Disinformation Detection & Debunking

The International Summer School on Disinformation Detection and Debunking is a doctoral-level, research-oriented training program that combines:

1. computational methods for detecting, explaining, and mitigating disinformation,
2. cognitive-psychological mechanisms that drive the formation and persistence of beliefs, and 
3. responsible, evidence-based debunking practices. 

The curriculum is based on the “information disorder” framework (misinformation, disinformation, and malinformation) and on empirical findings that misinformation is often resistant to correction without well-designed interventions.

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: the foundations of cognitive psychology, 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 (based on leaderboard rankings and scientific review) will enter a post-school mentorship track with lecturers to develop publishable outputs

Research on disinformation and misinformation shows that “corrections” can fail unless they are designed with careful consideration of cognition, narrative coherence, and trust; this is why the school explicitly combines detection with debunking and human factors. The program also treats adversarial behavior as a primary concern (e.g., manipulation techniques, robustness), reflecting current evaluation practices in shared tasks that emphasize robustness and adversarial examples in credibility-related domains.

Goals

1. enable participants to design and evaluate disinformation-detection systems under realistic constraints (domain shift, persuasion techniques, and synthetic content);
2. connecting algorithmic signals to psychological mechanisms and debunking strategies; 
3. build lasting international collaborations among PhD researchers and practitioners.