Tesis de Maestría / master Thesis
Digital violence against women: a phenomenon exploration to understand and counteract from a Data Science perspective
Fecha
2021-12-02Registro en:
737330
57226374794
Autor
Reyes González, Gregorio Arturo
Institución
Resumen
Investigations have shown that Violence Against Women is a pervasive problem that has
been increasing over the last years. Until a few years ago, it took place both in public and
private spaces, but it has now broken into Digital Space adopting more symbolic expressions.
Mexican cyberfeminists have fought to put this social problem on public agenda, achieving
this past June to legally typify Violence Against Women in Digital Space at the federal level.
There have been some important related work from Data Science approaches but mainly
on cyberbullying and in the detection of language patterns through supervised algorithms,
through social network features, through profile information, a few works on unsupervised
learning, and on violence against women. However, it is important to tackle Digital Violence
Against Women as a phenomenon with its particularities and separated from cyberbullying.
Moreover, it is necessary to study this phenomenon from a gender perspective since all crimes
against women are contained by a gender symbolic structure.
The hypothesis of this Thesis Project is that Data Science approaches such as Text Mining,
Supervised Learning, Time Series Analysis, Natural Language Processing, and Network
Analysis can find associations between proposed variables of Spanish-language text data from
microblogging social network, Twitter, datasets. The goal of this thesis is to implement Data
Science techniques to analyze the Digital Violence Against Women phenomenon in order
to achieve the identification of major associations that will let us understand and counteract
violent social discourses and structural violence in digital space. The proposed model is composed
of several techniques such as Time Series Analysis, Natural Language Processing, and
Network Analysis, that are fed by the outcomes of the ensemble between Supervised Classifiers
and an Ontological Matcher.
Results indicate a higher presence of Digital Violence Against Women for the predicted
tweets under the ensemble algorithm in comparison with just the Supervised Learning Algorithms
or just the Ontological Matcher. Time Series Analysis shows peaks in Digital Violence
Against Women in dates that correspond to days in which the fight for Women’s Rights
was positioned. Natural Language Processing confirms the existence of a violent semantic
discourse under this phenomenon. And, Network Analysis exhibits generalized individual
attacks connected to a structural and systemic problem. Finally, there were four strategies
proposed to counteract Digital Violence Against Women, which are based on detection, prevention,
and specificity of the phenomenon.