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Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning.

Woojin Jung, Donghun Kim, Seojin Nam

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    Summary
    This summary is machine-generated.

    Machine learning models can detect suicidality on Twitter by analyzing post content and metadata. Replies, afternoon posts, and weekend/fall tweets are more likely to be flagged, improving detection accuracy.

    Keywords:
    Classificationfeature extractionmachine learningsocial mediasuicidality detection

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    Area of Science:

    • Computational social science
    • Artificial intelligence in mental health

    Background:

    • Social media platforms like Twitter are increasingly used for communication, making them a potential source for identifying individuals at risk of suicide.
    • Previous research has explored text-based features for suicide risk detection, but the role of metadata remains underexplored.

    Purpose of the Study:

    • To develop and evaluate machine learning models for detecting suicidality in Twitter posts.
    • To investigate the significance of metadata features in enhancing the accuracy of suicidality detection models.

    Main Methods:

    • A dataset of 20,000 randomly selected and annotated Twitter posts was utilized.
    • Machine learning models were trained and evaluated using both text and metadata features.
    • Metadata features, including posting type and time-related information, were analyzed in detail.

    Main Results:

    • Posting type (reply vs. original tweet) and time-related features (month, day of the week, AM/PM) were identified as crucial metadata for suicidality detection.
    • Suicidality tweets were found to be more probable in replies, during afternoons, on Fridays, weekends, and in the fall season.
    • Integrating metadata and text features yielded a high-performing model with an F1 score of 0.846.

    Conclusions:

    • Metadata features significantly contribute to the accuracy of machine learning models for detecting suicidality on Twitter.
    • The developed model demonstrates practical utility in assisting human moderators in identifying at-risk social media content.
    • Further research can leverage these findings to build more sophisticated and effective suicide prevention tools for social media platforms.