About PJAIT Summer School
This intensive program at PJAIT (Warsaw) integrates data science and psychology to train future researchers in combating disinformation. Participants will engage in lectures and team projects using real data, guided by experts from Poland and abroad. The program culminates in a supervised data-analysis challenge and publication workshop. The school is co-located with AI Summit PJAIT 2026 (Sept 16), which participants will attend to connect theory with current AI practice. NAWA co-funds participation, and partial scholarships are available.
Key Dates:
- Application Opens: May 20, 2026
- Deadline (online): June 20, 2026, 23:59 CEST
- Notification of Acceptance: June 30, 2026
- School Dates: Sep 12–18, 2026 (AI Summit Sep 16)
Organizers:
A team at Polish-Japanese Academy of Information Technology (PJAIT), in partnership with University of Koblenz, Open University of Cyprus, NAWA and EUonAIR University Alliance.
Contacts
For inquiries:
Kinga Skorupska (kinga.skorupska@pjwstk.edu.pl).
Adam Wierzbicki (adam.wierzbicki@pjwstk.edu.pl)
Arkadiusz Modzelewski (arkadiusz.modzelewski@pja.edu.pl)
Topics: ICT & Psychology Concepts related to Disinformation Detection and Debunking
The International Summer School on Disinformation Detection and Debunking is a doctoral-level, research-oriented training event that integrates:
- computational methods for detecting, explaining, and mitigating disinformation,
- cognitive-psychology mechanisms that drive belief formation and persistence, and
- responsible, evidence-based debunking practice.
The curriculum is grounded in the “information disorder” framing (mis-, dis-, and mal-information) and in empirical findings that misinformation is often resistant to correction without well-designed interventions.
The school is delivered in English and organized as two tightly coupled blocks.
The ICT block covers: data science workflows; machine learning and deep learning for credibility assessment; NLP for stance, evidence, and persuasion signals; multimodal analysis (image/video + text); and human-centered interface design for analyst decision support.
The Psychology/Ethics block covers: cognitive psychology foundations, perception and attention, and FATE (fairness, accountability, transparency, and ethics) as they apply to disinformation-detection systems.
The central learning component is a supervised dataset challenge. Participants work in small interdisciplinary teams on curated, license-compliant datasets such as MIPD (Modzelewski et al., 2024), MALINT (Modzelewski et al, 2026) or ISOT Fake News (Ahmed et al, 2018), with at least one multilingual and one multigenre track, and with explicit attention to manipulation techniques and malicious intent in disinformation. Teams will submit reproducible pipelines and short research reports. The top projects (by leaderboard plus scientific review) will enter a post-school mentorship track with lecturers to develop publishable outputs
Educational rationale and best-practice orientation
Disinformation and misinformation research shows that “corrections” can fail unless they are designed with attention to cognition, narrative coherence, and trust; this motivates the school’s explicit coupling of detection with debunking and human factors. The programme also treats adversarial behavior as a first-class concern (e.g., manipulation techniques, robustness), reflecting current evaluation practice in shared tasks that stress robustness and adversarial examples in credibility-related domains.
Goals
- enable participants to design and evaluate disinformation-detection systems under realistic constraints (domain shift, persuasion techniques, and synthetic content);
- connect algorithmic signals to psychological mechanisms and debunking strategies;
- build durable international collaborations among PhD researchers and practitioners.
Supervised Dataset Challenge
The supervised dataset challenge will begin with an introduction to key datasets that will serve as the foundation for participants’ preliminary research projects. These will include datasets developed by the program lecturers, such as MIPD (Modzelewski et al., 2024) and MALINT (Modzelewski et al., 2026), as well as widely used and highly cited public resources in natural language processing, such as the ISOT Fake News dataset (Ahmed et al., 2018). The MIPD and MALINT datasets have been manually annotated by experienced disinformation researchers and fact-checking experts with at least 3–5 years of professional experience, ensuring high-quality and reliable labels.
Following the introductory session, participants will be divided into teams to identify and refine research problems that can be explored during the summer school. This phase will include guided brainstorming sessions with lecturers, as well as a review of relevant academic literature.
Each team will then conduct a preliminary research project based on the selected datasets. The outcomes of this work will include a structured presentation of the research problem, methodology, and initial findings, as well as an outline of a potential research paper.
At the end of the summer school, teams will present their work, and both the presentations and research proposals will be evaluated by the lecturers.
- Arkadiusz Modzelewski, Giovanni Da San Martino, Pavel Savov, Magdalena Anna Wilczyńska, and Adam Wierzbicki. 2024. MIPD: Exploring Manipulation and Intention In a Novel Corpus of Polish Disinformation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 19769–19785, Miami, Florida, USA. Association for Computational Linguistics.
- Arkadiusz Modzelewski, Witold Sosnowski, Eleni Papadopulos, Elisa Sartori, Tiziano Labruna, Giovanni Da San Martino, and Adam Wierzbicki. 2026. MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3125–3148, Rabat, Morocco. Association for Computational Linguistics.
- Ahmed, H., Traore, I., & Saad, S. (2018). Detecting opinion spams and fake news using text classification. Security and Privacy, 1(1), e9.
Accepted lecturers
Arkadiusz Modzelewski is a final-year PhD candidate pursuing a joint doctorate under a cotutelle agreement at the University of Padua and the Polish-Japanese Academy of Information Technology. He is the author and co-author of multiple papers accepted to top-tier AI and NLP conferences, including ACL, EMNLP, and EACL, where he also serves as a reviewer. His research focuses on applying advanced NLP techniques and large language models to the detection of disinformation and propaganda. He conducts his doctoral research under the supervision of Prof. Adam Wierzbicki and Prof. Giovanni Da San Martino. Arkadiusz is affiliated with NASK – National Research Institute in Poland, one of the country’s leading institutions in AI safety and cybersecurity, where he works as a Senior NLP Specialist and Researcher. Prior to his PhD, he gained industry experience as a Data Scientist and completed a Master’s degree in Economics, specializing in Econometrics and Statistics, at the University of Bonn, ranked 35th globally in Economics (ShanghaiRanking by Subject, 2018) at the time of his studies.
Marina Ernst is a researcher and PhD candidate at University of Koblenz. Her research focuses on LLM-based disinformation detection, and subsequently exploring human–machine interaction in the context of AI-assisted misinformation detection and reliability assessment. Alongside research, she is teaching and supervising Master’s students in conducting research, leading to joint academic publications in the field of misinformation detection. Prior to her research position, she obtained an MSc in Web Science and gained five years of industry experience.
Styliani Kleanthous is an Assistant Professor in the field of Human–AI Interaction at the Open University of Cyprus, where she co-directs the Cyprus Center for Trustworthy AI (CyCAT). Styliani holds a PhD in Computer Science from the University of Leeds, United Kingdom. Her main areas of expertise and research interests lie in Human–Artificial Intelligence Interaction, focusing on how humans and AI systems collaborate to achieve optimal outcomes. Styliani adopts user modeling approaches to understand and model collaboration and interaction between humans and artificial intelligence across different scenarios. More specifically, she leverages psychological and social theories to model and understand Human–AI Interaction, based on the view that artificial intelligence should empower people in everyday decision-making by providing personalized support. Within this context, she also leads the development of interactive tools for data collection during user interaction and for raising awareness (and providing education) about the capabilities and limitations of AI technologies. She is the coordinator of the project AI4Everyone – Bridging the Gap Between Smart Technologies and Society and has published more than 60 research papers, contributing to the scientific community through multiple roles. She has organized at least 10 Workshops and 2 seasonal schools in the past years.
Editor-in-chief of FakeNews.pl and vice-president of the Foundation „Counteracting Disinformation” (Fundacja „Przeciwdziałamy Dezinformacji”), an IFCN Code of Principles signatory, responsible for the organisation’s editorial and fact-checking activities. He is a fact-checker, analyst and trainer. Author of nearly 170 fact-checking articles and numerous analytical and research publications. In 2022, he coordinated a group of eight volunteers during research for the report “The anti-vaccine movement and pro-Russian propaganda on social media” published on the FakeNews.pl website. Co-author of analytical reports for FakeNews.pl, such as “The ‘Bronimy Munduru’ Association – a network of connections and analysis of its activities” and “Anti-establishment movements and HPV vaccinations – an analysis of disinformation campaigns”. In 2023, commissioned by EU DisinfoLab, he produced the report “The Disinformation Landscape in Poland”, which formed part of a project compiling expert summaries of the disinformation situation in 20 European countries. In 2025, an updated version of the publication was released, also authored by Mateusz Zadroga. It received a positive review from NASK experts before publication.
As part of the Infotester project (2021–2022) carried out by the Polish-Japanese Academy of Information Technology, he served as a member of the expert team investigating disinformation websites, identifying false narratives. Since April 2024, he has been coordinating four international teams of students at PJAIT, totalling around 40 people, investigating disinformation on websites as part of the Infotester4Education project. He is the lead expert and consultant for disinformation countermeasure projects at PJAIT.
