57 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Brno University of TechnologyCZ13 papers
- Slovak University of Technology in BratislavaSK6 papers
- Adobe Systems (United States)US3 papers
- Comenius University BratislavaSK3 papers
- Amazon (Germany)DE2 papers
- University of SheffieldGB2 papers
- Agence France-PresseFR1 paper
- Amazon (United Kingdom)GB1 paper
- Amazon (United States)US1 paper
- Amsterdam University of the ArtsNL1 paper
- Central European UniversityAT1 paper
- Fondazione Bruno KesslerIT1 paper
31 papers
The Brittle Compass: Navigating LLM Prompt Sensitivity in Slovak Migration Media Discourse
Samuel Harvan, Jaroslav Kopčan, Marek Šuppa +1
In this work, we present a case study that explores various tasks centered around the topic of migration in Slovak, a low-resource language, such as topic relevance and geographica…
Algorithmic Audit of Personalisation Drift in Polarising Topics on TikTok
Branislav Pecher, Adrian Bindas, Jan Jakubcik +8
Social media platforms have become an integral part of everyday life, serving as a primary source of news and information for many users. These platforms increasingly rely on perso…
The DSA's Blind Spot: Algorithmic Audit of Advertising and Minor Profiling on TikTok
Sara Solarova, Matej Mosnar, Matus Tibensky +6
Adolescents spend an increasing amount of their time in digital environments where their still-developing cognitive capacities leave them unable to recognize or resist commercial p…
From Latent to Observable Position-Based Click Models in Carousel Interfaces
Santiago de Leon-Martinez, Robert Moro, Branislav Kveton +1
Click models are a central component of learning and evaluation in recommender systems, yet most existing models are designed for single ranked list interfaces. In contrast, modern…
Beyond the Checkbox: Strengthening DSA Compliance Through Social Media Algorithmic Auditing
Sara Solarova, Matúš Mesarčík, Branislav Pecher +1
Algorithms of online platforms are required under the Digital Services Act (DSA) to comply with specific obligations concerning algorithmic transparency, user protection and privac…
Clustering Malware at Scale: A First Full-Benchmark Study
Martin Mocko, Jakub Ševcech, Daniela Chudá
Recent years have shown that malware attacks still happen with high frequency. Malware experts seek to categorize and classify incoming samples to confirm their trustworthiness or…
Political Leaning and Politicalness Classification of Texts
Matous Volf, Jakub Simko
This paper addresses the challenge of automatically classifying text according to political leaning and politicalness using transformer models. We compose a comprehensive overview…
Riding the Carousel: The First Extensive Eye Tracking Analysis of Browsing Behavior in Carousel Recommenders
Santiago de Leon-Martinez, Robert Moro, Branislav Kveton +1
Carousels have become the de-facto standard user interface in online services. However, there is a lack of research in carousels, particularly examining how recommender systems may…
Eye Movements as Indicators of Deception: A Machine Learning Approach
Valentin Foucher, Santiago de Leon-Martinez, Robert Moro
Gaze may enhance the robustness of lie detectors but remains under-studied. This study evaluated the efficacy of AI models (using fixations, saccades, blinks, and pupil size) for d…
RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces
Santiago de Leon-Martinez, Jingwei Kang, Robert Moro +4
Carousel interfaces are widely used in e-commerce and streaming services, but little research has been devoted to them. Previous studies of interfaces for presenting search and rec…
Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings
Matej Mosnar, Adam Skurla, Branislav Pecher +5
Social media platforms are constantly shifting towards algorithmically curated content based on implicit or explicit user feedback. Regulators, as well as researchers, are calling…
Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation
Dominik Macko, Aashish Anantha Ramakrishnan, Jason Samuel Lucas +4
Increased sophistication of large language models (LLMs) and the consequent quality of generated multilingual text raises concerns about potential disinformation misuse. While huma…
Context-aware adaptive personalised recommendation: a meta-hybrid
Peter Tibensky, Michal Kompan
Recommenders take place on a wide scale of e-commerce systems, reducing the problem of information overload. The most common approach is to choose a recommender used by the system…
A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models
Ivan Srba, Olesya Razuvayevskaya, João A. Leite +10
In the age of social media and generative AI, the ability to automatically assess the credibility of online content has become increasingly critical, complementing traditional appr…
Task Prompt Vectors: Effective Initialization through Multi-Task Soft-Prompt Transfer
Robert Belanec, Simon Ostermann, Ivan Srba +1
Prompt tuning is an efficient solution for training large language models (LLMs). However, current soft-prompt-based methods often sacrifice multi-task modularity, requiring the tr…
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness
Branislav Pecher, Ivan Srba, Maria Bielikova
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 small a…
Expert enhanced dynamic time warping based anomaly detection
Matej Kloska, Gabriela Grmanova, Viera Rozinajova
Dynamic time warping (DTW) is a well-known algorithm for time series elastic dissimilarity measure. Its ability to deal with non-linear time distortions makes it helpful in variety…
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification
Olesya Razuvayevskaya, Ben Wu, Joao A. Leite +5
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrate…
Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis
Santiago de Leon-Martinez, Robert Moro, Maria Bielikova
Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking d…
Learning Action Embeddings for Off-Policy Evaluation
Matej Cief, Jacek Golebiowski, Philipp Schmidt +2
Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy. OPE is a viable alternative to run…
KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection
Timo Hromadka, Timotej Smolen, Tomas Remis +2
This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of t…
Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems
Andrea Hrckova, Robert Moro, Ivan Srba +2
To mitigate the negative effects of false information more effectively, the development of Artificial Intelligence (AI) systems to assist fact-checkers is needed. Nevertheless, the…
Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles
Ivan Srba, Robert Moro, Matus Tomlein +8
In this paper, we present results of an auditing study performed over YouTube aimed at investigating how fast a user can get into a misinformation filter bubble, but also what it t…
Pessimistic Off-Policy Optimization for Learning to Rank
Matej Cief, Branislav Kveton, Michal Kompan
Off-policy learning is a framework for optimizing policies without deploying them, using data collected by another policy. In recommender systems, this is especially challenging du…