Data Science Research Theme: Self-harm: Detection and Support on Twitter

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Date/Time
Date(s) - 27/08/2021
11:00 am - 12:00 pm


Since the advent of online social media platforms such as Twitter and Facebook, useful health-related studies have been conducted using the information posted by online participants. Personal health-related issues such as mental health, self-harm and depression have been studied because users often share their stories on such platforms. Online users resort to sharing because the empathy and support from online communities are crucial in helping the affected individuals.

A preliminary analysis shows how contents related to non-suicidal self-injury (NSSI) proliferate on Twitter. Thus, we use Twitter to collect relevant data, analyse, and proffer ways of supporting users prone to NSSI behaviour. Our approach utilises a custom crawler to retrieve relevant tweets from self-reporting users and relevant organisations interested in combating self-harm. Through textual analysis, we identify six major categories of self-harming users consisting of inflicted, anti-self-harm, support seekers, recovered, pro-self-harm and at risk.

The inflicted category dominates the collection. From an engagement perspective, we show how online users respond to the information posted by self-harm support organisations on Twitter. By noting the most engaged organisations, we apply a useful technique to uncover the organisations’ strategy. The online participants show a strong inclination towards online posts associated with mental health related attributes.

The study is based on the premise that social media can be used as a tool to support proactive measures to ease the negative impact of self-harm. Consequently, we proffer ways to prevent potential users from engaging in self-harm and support affected users through a set of recommendations. To support further research, the dataset will be made available for interested researchers.

Link to paper: https://arxiv.org/abs/2104.00174.

The seminar will cover (in summary) the study’s background, research approach, and critical findings.

Join via Zoom:
https://strath.zoom.us/j/89703761857

Meeting ID: 897 0376 1857, Password: 363590

Organiser: Muhammad Alhassan, University of Strathclyde.

 

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