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Cognitive Systems

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  9. Seminar Cognitive Systems

Bachelor-/Master-Seminar (SS 2026)

General Information

  • This seminar is open for bachelor- and master students (BA AI, MA AI, CitH).
  • You find administrative information at UnivIS.
  • Participants should sign up for the course in the virtual campus.
  • The course is usually offered in the winter term.
  • Course language is Geman by default.

Topic: AI and Disinformation

Techniques for Automated Detection of Manipulative Texts

In the current times of rising extremist views on social media, in news outlets and political speeches, disinformation plays an integral role. To tackle this issue, in recent years different approaches have been proposed to automatically detect disinformation on a textual basis, as well as for combinations of different media types and even complete disinformation campaigns. In this seminar we want to focus on approaches to identify manipulative texts, using neurosymbolic AI methods which combine the advantages of Artificial Neural Networks and Classical Approaches. 

Recommended Reading / Links / Topics

Relevant Topics include Fact Checking, Claim Verification, Topic and Argument Mining, Neural Inductive Logic Programming and Explainable AI.

Review papers

  • Das, A., Liu, H., Kovatchev, V., & Lease, M. (2023). The state of human-centered NLP technology for fact-checking. Information Processing & Management, 60(2), 103219. https://doi.org/10.1016/j.ipm.2022.103219
  • Guo, Z., Schlichtkrull, M., & Vlachos, A. (2022). A survey on automated fact-checking. Transactions of the Association for Computational Linguistics, 10, 178–206. https://doi.org/10.1162/tacl_a_00454
  • Haider Rizvi, S. M., Imran, R., & Mahmood, A. (2025). Text Classification Using Graph Convolutional Networks: A Comprehensive Survey. ACM Comput. Surv., 57(8), 201:1-201:38. https://doi.org/10.1145/3714456
  • Hamilton, K., Nayak, A., Božić, B., & Longo, L. (2024). Is neuro-symbolic AI meeting its promises in natural language processing? A structured review. Semantic Web, 15(4), 1265–1306. https://doi.org/10.3233/SW-223228
  • Panchendrarajan, R., & Zubiaga, A. (2024). Claim detection for automated fact-checking: A survey on monolingual, multilingual and cross-lingual research. Natural Language Processing Journal, 7, 100066. https://doi.org/10.1016/j.nlp.2024.100066
  • Zeng, X., Abumansour, A. S., & Zubiaga, A. (2021). Automated fact‐checking: A survey. Language and Linguistics Compass, 15(10). https://doi.org/10.1111/lnc3.12438

Specific Approaches to Fact Checking / Disinformation Detection

  • Ashraf, S., Bezzaoui, I., Andone, I., Markowetz, A., Fegert, J., & Flek, L. (2024). DeFaktS: A German Dataset for Fine-Grained Disinformation Detection through Social Media Framing. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 4580–4591). ELRA and ICCL. https://aclanthology.org/2024.lrec-main.409/
  • Choi, E.C., & Ferrara, E. (2024). FACT-GPT: Fact-checking augmentation via claim matching with LLMs. Companion Proceedings of the ACM Web Conference 2024, 883–886. https://doi.org/10.1145/3589335.3651504
  • Hassan, N., Zhang, G., Arslan, F., Caraballo, J., Jimenez, D., Gawsane, S., Hasan, S., Joseph, M., Kulkarni, A., Nayak, A. K., Sable, V., Li, C., & Tremayne, M. (2017). ClaimBuster: The first-ever end-to-end fact-checking system. Proc. VLDB Endow., 10(12), 1945–1948. https://doi.org/10.14778/3137765.3137815
  • Liu, H., Wang, W., Li, H., & Li, H. (2024). TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection. Findings of the Association for Computational Linguistics ACL 2024, 15556–15583. https://doi.org/10.18653/v1/2024.findings-acl.919
  • Menzner, T., & Leidner, J. L. (2025). BiasScanner: Automatic News Bias Classification for Strengthening Democracy. In C. Hauff, C. Macdonald, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, & N. Tonellotto (Eds.), Advances in Information Retrieval (pp. 105–110). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-88720-8_18
  • Monnier, A., & Ségur, C. (2025). Challenges for fact-checking: Beyond false/true verification. InMedia, 10. https://doi.org/10.4000/145d4
  • Solopova, V., Popescu, O.-I., Benzmüller, C., & Landgraf, T. (2023). Automated Multilingual Detection of Pro-Kremlin Propaganda in Newspapers and Telegram Posts. Datenbank-Spektrum, 23(1), 5–14. https://doi.org/10.1007/s13222-023-00437-2

Neuro-Symbolic and Explainable Architectures for Text Classification

  • Bougzime, O., Jabbar, S., Cruz, C., & Demoly, F. (2025). Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitations (arXiv:2502.11269). arXiv. https://doi.org/10.48550/arXiv.2502.11269
  • Kiefer, S. (2022). CaSE: Explaining text classifications by fusion of local surrogate explanation models with contextual and semantic knowledge. Information Fusion, 77, 184–195. https://doi.org/10.1016/j.inffus.2021.07.014
  • Manhaeve, R., Dumančić, S., Kimmig, A., Demeester, T., & De Raedt, L. (2021). Neural probabilistic logic programming in DeepProbLog. Artificial Intelligence, 298, 103504. https://doi.org/10.1016/j.artint.2021.103504
  • Muggleton, S. H., Schmid, U., Zeller, C., Tamaddoni-Nezhad, A., & Besold, T. (2018). Ultra-Strong Machine Learning: Comprehensibility of programs learned with ILP. Machine Learning, 107(7), 1119–1140. https://doi.org/10.1007/s10994-018-5707-3
  • Schmitt, V., Villa-Arenas, L.-F., Feldhus, N., Meyer, J., Spang, R. P., & Möller, S. (2024). The Role of Explainability in Collaborative Human-AI Disinformation Detection. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’24, 2157–2174. https://doi.org/10.1145/3630106.3659031

Previous Seminars

Bachelor/Master-Seminar: [WS25/26]

Bachelor/Master-Seminar: Explainable Artificial Intelligence: [WS 19/20] [WS 20]

KI-Seminare (KI gestern, heute, morgen):  [WS 18/19] [WS 17/18] [WS 16/17] [WS 15/16]

Bachelor Seminare: [WS 04/05] [WS 05/06] [WS 06/07] [WS 07/08] [WS 08/09] [WS 09/10]  [WS 10/11]    [SS 11] [WS 11/12] [WS 12/13] [WS13/14]

Master Seminare: [SS 05] [SS 06] [SS 08] [SS 09] [WS 09/10] [SS 10]  [WS 11/12] [WS 12/13] [WS 13/14] [SS20] [SS21] [SS22] [SS23] [SS24] [SS25]

Reading Clubs:

  • WS 14/15: Cognitive Models for Number Series Induction Problems  [Archiv Page]
  • SS 2014: Experimenting with a Humanoid Robot - Programming NAO to (Inter-)Act  [Archiv Page]
  • SS 2013: An introduction into statistic data analysis with R  [Archiv Page]
  • SS 2012: Transfer Learning  [Archiv Page]
  • SS 2011: Emotion Mining in Images and Text  [Archiv Page]
  • SS 2010: Aspects of Cognitive Robotics [Archiv Page]
  • SS 2009: Reading Club Decision Support Systems [Archiv Page]
  • WS 08/09: Algebraic Foundations of Functional Programming (together with Theoretical Computer Science) [Archiv Page]
  • SS 2008: Similarity (together with Statistics) [Archiv Page]
  • SS 2007: Automated Theorem Proving with Isabelle (together with Theoretical Computer Science) [Archiv Page]
  • SS 2006: Support Vector Machines [Archiv Page]