Purpose
The online activity of users produces a huge volume of behavioural data, which can be processed to provide useful knowledge and applications that make digital spaces more convenient, safe and impactful. This involves a range of methods, mostly based on AI.
In the Web and User Data Processing group, we combat false information and online malicious behaviour. We strive to create an extensive infrastructure for collecting online content in large volumes. This content is then monitored by AI-based methods to detect signs of false information and online malicious behaviour. We also make use of information retrieval and recommendations, mostly for e-commerce applications. Our research delivers innovative methods that improve customer experience and business goals. These methods can also reveal connections in our partners’ data to improve their processes and offer personalized services to their customers.
RESEARCH AREAS OF INTEREST:
- Misinformation analysis and characterization
- Machine learning based detection and prediction methods
- Interpretation and explanation of machine learning models
- User behaviour modelling and segmentation
Team
Jakub Šimko
Lead and Researcher
Maria Bielikova
Lead and Researcher
Michal Kompan
Lead and Researcher
Andrea Hrčková
Researcher
Martin Hyben
Researcher
Dominik Macko
Researcher
Róbert Móro
Researcher
Pavol Návrat
Researcher
Ivan Srba
Researcher
Matúš Tibenský
Researcher
Jakub Fedorko
Research Engineer
Ján Jakubčík
Research Engineer
Jakub Kopál
Research Engineer
Ivan Novgorodtsev
Research Engineer
Tomáš Sako
Research Engineer
Róbert Belanec
PhD Student
Ján Čegiň
PhD Student
Matej Čief
PhD Student
Santiago de Leon
PhD Student MSCA Doctoral Network
Branislav Pecher
PhD Student
Richard Bača
Research Intern
Jozef Barut
Research Intern
Lívia Kostová
Research Intern
Matej Mosnár
Research Intern
Tomáš Nagy
Research Intern
Tomáš Remiš
Research Intern
Michal Spiegel
Research Intern
Adam Škurla
Research Intern
Aneta Žugecová
Research Intern
Former members
Ivan Kačala
Research Engineer 01/2024 – 08/2024
Sneha Pareek
Research Intern 05/2024 – 07/2024
Roman Matúš Labuš
Research Intern 07/2023 – 07/2024
Martin Melišek
Research Intern 03/2022-05/2024
Rastislav Papšo
PhD Student 09/2022-12/2023
Ivan Agarský
PhD Student 09/2022-10/2023
Timotej Smoleň
Research Intern 06/2022-04/2023
Timo Hromádka
Research Intern 07/2022-01/2023
Matúš Tomlein
Researcher 10/2020-04/2022
Elena Štefancová
Researcher 10/2020-08/2021
Research & Innovation Projects
Selected publications
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness
Pecher, B., Srba, I., Bielikova, M. Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-learning or few-shot learning, aims to effectively train a model using only a…
An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
The negative effects of misinformation filter bubbles in adaptive systems have been known to researchers for some time. Several studies investigated, most prominently on YouTube, how fast a user can…
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing? Traditionally, crowdsourcing has been used for acquiring solutions to a wide variety…
