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@inproceedings{2273397,
author = {Plhák, Jaromír and Sotolář, Ondřej and Lebedíková, Michaela and Šmahel, David},
address = {https://proceedings.mlr.press},
booktitle = {26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023},
editor = {Ruiz F., Dy J., van de Meent J-W.},
keywords = {adolescents; smartphones; machine learning; risky behavior; instant messaging},
howpublished = {elektronická verze "online"},
language = {eng},
location = {https://proceedings.mlr.press},
pages = {2390-2404},
publisher = {ML Research Press},
title = {Classification of Adolescents' Risky Behavior in Instant Messaging Conversations},
url = {https://proceedings.mlr.press/v206/plhak23a/plhak23a.pdf},
year = {2023}
}
TY - JOUR
ID - 2273397
AU - Plhák, Jaromír - Sotolář, Ondřej - Lebedíková, Michaela - Šmahel, David
PY - 2023
TI - Classification of Adolescents' Risky Behavior in Instant Messaging Conversations
PB - ML Research Press
CY - https://proceedings.mlr.press
KW - adolescents
KW - smartphones
KW - machine learning
KW - risky behavior
KW - instant messaging
UR - https://proceedings.mlr.press/v206/plhak23a/plhak23a.pdf
N2 - 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 first that presents a process of building models that can efficiently detect the following four online risky behavior: (1) aggression, harassment, hate; (2) mental health; (3) use of alcohol, and drugs; and (4) sexting. Furthermore, the corpora in this research are unique because of the usage of private instant messaging conversations in the Czech language provided by adolescents. The combination of publicly unavailable and unique data with high-quality annotations of specific psychological phenomena allowed us for precise detection using transformer machine learning models that can handle sequential data and involve the context of utterances. The impact of the context length and text augmentation on model efficiency is discussed in detail. The final model provides promising results with an acceptable F1 score. Therefore, we believe that the model could be used in various applications, e.g., parental applications, chatbots, or services provided by Internet providers. Future research could investigate the usage of the model in other languages.
ER -
PLHÁK, Jaromír, Ondřej SOTOLÁŘ, Michaela LEBEDÍKOVÁ and David ŠMAHEL. Classification of Adolescents' Risky Behavior in Instant Messaging Conversations. Online. In Ruiz F., Dy J., van de Meent J-W. \textit{26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023}. https://proceedings.mlr.press: ML Research Press, 2023, p.~2390-2404. ISSN~2640-3498.
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