Jerry Spanakis (G.)

My research currently focuses on the area of Social Machine Learning: How to apply Data Mining and Machine Learning techniques in order to assist solving social issues in different areas of our life.

I am currently working on three themes and diverse projects:

1)Machine learning for the people:

Machine learning has brought a revolution to our lives and has more and more applications in different domains. The goal is to build responsible and interpretable models that solve current societal issues and automate/improve current processes.

2)Computational Social Media:

Internet and Social Media have not only changed the way many people communicate but also have disrupted the way that consumers, businesses and regulators should think and work. In this context, I am interested into harnessing the power of social media data to explore modern online harms (e.g. misleading advertising, content moderation, complaints, reviews) and understand how people react to and how they use these new technologies.

3)Structuring the Unstructured:

Collecting (e.g. by scrapping) and aggregating (e.g. from different sources) information (mostly unstructured: text, images, measurements etc.) from the Web (or other systems)  is a promising direction with many applications:

• Text understanding: Efficiently organizing large document collections (e.g. in the legal domain or short text from social media) by taking into account their underlying semantic information and tracking their evolution over time.
• Domain-specific applications: Lots of domains are in need of exploring the underlying structures of data that are generated. That includes the higher education area (learning analytics, exploring embedding techniques for automating assessment etc.) and the air traffic management system (analyzing the structure of airline/airport networks, predicting links and bottlenecks in the complex aviation system etc.)

Current projects I am being involved:

• VOXReality (HORIZON-CL4-2021-HUMAN-01 Research and Innovation Project) Voice driven interactions in eXtended Reality spaces
• RegTech4AI
• Digital Legal Studies: Building NLP systems to automate legal processes
• Advertising and influencer marketing (HumanAds)
• NWO Gravitation Grant: New Science of Mental Disorders
• Beating the Binge
• Data Markets on the Dark Web
• Dark Patterns and Unsafe products/Complaints on the Internet

Previous projects

• EIT/EIT Health Initiative: Combating Childhood Obesity
• #MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
• INTERREG/IMPACT: Artificial Intelligence and Arts: Infinity Games
• NWO/Phillips: “Train your brain, ThinkSlim”

• Louis, A., van Dijck, G., & Spanakis, G. (2024). Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models. In J. Dy, S. Natarajan, & M. Wooldridge (Eds.), Proceedings of the 38th AAAI Conference on Artificial Intelligence (20 ed., Vol. 38, pp. 22266-22275). AAAI Press. https://doi.org/10.1609/aaai.v38i20.30232

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• Issam, A., Semerci, Y. C., Scholtes, J., & Spanakis, G. (2024). Fixed and Adaptive Simultaneous Machine Translation Strategies Using Adapters. In E. Salesky, M. Federico, & M. Carpuat (Eds.), IWSLT 2024 - 21st International Conference on Spoken Language Translation, Proceedings of the Conference (pp. 346-358). Association for Computational Linguistics (ACL). https://aclanthology.org/2024.iwslt-1.36/

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• Maka, P., Semerci, Y. C., Scholtes, J., & Spanakis, G. (2024). Sequence Shortening for Context-Aware Machine Translation. In Y. Graham, M. Purver, & M. Purver (Eds.), EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2024 (pp. 1874-1894). Association for Computational Linguistics (ACL).

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• Erker, J. J., Mai, F., Reimers, N., Spanakis, G., & Gurevych, I. (2024). Triple-Encoders: Representations That Fire Together, Wire Together. In L.-W. Ku, A. F. T. Martins, & V. Srikumar (Eds.), 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 - Proceedings of the Conference (Vol. 1, pp. 5317-5332). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2024.acl-long.290

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• Fuchs, M., Jadhav, A., Jaishankar, A., Cauffman, C., & Spanakis, G. (2023). "What's Wrong with This Product?": Detection of Product Safety Issues Based on Information Consumers Share Online. In Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law (ICAIL '23) (pp. 397–401). Association for Computing Machinery. https://doi.org/10.1145/3594536.3595145

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• Goanta, C., Aletras, N., Chalkidis, I., Ranchordas, S., & Spanakis, G. (2023). Regulation and NLP (RegNLP): Taming Large Language Models. In H. Bouamor, J. Pino, & K. Bali (Eds.), EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 8712-8724). Association for Computational Linguistics (ACL). https://aclanthology.org/2023.emnlp-main.539/

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• Spanakis, G. (2024). Machine Learning for Consumer Forensics (or We Need to Talk About Online Enforcement of Regulations). Journal of European Consumer and Market Law, 193-195. http://www.kluwerlawonline.com/api/Product/CitationPDFURL?file=Journals\EuCML\EuCML2024026.pdf

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• Gui, H., Bertaglia, T., Goanta, C., de Vries, S., & Spanakis, G. (2024). Across Platforms and Languages: Dutch Influencers and Legal Disclosures on Instagram, YouTube and TikTok. https://doi.org/10.48550/arXiv.2407.12451

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• Ntekouli, M., Spanakis, G., Waldorp, L., & Roefs, A. (2023). Evaluating multivariate time-series clustering using simulated ecological momentary assessment data. Machine Learning with Applications, 14, Article 100512. https://doi.org/10.1016/j.mlwa.2023.100512

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For a complete list see:

https://jerryspan.github.io/publications/