","id":"100740","first_name":"Heike","last_name":"Trautmann"},{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"}],"date_updated":"2024-06-07T07:13:35Z","place":"Palo Alto, CA, USA","language":[{"iso":"eng"}],"date_created":"2023-08-04T07:11:34Z","publication":"Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)","doi":"10.36190/2022.91","status":"public","user_id":"15504","citation":{"chicago":"Pohl, Janina Susanne, Dennis Assenmacher, Moritz Seiler, Heike Trautmann, and Christian Grimme. “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.” In Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), edited by for the Advancement of Artificial Intelligence (AAAI) Association, 1–10. Palo Alto, CA, USA: AAAI Press, 2022. https://doi.org/10.36190/2022.91.","bibtex":"@inproceedings{Pohl_Assenmacher_Seiler_Trautmann_Grimme_2022, place={Palo Alto, CA, USA}, title={Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches}, DOI={10.36190/2022.91}, booktitle={Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)}, publisher={AAAI Press}, author={Pohl, Janina Susanne and Assenmacher, Dennis and Seiler, Moritz and Trautmann, Heike and Grimme, Christian}, editor={the Advancement of Artificial Intelligence (AAAI) Association, for}, year={2022}, pages={1–10} }","short":"J.S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, C. Grimme, in: for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), AAAI Press, Palo Alto, CA, USA, 2022, pp. 1–10.","ieee":"J. S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, and C. Grimme, “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches,” in Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), 2022, pp. 1–10, doi: 10.36190/2022.91.","mla":"Pohl, Janina Susanne, et al. “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.” Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), edited by for the Advancement of Artificial Intelligence (AAAI) Association, AAAI Press, 2022, pp. 1–10, doi:10.36190/2022.91.","apa":"Pohl, J. S., Assenmacher, D., Seiler, M., Trautmann, H., & Grimme, C. (2022). Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM) (pp. 1–10). AAAI Press. https://doi.org/10.36190/2022.91","ama":"Pohl JS, Assenmacher D, Seiler M, Trautmann H, Grimme C. Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In: the Advancement of Artificial Intelligence (AAAI) Association for, ed. Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM). AAAI Press; 2022:1–10. doi:10.36190/2022.91"},"year":"2022","_id":"46303","publisher":"AAAI Press","title":"Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches","abstract":[{"text":"Social media platforms are essential for information sharing and, thus, prone to coordinated dis- and misinformation campaigns. Nevertheless, research in this area is hampered by strict data sharing regulations imposed by the platforms, resulting in a lack of benchmark data. Previous work focused on circumventing these rules by either pseudonymizing the data or sharing fragments. In this work, we will address the benchmarking crisis by presenting a methodology that can be used to create artificial campaigns out of original campaign building blocks. We conduct a proof-of-concept study using the freely available generative language model GPT-Neo in this context and demonstrate that the campaign patterns can flexibly be adapted to an underlying social media stream and evade state-of-the-art campaign detection approaches based on stream clustering. Thus, we not only provide a framework for artificial benchmark generation but also demonstrate the possible adversarial nature of such benchmarks for challenging and advancing current campaign detection methods.","lang":"eng"}],"type":"conference","page":"1–10","editor":[{"full_name":"the Advancement of Artificial Intelligence (AAAI) Association, for","last_name":"the Advancement of Artificial Intelligence (AAAI) Association","first_name":"for"}]}