AraDepSu: Detecting Depression and Suicidal Ideation in Arabic Tweets Using Transformers

Mariam Hassib, Nancy Hossam, Jolie Sameh, Marwan Torki
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引用次数: 1

Abstract

Among mental health diseases, depression is one of the most severe, as it often leads to suicide which is the fourth leading cause of death in the Middle East. In the Middle East, Egypt has the highest percentage of suicidal deaths; due to this, it is important to identify depression and suicidal ideation. In Arabic culture, there is a lack of awareness regarding the importance of diagnosing and living with mental health diseases. However, as noted for the last couple years people all over the world, including Arab citizens, tend to express their feelings openly on social media. Twitter is the most popular platform designed to enable the expression of emotions through short texts, pictures, or videos. This paper aims to predict depression and depression with suicidal ideation. Due to the tendency of people to treat social media as their personal diaries and share their deepest thoughts on social media platforms. Social data contain valuable information that can be used to identify user’s psychological states. We create AraDepSu dataset by scrapping tweets from twitter and manually labelling them. We expand the diversity of user tweets, by adding a neutral label (“neutral”) so the dataset include three classes (“depressed”, “suicidal”, “neutral”). Then we train our AraDepSu dataset on 30+ different transformer models. We find that the best-performing model is MARBERT with accuracy, precision, recall and F1-Score values of 91.20%, 88.74%, 88.50% and 88.75%.
AraDepSu:使用变压器检测阿拉伯语推文中的抑郁和自杀意念
在心理健康疾病中,抑郁症是最严重的疾病之一,因为它经常导致自杀,而自杀是中东地区第四大死亡原因。在中东,埃及的自杀死亡率最高;因此,识别抑郁症和自杀意念是很重要的。在阿拉伯文化中,人们缺乏对诊断和患有精神疾病的重要性的认识。然而,正如过去几年所指出的,世界各地的人们,包括阿拉伯公民,倾向于在社交媒体上公开表达他们的感受。Twitter是最受欢迎的平台,旨在通过短文本、图片或视频来表达情感。本研究旨在预测抑郁症及抑郁症伴自杀意念。因为人们倾向于把社交媒体当成自己的个人日记,在社交媒体平台上分享自己最深刻的想法。社交数据包含有价值的信息,可以用来识别用户的心理状态。我们通过从twitter上删除tweet并手动标记它们来创建AraDepSu数据集。我们通过添加一个中性标签(“中性”)来扩展用户tweet的多样性,因此数据集包括三个类别(“抑郁”,“自杀”,“中立”)。然后我们在30多个不同的变压器模型上训练我们的AraDepSu数据集。我们发现表现最好的模型是MARBERT,其准确率、精密度、召回率和F1-Score值分别为91.20%、88.74%、88.50%和88.75%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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