57 citations · 214 across the 21 of their papers we have counts for
21 papers
MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust Check-Worthiness Detection Models
Martin Hyben, Sebastian Kula, Jan Cegin +3
Large Language Models (LLMs) are beginning to reshape how media professionals verify information, yet automated support for detecting check-worthy claims a key step in the fact-che…
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…
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…
Increasing the Robustness of the Fine-tuned Multilingual Machine-Generated Text Detectors
Dominik Macko, Robert Moro, Ivan Srba
Since the proliferation of LLMs, there have been concerns about their misuse for harmful content creation and spreading. Recent studies justify such fears, providing evidence of LL…
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…
Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation
Aneta Zugecova, Dominik Macko, Ivan Srba +4
The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts raises many concerns regarding their m…
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…
MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts
Dominik Macko, Jakub Kopal, Robert Moro +1
Recent LLMs are able to generate high-quality multilingual texts, indistinguishable for humans from authentic human-written ones. Research in machine-generated text detection is ho…
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
Dominik Macko, Robert Moro, Adaku Uchendu +7
High-quality text generation capability of recent Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-ge…
Disinformation Capabilities of Large Language Models
Ivan Vykopal, Matúš Pikuliak, Ivan Srba +3
Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with d…
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts
Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko +5
In the realm of text manipulation and linguistic transformation, the question of authorship has been a subject of fascination and philosophical inquiry. Much like the Ship of These…
Multilingual and Multi-topical Benchmark of Fine-tuned Language models and Large Language Models for Check-Worthy Claim Detection
Martin Hyben, Sebastian Kula, Ivan Srba +2
This study compares the performance of (1) fine-tuned language models and (2) large language models on the task of check-worthy claim detection. For the purpose of the comparison w…
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…
Multilingual Previously Fact-Checked Claim Retrieval
Matúš Pikuliak, Ivan Srba, Robert Moro +7
Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the c…
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…
Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims
Ivan Srba, Branislav Pecher, Matus Tomlein +4
False information has a significant negative influence on individuals as well as on the whole society. Especially in the current COVID-19 era, we witness an unprecedented growth of…
An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
Matus Tomlein, Branislav Pecher, Jakub Simko +7
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, h…
A Study of Fake News Reading and Annotating in Social Media Context
Jakub Simko, Patrik Racsko, Matus Tomlein +3
The online spreading of fake news is a major issue threatening entire societies. Much of this spreading is enabled by new media formats, namely social networks and online media sit…