Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Hospital de Día de Adolescentes, Servicio de Salud Mental, Consorci Corporació Sanitària Parc Taulí, Sabadell, Spain.
J Med Internet Res. 2020 Jul 7;22(7):e17758. doi: 10.2196/17758.
Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users.
This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk.
We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group).
We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users.
The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders.
自杀风险评估通常涉及医生和患者之间的互动。然而，由于获得心理健康保健设施的机会有限；临床医生的可用性降低；缺乏意识；以及围绕精神障碍的污名化、忽视和歧视，相当多的精神障碍患者得不到治疗。相比之下，互联网的普及和社交媒体的使用显著增加，为专家和患者提供了一种沟通的手段，这可能有助于开发检测社交媒体用户心理健康问题的方法。
本文旨在描述一种针对西班牙语社交媒体用户的自杀风险评估方法。我们旨在探索从多个社交平台提取的行为、关系和多模态数据，并开发用于检测风险用户的机器学习模型。
我们根据用户的写作、发布模式、与其他用户的关系以及发布的图像对用户进行特征描述。我们还评估了用于处理多模态数据以检测有自杀意念迹象的用户的统计和深度学习方法（自杀意念风险组）。我们的方法在由临床医生标注的 252 名用户的数据集上进行了评估。为了评估我们模型的性能，我们区分了 2 个对照组：使用自杀相关词汇的用户（重点对照组）和普通随机用户（普通对照组）。
我们发现，与自杀意念风险组相比，每个对照组的文本和行为属性都存在显著的统计学差异。在 95%置信区间内，当比较自杀意念风险组和重点对照组时，朋友数量（P=.04）和平均推文长度（P=.04）有显著差异。重点对照组用户的平均朋友数量（中位数 578.5）高于风险组用户（中位数 372.0）。同样，重点对照组用户的平均推文长度也较高，为 16 个单词，而自杀意念风险用户为 13 个单词。我们的研究结果还表明，文本、视觉、关系和行为数据的组合优于单独使用每种模态的准确性。我们定义了基于词汇袋和词嵌入的文本基线模型，这些模型的性能优于我们的模型，当将风险用户与两种类型的对照组用户区分开来时，准确性提高了 8%。
分析的属性类型对检测风险用户具有重要意义，并且它们的组合优于通用的、仅基于文本的基线模型提供的结果。在评估基于图像的预测模型的贡献后，我们认为通过增强基于文本和关系特征的模型可以提高我们的结果。这些方法可以扩展并应用于与其他精神障碍相关的其他用例。
