![]() This is at a time when the lack of datasets is a major obstacle to the development of applied mental health research. The general process of using machine learning techniques in the research is as follows: 1) presenting a questionnaire for a predefined group of individuals, 2) request access to data and collection, 3) fitting the model based on the selected features and information extracted from the questionnaires, and 4) measuring the accuracy of estimation based on the test set. For example, many studies have highlighted that language patterns may serve as an indicator of the mental health state, also leading to the early detection of depression through machine learning techniques. Predicting well-known symptoms might be done from user-generated content on social media, leading to new forms for the screening of the mental disorder. While mental disorders are difficult to diagnose and monitor through traditional approaches, which heavily relying on surveys and interviews, online screening tools are valuable and might act in the future as more standard assessment strategies, like medical decision support systems or health surveillance tools that can analyze signs of mental disorders. highlighted that social media is established as a data source in various contexts, increasingly used in population health monitoring, and is beginning to be used for mental health applications. These data provide a unique opportunity for researchers to understand users in detail. It is also stated that the global unique user total grew by 520 million over the past year, representing annual growth of more than 13%. According to Hootsuite, 2 a well-known social media management platform, in July 2021, 4.48 billion people, or equal to almost 57% of the world's total population, are using social media. This leads to big social data, containing traces of valuable information reflecting people’s interests, moods, and behavior. There is evidence that people increasingly turn to social media platforms such as Twitter and Facebook to represent their opinions, communicate with others, and share their feelings. The growing number of US youths with a major depressive episode from 2004 to 2019, by gender It is clear that new prevention and intervention strategies are in high demand. Besides that, studies pointed out that the estimated economic value of mental illness is expected to reach 5 trillion dollars by 2030. As an example of this trend, the percentage of US youths with a major depressive episode from 2004 to 2019, by gender is depicted in Fig. The lack of appropriate treatment can lead to disability, psychotic episodes, thoughts of self-harm, and suicide, that is contributing to more than 800,000 deaths every year, and ranking as the second leading cause of deaths among 15 to 29 year olds. While there are known, effective treatments for depression, only a few percentage people have received treatment for it. 1 Recent findings also state that there is a high prevalence of mental health problems, during the COVID-19 outbreak. In January 2020, the Mental Disorders Fact Sheet on World Health Organization (WHO) showed that, globally, more than 264 million people of all ages suffer from depression. We also believe performance improvements can be achieved by limiting the user domain or presence of clinical information. ![]() ![]() Based on the analysis, the tweets and bio-text alone showed 91% and 83% accuracy in predicting depressive symptoms, respectively, which seems to be an acceptable result. We consider the correlation-based feature selection and nine different classifiers with standard evaluation metrics to assess the effectiveness of the method. We used n-gram language models, LIWC dictionaries, automatic image tagging, and bag-of-visual-words. ![]() In this paper, we provide an automated approach to collect and evaluate tweets based on self-reported statements and present a novel multimodal framework to predict depression symptoms from user profiles. Due to the tendency of people to share their thoughts on social platforms, social data contain valuable information that can be used to identify user’s psychological states. Depression is the most prevalent mental disorder that can lead to suicide. ![]()
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