Assessment of supervised classifiers for the task of detecting messages with suicidal ideation

Bootstrapping semi-supervised annotation method for potential suicidal messages

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.

UrbangEnCy: An emergency events dataset based on citizen sensors for monitoring urban scenarios in Ecuador

Recently, the use of the citizen-sensors (people generating and sharing real data by social media) for detecting and disseminating emergency events in real-time have shown a considerable increase because people at the place of the event, as well as elsewhere, can quickly post relevant information on this type of alerts. Here, we present an emergency events dataset called UrbangEnCy. The dataset contains over 25500 texts in Spanish posted on Twitter from January 19th to August 19th, 2020, with emergencies and non-emergencies related content in Ecuador. We obtained, cleaned and, filtered these tweets and, then we selected the location and temporal data as well as tweet content. Besides, the data set includes annotations regarding the type of tweet (emergency / non-emergency) as well as additional nomenclature used to describe emergencies in the Center for immediate response service to emergencies (ECU 911) of Ecuador and international emergency services agencies (ESAs). UrbangEnCy dataset facilitates evaluating data science performance, machine learning, and natural language processing algorithms used with supervised and unsupervised problems re- related to text mining and pattern recognition. The dataset is freely and publicly available at https://doi.org/10.17632/4x37zz82k8.