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.
翻译:自在线社交媒体平台(如Twitter和Facebook)出现以来,利用在线参与者公布的信息开展了有益的健康相关研究。个人健康相关问题(如心理健康、自我伤害和抑郁症)已经研究,因为用户经常在此类平台上分享其故事。在线用户之所以使用共享,是因为在线社群的同情和支持对帮助受影响个人至关重要。初步分析显示与非自杀自残(NSSI)有关的内容如何在Twitter上扩散。因此,我们利用Twitter收集相关数据、分析和提供支持容易发生 NSSI行为的用户的方法。我们的方法是使用定制爬升器从自我报告用户和有兴趣打击自我伤害的相关组织那里检索相关推特。通过文本分析,我们确定了自我伤害用户的六大类主要自我伤害用户,这些用户包括受伤害、自残、自残、自残和风险等。从参与角度出发,我们展示在线用户如何应对在支持组织在Twitter上发布的信息。我们注意到最有参与的组织,我们运用了一种有用的技术从自我定位的自我定位,通过在线支持来展示我们的潜在健康工具,从而发现我们的潜在健康目标。我们使用了一个有用的工具。