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Analyzing Language in Suicide Notes and Legacy Tokens.
Michael J Egnoto1, Darrin J Griffin2
11 FIRE Risk Communication and Resilience, National Consortium for the Study of Terrorism and Responses to Terrorism (START), University of Maryland, College Park, MD, USA.
Researchers used text analysis to identify writings indicating self-harm or harm to others. This automated method shows promise for early detection of harmful ideation in individuals.
Area of Science:
- Computational Linguistics
- Psychological Assessment
- Behavioral Forensics
Background:
- Identifying individuals at risk of self-harm or harming others is a critical research area.
- Previous studies focused on various precursors to violent behavior.
Purpose of the Study:
- To investigate the potential of using linguistic analysis of legacy tokens and suicide notes.
- To uncover behavioral signals that may foreshadow harmful actions.
Main Methods:
- Comparative text analysis of 25 suicide notes and 21 legacy tokens against over 20,000 student writings.
- Utilized computer-assisted analysis with the Linguistic Inquiry and Word Count (LIWC) tool.
Main Results:
- Text analysis effectively distinguished between suicidal and homicidal writings.
- Automated identification of harmful writings was achieved with high accuracy.
Conclusions:
- Automated text analysis can identify writings associated with self-harm and harm to others.
- This research supports scalable, low-cost methods for detecting harmful ideation.
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