Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

Carlos M. Castorena, Itzel M. Abundez, Roberto Alejo, Everardo E. Granda-Gutiérrez, Eréndira Rendón and Octavio Villegas
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Carlos M. Castorena: Division of Postgraduate Studies and Research, National Technological of Mexico, Instituto Tecnológico de Toluca, Metepec 52149, Mexico
Itzel M. Abundez: Division of Postgraduate Studies and Research, National Technological of Mexico, Instituto Tecnológico de Toluca, Metepec 52149, Mexico
Roberto Alejo: Division of Postgraduate Studies and Research, National Technological of Mexico, Instituto Tecnológico de Toluca, Metepec 52149, Mexico
Everardo E. Granda-Gutiérrez: UAEM University Center at Atlacomulco, Autonomous University of the State of Mexico, Toluca 50450, Mexico
Eréndira Rendón: Division of Postgraduate Studies and Research, National Technological of Mexico, Instituto Tecnológico de Toluca, Metepec 52149, Mexico
Octavio Villegas: Division of Postgraduate Studies and Research, National Technological of Mexico, Instituto Tecnológico de Toluca, Metepec 52149, Mexico

Mathematics, 2021, vol. 9, issue 8, 1-12

Abstract: The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word’s appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately.

Keywords: gender-based violence in Mexico; twitter messages; deep neural networks; class imbalance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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