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AI study uses clustering to find patterns in social media use and mental health

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed a clustering-based approach using unsupervised machine learning to analyze the relationship between social media usage and mental health. The study segmented 551 participants into six distinct clusters based on their social media habits and psychological well-being. Findings revealed patterns, including a 0.28 correlation between hours spent on social media and reported anxiety levels. AI

影响 Provides a new method for analyzing user behavior patterns and mental health correlations using unsupervised learning.

排序理由 This is a research paper published on arXiv detailing a novel application of machine learning techniques.

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AI study uses clustering to find patterns in social media use and mental health

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Md All Shahria, Sanjeda Dewan Mithila, Touhid Alam, Mohammad Sakib Mahmood, Mahfuza Khatun ·

    Uncovering Latent Patterns in Social Media Usage and Mental Health: A Clustering-Based Approach Using Unsupervised Machine Learning

    arXiv:2604.24611v1 Announce Type: new Abstract: The widespread adoption of social media has heightened interest in its psychological effects, particularly on mental health indicators such as anxiety, depression, loneliness, and sleep quality, as these platforms increasingly influ…

  2. arXiv cs.LG TIER_1 English(EN) · Mahfuza Khatun ·

    Uncovering Latent Patterns in Social Media Usage and Mental Health: A Clustering-Based Approach Using Unsupervised Machine Learning

    The widespread adoption of social media has heightened interest in its psychological effects, particularly on mental health indicators such as anxiety, depression, loneliness, and sleep quality, as these platforms increasingly influence social interactions and well-being. Althoug…

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