Assessment of the prevalence of suicide among young adults using machine learning
Ibode, R.T., Tunde-Francis, A.A., Afeye, A.F., Anifowose, O.T. Owolola, O.I. & Ogidan, O.A.
Due to the high rate of suicide all over the world resulting in about 800,000 people dying by suicide each year. The instances where suicide victims constantly publish suicide messages deliberately to express their feelings on social media, there is need to address suicide issues, and how suicide can be prevented. Therefore, as a solution to this, there is need to create a model that classifies these users" social media posts and identify users with suicidal ideations, so as to prevent future suicide cases by getting the identified users the necessary help needed. The study adopted a binary classification of a suicide-related tweet with respect to age 15 up till 29 years, on a document-level basis. A machine learning approach was employed to solve the problem of tweet classification and predictions. The dataset was generated from a Twitter API. It was observed that suicidal issues are rampant among the young adult, which need urgent attention. The paper recommended that timely intervention should be provided so as to reduce suicidal victims and preserve the future of young adults.