A Novel Approach for Communication-related to suicidal detection on Twitter using multi-class data

Abstract

Suicide is a significant issue for public health worldwide since suicide is not something that happens randomly but is influenced by social and environmental variables as well. At the same time, effective early diagnosis and treatment may lead to several positive health and behavioural results. Suicide persists undiagnosed and untreated for many reasons, including denial of sickness and cultural and social disgrace. Through the ubiquity of social media, by expressing opinions, thoughts and everyday struggles with mental health on social media, millions of people are sharing their online identity. As opposed to typical retrospective research that uses self-reported surveys and questionnaires, this study assesses the validity of identifying suicidal symptoms using Twitter tweets that were gathered over more than a year, using a variety of online web-blogging sites as points of reference. For recognizing tweets expressing suicidal thoughts, three sets of characteristics are employed for training the dataset employing base and ensemble classifiers. The Rotation Forest (RF) approach is the preferred baseline, and the Maximum Probability Voting Decision approach is used in seven different labelled classes relating to suicide communication and class demonstrating suicidal thoughts. With the suicidal ideation class scoring 0.76 and the suicidal contents for all seven classes scoring 0.82, this revised model was able to attain an F-measure. To increase awareness of the vocabulary made use of on Twitter to express suicidal thoughts, the findings are summarized by highlighting the predictive principal component of suicide communication in classrooms.