A ra D ep S u: Detecting Depression and Suicidal Ideation in A rabic Tweets Using Transformers (2022)

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%.

## introduction the well-being of a person comprises physical health and mental health. the mental health of a person shows the individual's state of mind. mental disorders are a worldwide health problem affecting a large number of people and causing numerous deaths every year @xcite . depression is one of the most well-known mental health disorders and it is considered a major issue for mental health practitioners. depression is a mood disorder that causes a persistent feeling of sadness and loss of interest. also called a major depressive disorder or clinical depression. it affects how you feel, think and behave and can lead to a variety of emotional and physical problems. fortunately, it is also treatable especially if we identify it in the early stage. 2 in arabic culture, early diagnosis of mental illness is difficult, because of the stigma of mental illness and lack of awareness in the field of psychiatry. 3 depression has become a silent killer as it increases suicide risk. 4 people tend to express their feelings openly on social media, especially on twitter. twitter provides a platform where users share their thoughts, emotions, feelings, and expressions. these tweets can aid in determining a person's thought process, mental health, and behavioral traits. in this paper, our objective is to come up with a methodology to accurately classify and analyze arabic tweets. we classify whether they are suffering from depression or depression with suicidal ideation which can help prevent suicidal deaths. we focus on the potential of natural language processing (nlp) and machine learning techniques that can be utilized in the mental health field. nlp is very helpful when it comes to understanding the context of natural human language. as a result, it extracts latent meaning from text and creates aibased solutions using text data available on social ## background depression is considered a global concern. it is a very common illness, as it affects people across all nations. approximately 280 million people have recently been afflicted with depression in the world. 5 depression can cause the affected person to suffer greatly and function poorly at work, at school, and in the family. at its worst, depression can lead to suicide. over 700,000 people die due to suicide every year. depression is different from usual mood fluctuations and short-lived emotional responses to challenges in everyday life. 6 it comes in many forms, each accompanied by its own symptoms. the most common and known form is major depressive disorder (mdd), which influences the ability of individuals to do daily tasks (aldarwish and ahmad, 2017). depression does not have a target age, as it may begin at a young age. curbing depression is essential to saving people's lives @xcite . in arabic culture, the stigma on mental illness is deeply entrenched, and there is a lack of awareness regarding this issue. the review reveals that beyond society and culture, the persistence of mental illness stigma. in the arab world may be explained by inefficient monitoring mechanisms of mental health legislation and policies within the healthcare setting @xcite . ## related work this section presents a summary of prior studies that have been conducted on the prediction and monitoring of depression and suicide using social media. various related data have been in the literature for the prediction of depression via various approaches. two main strategies were reported in the literature to collect data and detect depression through social media. the first strategy is crowd-sourcing data collection from social media publicly available. this allows researchers to cheaply outsource simple tasks or questionnaires, and gather data in realtime. it also helps to obtain far more numerous and widespread observations than in traditional data collection given its relatively low cost. the crowdsourcing strategy is mainly conducted in two stages @xcite . first, responses from an online clinical depression survey are gathered. then, contents are collected by accessing the twitter data of the consented participants. this main strategy limitation is time-consuming. the second alternative strategy is characterized by gathering data quickly and cheaply @xcite . as such, the data is collected directly from social media that are publicly available for participants with self-identified mental illnesses. the disadvantage of this strategy is its low reliability. unfortunately, very few of these collected data and applied models were found in arabic. conducting a sentiment analysis of texts in the arabic language is more complex than that directed toward english texts. that is because the arabic language is characterized by more forms than other languages. the formal variant of arabic is modern standard arabic (msa), but this is rarely used in spoken interactions. the most frequently used informal variant is dialectal arabic (da), especially for communication purposes. a total of 30 major arabic dialects differ from msa, the approaches used to translate difficult msa terms are ineffective when applied to da translation. recently, arabic researchers have developed solutions for different dialects, but these remain minimally inaccurate and cover only a few dialects @xcite . our work mainly focuses on egyptian dialectal with it the most studied and widely spoken da. a common challenge that faces most depression detection trials is to identify the symptoms of mental illness in online health communities. this is due to the symptom overlapping between multiple mental illnesses. to the best of our knowledge, no previous trials went deeply into the arabic twitter data for detecting whether a user's tweet is depressed or depressed with suicidal ideation. to fill this literature gap, our solution helps in detecting signs relevant to depression using arabic language tweets. to avoid the limitations related to data collection we faced in the beginning, we combine low-cost and reliable data collection strategies. we collected more than 20k tweets data from public tweets and labeled them manually. ## discussion models pre-trained on multiple languages ## conclusion this study enables intelligent instruments to identify and predict depression symptoms and suicide ideation from arabic text based on depressionrelated words. this paper proposed computational approaches for the utilization of arabic tweets. we scraped data from tweeter with keywords that act as depression triggers and labeled them manually. in conclusion and based on the results discussed above, arabic people do share their feelings on twitter. the results prove that depressed people show specific behaviors within their tweets. they often use negative words to describe their symptoms, like suicidal thoughts or sleeping disorders. we built a predictive model to predict whether a user's tweet is depressed or depressed with suicidal ideation. we examined the performance of all the above classifiers using a dataset collected from twitter and labeled manually with truth labels ("depressed", "suicidal", "neutral"). we found the best accuracy with the marbert classifier at 91.20%.