Self-harm: detection and support on Twitter
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. Our 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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Depression and Self-Harm Risk Assessment in Online Forums
Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neu…
Just a Scratch: Enhancing LLM Capabilities for Self-harm Detection through Intent Differentiation and Emoji Interpretation
Self-harm detection on social media is critical for early intervention and mental health support, yet remains challenging due to the subtle, context-dependent nature of such expressions. Identifying self-harm intent aids…
Multi-Task LearningSensitivityDetecting Adverse Drug Reactions from Twitter through Domain-Specific Preprocessing and BERT Ensembling
The automation of adverse drug reaction (ADR) detection in social media would revolutionize the practice of pharmacovigilance, supporting drug regulators, the pharmaceutical industry and the general public in ensuring th…
PharmacovigilanceMining Mental Health Signals: A Comparative Study of Four Machine Learning Methods for Depression Detection from Social Media Posts in Sorani Kurdish
Depression is a common mental health condition that can lead to hopelessness, loss of interest, self-harm, and even suicide. Early detection is challenging due to individuals not self-reporting or seeking timely clinical…
Effects of Sampling on Twitter Trend Detection
Much research has focused on detecting trends on Twitter, including health-related trends such as mentions of Influenza-like illnesses or their symptoms. The majority of this research has been conducted using Twitter{'}s…