A Dataset for Research on Modelling Depression Severity in Online Forum Data (2021)

Abstract: People utilize online forums to either look for information or to contribute it. Because of their growing popularity, certain online forums have been created specifically to provide support, assistance, and opinions for people suffering from mental illness. Depression is one of the most frequent psychological illnesses worldwide. People communicate more with online forums to find answers for their psychological disease. However, there is no mechanism to measure the severity of depression in each post and give higher importance to those who are diagnosed more severely depressed. Despite the fact that numerous researches based on online forum data and the identification of depression have been conducted, the severity of depression is rarely explored. In addition, the absence of datasets will stymie the development of novel diagnostic procedures for practitioners. From this study, we offer a dataset to support research on depression severity evaluation. The computational approach to measure an automatic process, identified severity of depression here is quite novel approach. Nonetheless, this elaborate measuring severity of depression in online forum posts is needed to ensure the measurement scales used in our research meets the expected norms of scientific research.

## introduction with technological advancement, online support forums (osf) are popular among society and attempt to seek solutions for their physical, social and psychological problems @xcite . as subgroups of osf, online mental support forums (omsf) are designed primarily for psychological expertise and experienced peer groups to provide support and guidance to the psychologically impaired community. early identifying and analysing the severity of depression is crucial for: (a) providing suitable medications and therapeutic processes immediately after diagnosing (b) preventing fatal accidents such as deliberate self-harms or suicides. depression is greater than definitely feeling unhappy or bored stiff for a few days. the majority undergo durations of feeling down, but whilst you're depressed you experience persistently unhappy for weeks or months, in preference to only some days. the best information is that with the proper treatment and support, the majority with depression could make a complete recuperation @xcite . to prevent from those tragic situations, in clinical psychology several psychological assessments such as dsm -v, bdi were defined to measure the severity of depression disorder. among those assessments, dsm-v criteria are widely used to diagnose clinical depression disorder and have been classified into different severity levels according to their number of symptoms, duration and cause for depressive episodes. ## motivation in previous studies, forum data was annotated as depressive or non-depressive posts and but with no information regarding the severity level of depression in each post based on the individual's thought process. we aim to build a level of severity for each forum post, annotated dataset for depression severity assessment using an automatic annotation approach which enables us to extract evidence related to depression. moreover, to predict the level of depression severity from posts available in omsf. ## conclusion there has been growing research interest towards the identification changes of the mental state through the exploitation of social data. the absence of resources, such as datasets might limit the development of cutting-edge technologies to support health practitioners. we developed a dataset and automatic process for labelling the forum posts with four severity levels of depression. according to the results obtained deep learning algorithms performs better than general classification algorithms, when analysing unstructured forum data. since this research is ongoing research, we have considered only quarter of our dataset for this experiment. in future, we are planning to train this model with full dataset which requires huge computation time. also, we are planning improve the overall accuracies of the models by optimizing the classification algorithms. nowadays, emoji plays a fundamental role in human computer-mediated communications, allowing the latter to convey body language, objects, symbols, or ideas in text messages. the relationship between language, emoji, and emotions is now being studied by several disciplines such as linguistics, psychology, natural language processing (nlp), and machine learning (ml). therefore, we hold the opinion that in the future, could also benefit from a more qualitative approach, such as interviews and case studies to learn about emoji use in the context of real-world communication. in the future, we plan to identify the progression of depression severity within the forum thread in omsf.