Classification of Adolescents' Risky Behavior in Instant Messaging Conversations

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Publikace nespadá pod Lékařskou fakultu, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
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PLHÁK Jaromír SOTOLÁŘ Ondřej LEBEDÍKOVÁ Michaela ŠMAHEL David

Rok publikování 2023
Druh Článek ve sborníku
Konference 26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://proceedings.mlr.press/v206/plhak23a/plhak23a.pdf
Klíčová slova adolescents; smartphones; machine learning; risky behavior; instant messaging
Popis 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.
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