Predicting Depression Symptoms in an Arabic Psychological Forum

Abstract

Recently, social media platforms have been widely used as a communication tool on social networks. Many users have utilized these platforms to reflect their personal lives. These users differ in terms of background, language, age, and educational level. The close relationship between these platforms and their users has created rich information that is related to these users and can be exploited by researchers. Their posts can be analysed using natural language processing (NLP) to predict psychological traits such as depression. However, to the best of our knowledge, no study has utilized social media to predict mental health disorders in Arabic posts, especially depression. Therefore, in this study, we investigate the application of natural language processing and machine learning on Arabic text for the prediction of depression, and we evaluate and compare the performance. Our research method is based on the collection of Arabic text from online forums and the application of either a lexicon-based approach or a machine-learning-based approach. In the former approach, the ArabDep lexicon is created, and a rule-based algorithm is used to predict depression symptoms using the created lexicon; however, in the latter approach, the data are annotated with the help of a psychologist, text features are extracted from Arabic posts, and machine learning algorithms are ultimately applied to predict depression symptoms. We demonstrate that our applied approaches exhibit promising performance in predicting whether a post corresponds to depression symptoms, with an accuracy of more than 80%, a recall of 82% and a precision of 79%.

Prediction of depressed Arab women using their tweets

“…Clinical depression is described as suffering from unhappiness and hopelessness for a long period of time, which (if not received adequate treatment and support) has the ability to last many months at a time (Khan et al, 2018). Depression also has physical symptoms such as; constant feeling of tiredness, sleepiness, and loss of appetite, besides all this, depression is still seen as a taboo in the Arab world and cannot be talked about publicly out of fear of criticism (Alghamdi et al, 2020). This has led many to choose to express their feelings over social media, as an escape.…”

“…However, there hasn't been enough research on doing this in the Arab world, as there are some barriers. The formal Arabic language is much more complex than English, where one word can have many meanings, making it difficult for software to analyse at a high level of accuracy (Alghamdi et al, 2020). Moreover, the Arabic used is often informal, which has a high level of variance between each country and dialect (Alruwaili et al, 2019).…”

“…The idea of the proposed model is the ability to predict the women depression from her tweets to create a reliable model that is able to provide effective indicators to enhance its performance. As the available work for Arabic (Alghamdi et al, 2020) posts does not use RNN.…”

Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

“…Gupta et al [15] used SenticNet 4.0 Lexicon and their own manually created Profile Lexicon for predicting both sentiment and behavior. Alghamdi et al [16] proposed a semi-supervised approach (lexicon-based approach) for the prediction of depression from Arabic posts. Although the method based on the sentiment lexicon is simple in operation, the construction of the sentiment lexicon is time-consuming and labor-intensive, and the scope of expansion is limited, which has domain limitations.…”

Tracing the Emotional Roadmap of Depressive Users on Social Media Through Sequential Pattern Mining

“…Among the most popular social networks are Reddit, Facebook, Twitter, and Instagram. Online social networks are potential tools for specialists in human behavior, such as psychologists, psychiatrists, and therapists, given the extensive sharing of personal user data online, contributing to provide patients' extra information to specialists [5]- [9]. For example, a specialist could take advantage of an existing platform's emotional reports (with previous consent).…”