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Item type:Publication, Assessment of supervised classifiers for the task of detecting messages with suicidal ideation(Elsevier, 2020-08-01)According to the World Health Organization (WHO) close to 800,000 people worldwide die by suicide each year, and many more attempts to do it. In consequence, the WHO recognizes suicide as a global public health priority, which affects not only rich countries but poor and middle-income countries as well. This study makes a systematic analysis of 28 supervised classifiers using different features of the corpus Life to detect messages with suicidal ideation and depression to know if these can be used in an automatic prevention online system. The Life Corpus, used in this research, is a bilingual text corpus (English and Spanish) oriented to the detection of suicide ideation. This corpus was constructed retrieving texts from several social networks and its quality was measured using mutual annotation agreement. The different experiments determined that the classifier with the best performance was KStar, with the corpus features POS-SYNSETS-NUM, achieving the best results with the ROC Area metrics of 0,81036 and F-measure of 0,7148. The present research fulfilled the objective of discovering which supervised classifiers and which features are the most suitable for the automatic classification of messages with suicidal ideation using the Life Corpus. Also, given the imbalance of the results, a new precision measure was developed called the Two-dimensional Accuracy and Recovery Index (GDP), which can provide better results, in unbalanced systems, than the usual measures to assess the quality of the results (measure F, Area ROC), and thus increase the number of messages at risk of suicidal ideation, detected at the cost of receiving more messages that are not related to suicide or vice versa. Computer Science; Suicidal ideation; supervised classifiers; Machine Learning; Social networks; automatic classification; suicid10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Expressions of authoritarianism in Facebook before the salience of news about crime in Peru(Universidad Catolica del Uruguay, 2021-07-05)Under a context of public insecurity, the present study aims to analyse comments expressed in news about criminal acts published on the web pages of the main national newspapers during 2015. Comments were subject to content analysis and two analytical axes identified: (a) The expression of authoritarian beliefs and the implications of these beliefs for perceptions of offenders, and (b) a lack of trust in the public institutions of justice and its link with authoritarian response to criminality. The analysis of comments reveals a strong tendency towards endorsements of violent actions in dealing with crime supported by an authoritarian ideology. It was also found that distrust in the public institutions of justice is linked to support for extreme measures in order to eliminate the perceived threat. Limitations and suggestions for further research are discussed. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Psychological profiles of social media users: an exploratory and predictive study in Peruvian adolescents(Springer International Publishing, 2021-10-25) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bootstrapping semi-supervised annotation method for potential suicidal messages(Elsevier B.V., 2022-04-01)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 0.86.2