Bridging the Communication Gap: Utilizing Large Language Models to Detect Emotional Distress and Depression in Adolescent Communication for Parental Support

Jeremie Ruvunangiza* and C Valderrama

In the digital age, the communication gap between parents and adolescents has increased, presenting challenges to understanding the emotional well-being of their children. With the increasing prevalence of social networks, adolescents tend to express their feelings and struggles online rather than engage in face-to-face interaction. Existing monitoring tools allow parents to read messages and observe social media activity, but often fail to interpret the emotional content. Recent studies have explored the feasibility of using natural language processing and machine learning to predict depression based on social media activity. By analyzing the linguistic patterns, sentiments, and emotional content of online communication, researchers have demonstrated the potential to identify individuals suffering from depression at an early stage.

This article proposes a novel solution that uses large language models (LLMs) to monitor and analyze adolescent communication on digital platforms, including smartphones and social media. The system aims to detect emotional distress, signs of depression, and other mental health indicators, providing timely alerts to parents. This technology enables parents to understand their teens’ emotions, offer the necessary support, and prevent the escalation of anxiety and depression.

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Published on: Jul 27, 2024 Pages: 56-61

Full Text PDF Full Text HTML DOI: 10.17352/2455-5460.000095
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