[HTML][HTML] Researching the links between smartphone behavior and adolescent well-being with the FUTURE-WP4 (modeling the future: understanding the impact of …

S Elavsky, J Blahošová, M Lebedíková… - JMIR Research …, 2022 - researchprotocols.org
Background Smartphone ownership has increased among teens within the last decade, with
up to 89% of adolescents owning a smartphone and engaging daily with the online world …

Classification of adolescents' risky behavior in instant messaging conversations

J Plhák, O Sotolář, M Lebedı́ková… - International …, 2023 - proceedings.mlr.press
Previous research on detecting risky online behavior has been rather scattered, typically
identifying single risks in online samples. To our knowledge, the presented research is the …

Leveraging Conceptual Similarities to Enhance Modeling of Factors Affecting Adolescents' Well-Being

O Sotolář, J Plhák, D Šmahel - … Conference on Text, Speech, and Dialogue, 2024 - Springer
While large language models consistently outperform their smaller transformer-based
counterparts, there are constraints on their deployment. Model size becomes a critical …

[PDF][PDF] Constructing datasets from dialogue data

O Sotolář, J Plhák, M Lebedíková, M Tkaczyk… - RASLAN 2022 Recent …, 2022 - nlp.fi.muni.cz
We present methods for transforming raw dialogue data into a dataset suitable for
processing with statistical NLP models. We reveal the potential pitfalls for processing this …

[PDF][PDF] Predicting the mood of smartphone users

BCM FEKETE - is.muni.cz
Smartphones have become an inseparable part of our lives thanks to their convenience,
ease of use, and constant availability. While several studies highlighted that smartphone use …

[PDF][PDF] Measuring Moral Outrage on Twitter

P RUSNOK - is.muni.cz
Through annotators and AI, I was measuring moral outrage on social media X (formerly,
Twitter). I then assessed attitude, expressed arousal and amount of likes to explore whether …

[PDF][PDF] Constructing Datasets from Dialogue Data

D Šmahel - is.muni.cz
We present methods for transforming raw dialogue data into a dataset suitable for
processing with statistical NLP models. We reveal the potential pitfalls for processing this …