Modeling the Differential Prevalence of Online Supportive Interactions in Private Instant Messages of Adolescents

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00141937" target="_blank" >RIV/00216224:14330/25:00141937 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2025.findings-naacl.347/" target="_blank" >https://aclanthology.org/2025.findings-naacl.347/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2025.findings-naacl.347" target="_blank" >10.18653/v1/2025.findings-naacl.347</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling the Differential Prevalence of Online Supportive Interactions in Private Instant Messages of Adolescents

Popis výsledku v původním jazyce

This paper focuses on modeling gender-based and pair-or-group disparities in online supportive interactions among adolescents. To address the limitations of conventional social science methods in handling large datasets, this research employs language models to detect supportive interactions based on the Social Support Behavioral Code and to model their distribution. The study conceptualizes detection as a classification task, constructs a new dataset, and trains predictive models. The novel dataset comprises 196,772 utterances from 2165 users collected from Instant Messenger apps. The results show that the predictions of language models can be used to effectively model the distribution of supportive interactions in private online dialogues. As a result, this study provides new computational evidence that supports the theory that supportive interactions are more prevalent in online female-to-female conversations. The findings advance our understanding of supportive interactions in adolescent communication and present methods to automate the analysis of large datasets, opening new research avenues in computational social science.

Název v anglickém jazyce

Modeling the Differential Prevalence of Online Supportive Interactions in Private Instant Messages of Adolescents

Popis výsledku anglicky

This paper focuses on modeling gender-based and pair-or-group disparities in online supportive interactions among adolescents. To address the limitations of conventional social science methods in handling large datasets, this research employs language models to detect supportive interactions based on the Social Support Behavioral Code and to model their distribution. The study conceptualizes detection as a classification task, constructs a new dataset, and trains predictive models. The novel dataset comprises 196,772 utterances from 2165 users collected from Instant Messenger apps. The results show that the predictions of language models can be used to effectively model the distribution of supportive interactions in private online dialogues. As a result, this study provides new computational evidence that supports the theory that supportive interactions are more prevalent in online female-to-female conversations. The findings advance our understanding of supportive interactions in adolescent communication and present methods to automate the analysis of large datasets, opening new research avenues in computational social science.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EH22_008%2F0004583" target="_blank" >EH22_008/0004583: Excelentní výzkum v oblasti digitálních technologií a wellbeingu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Findings of the Association for Computational Linguistics: NAACL 2025

ISBN

9798891761957

ISSN

—

e-ISSN

—

Počet stran výsledku

19

Strana od-do

6223-6241

Název nakladatele

Association for Computational Linguistics

Místo vydání

Albuquerque, New Mexico

Místo konání akce

Albuquerque, New Mexico

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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