Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Sep 03, 2024)
Date Submitted: Apr 25, 2023
Open Peer Review Period: Apr 25, 2023 - Jun 20, 2023
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Suicide Prediction from Social Media Data- Systemic Review Study
ABSTRACT
Background:
Timely prediction and appropriate intervention can prevent suicide. However, suicide prediction has long been a major public health challenge and researcher explored different innovative approaches to find out a cost effective and efficient prediction system that can be scaled up to reduce the burden. Social media opened new horizon of research to monitor the behavior and activities of person in different context and observe expression and interaction at their own environments. The opportunity to monitor of large number of people at their natural environment made Social Media such as Twitter, Facebook, Weibo an important area of research for suicide prediction. Several studies have been reported those used different methods to develop suicide prediction system from social media data. However, it is still unclear whether social media can predict completed suicide from social media data through a systematized literature search.
Objective:
This study aimed to conduct a systematic search to find out common method of suicide prediction from social media data and to what extend social media data can predict completed suicides.
Methods:
A systemic search was conducted in multiple databases, grey literature including conference proceeding and clinical trial register from inception to 25th July 2019 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-2009 guideline. Snowball searching from the reference lists of included papers and personal communication with few potential authors were made to ensure maximum number of updated papers. Two researchers conducted the initial literature search independently following the predefined search strategies.
Results:
Of the 2955 papers screened, 15 were selected for the review which documented social media can predict suicide at individual or large population level by monitoring the frequency of posting, pattern of use of language, emotional expression on it. The social media-based suicide data corelated with regional, national or international suicide data.
Conclusions:
Though social media-based suicide prediction documented promising result, their use in large scale suicide surveillance jeopardized by the ethical concern, quality of data and complex rapidly shifting digital ecosystem.
Citation
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