Exploratory Analysis of Social Media Prior to a Suicide Attempt

@inproceedings{Coppersmith2016ExploratoryAO,
  title={Exploratory Analysis of Social Media Prior to a Suicide Attempt},
  author={Glen A. Coppersmith and Kim Ngo and Ryan Leary and Anthony Wood},
  booktitle={CLPsych@HLT-NAACL},
  year={2016}
}
Tragically, an estimated 42,000 Americans died by suicide in 2015, each one deeply affecting friends and family. [] Key Result We find quantifiable signals of suicide attempts in the language of social media data and estimate performance of a simple machine learning classifier with these signals as a non-invasive analysis in a screening process.

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References

SHOWING 1-10 OF 52 REFERENCES
Quantifying Suicidal Ideation via Language Usage on Social Media
TLDR
This research examines quantifiable signals related to suicide attempts and suicidal ideation in the language of social media data, and applies simple language modeling techniques to separate users automatically, and examines what quantifiable signal allow them to function, tying them back to psychometrically validated concepts related to Suicide.
Measuring Post Traumatic Stress Disorder in Twitter
TLDR
PTSD is considered, a serious condition that affects millions worldwide, with especially high rates in military veterans, and its utility is demonstrated by examining differences in language use between PTSD and random individuals, building classifiers to separate these two groups and by detecting elevated rates of PTSD at and around U.S. military bases using classifiers.
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
TLDR
This paper develops a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation, and utilizes semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts.
Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides
TLDR
Findings on the prevalence of the Werther effect in an online platform: r/SuicideWatch on Reddit show that post-celebrity suicide content is more likely to be inward focused, manifest decreased social concerns, and laden with greater anxiety, anger, and negative emotion.
Predicting Depression via Social Media
TLDR
It is found that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement.
Social media as a measurement tool of depression in populations
TLDR
A social media depression index is introduced that may serve to characterize levels of depression in populations and confirm psychiatric findings and correlate highly with depression statistics reported by the Centers for Disease Control and Prevention (CDC).
Quantifying the Language of Schizophrenia in Social Media
TLDR
Potential linguistic markers of schizophrenia using the tweets 1 of self-identified schizophrenia sufferers are explored, several natural language processing methods are described to analyze the language of schizophrenia, and preliminary evidence of additional linguistic signals are provided.
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses
TLDR
A broad range of mental health conditions in Twitter data is examined by identifying self-reported statements of diagnosis and language differences between ten conditions with respect to the general population, and to each other are systematically explored.
Quantifying Mental Health Signals in Twitter
TLDR
A novel method for gathering data for a range of mental illnesses quickly and cheaply is presented, then analysis of four in particular: post-traumatic stress disorder, depression, bipolar disorder, and seasonal affective disorder are focused on.
An adolescent suicide cluster and the possible role of electronic communication technology.
TLDR
Relevant community agencies should proactively develop a strategy to enable the identification and management of suicide contagion and guidelines to assist communities in managing clusters should be updated to reflect the widespread use of communication technologies in modern society.
...
...