The suicide of a person is a tragedy that deeply affects families, communities, and countries. According to the standardized rate of suicides per number of inhabitants worldwide, in 2022 there will be approximately about 903,450 suicides and 18,069,000 unconsummated suicides, affecting people of all ages, countries, races, beliefs, social status, economic status, sex, etc. The publication of suicidal intentions by users of social networks has led to the initiation of research processes in this field, to detect them and encourage them not to commit suicide. This study focused on determining a semi-supervised method to populate the Life Corpus, using a bootstrapping technique, to automatically detect and classify texts extracted from social networks and forums related to suicide and depression based on initial supervised samples. To carry out the experiments we used two different classifiers: Support Vector Machine (SVM) (with Bag of Words (BoW) features with and without Term-Frequency/Inverse Document Frequency (Tf/Idf), as a weighted term, and with or without stopwords) and Rasa (with the default feature extraction system). In addition, we performed the experiments using five data collections: Life, Reddit, Life+Reddit, Life_en, and Life_en + Reddit. Using the semi-supervised method, we managed to increase the size of the Life Corpus from 102 to 273 samples with texts from the social network Reddit, in a combination Life+Reddit+BoW_Embeddings, with the SVM classifier, with which a macro f1 value of 0.80 was achieved. These texts were in turn evaluated by annotators manually with a Cohen's Kappa level of agreement of 0.86.
https://www.sciencedirect.com/science/article/pii/S2214782922000264
