The violence against women, bases in the inequity of opportunities in every social stratum. According to national surveys, women and girls are considered a vulnerable population due to the inequalities in access to essential services such as education, economic independence and technology. Currently, governments show interest to address this problem and provide parity of opportunities to contribute to the development of society. Therefore, several social studies analysed the situation of women and their impact on welfare indicators regarding the development of the population, in this context, some techniques propose solutions through Data Mining to measure and recognise possible discrimination and violence. The thesis work intends to develop data mining models to identi ed discrimination mainly towards women and girls, taking into account the environmental factors such as individual, community, social and institutions.
The World Health Organisation de nes gender violence as behaviour towards a
person, which caused physical, psychological or sexual harm. Around the world,
one out of every three women over 15 years old, is a victim of physical or verbal
aggression [
The relationship between discrimination and violence has been de ned by
[
However, most of social studies seek global di erences rather than analysed gender attribute in isolation, also gender discrimination can appear in speci c contexts, because of this, registries from discrimination facts are scares and comes from di erent references, complicating the analysis.
In contrast, the use of social networks and digital devices (mobile phones,
banking transactions, etc.), leave a trace which records the behaviour of people
? Supported by Universidad del Pac co-Early Stage
[
For instance, the notion of ubiquity shows inequity can be compare on the
level of mobility in men and women. It is not possible to a rm that the role of
a housewife is rooted in a particular geographic space, but through studies of
trajectories, we can see the distances that women travel with respect to men are
considerably smaller [
This context leads us to pose the following question: >How to measure gender discrimination in society through digital records?.
This article is organised as follow: The state of the art gathers research antecedents from social sciences and data sciences, to identify phenomena and factors related to violence; the proposal and approximations regarding the state of the art, the methodology to validate the hypothesis; so far, the results show information about environmental factors and nally the discussion about the di culties that this research face it. 2
Our research lines has two main groups, the social sciences and the data sciences.
In this context, International Institutions are interest to address this problem on
detail. They develop indicators about environment factors, which might arise the
persistence of violence in emerging societies or not. [
Another view, mention that lifestyle have an in uence of men who report
having perpetrated physical violence towards their couple throughout his life
[
They concluded, that it is necessary to understand how inequity a ects the
development of societies, the di culties to face these problems and the factors
predisposing to disparity [
Although most of these studies propose indicators to face inequity [
The measures to determine discrimination, in general, have not been fully
developed by the data sciences, although, the Discrimination-Awareness Data mining
(DADM) [
As indirect discrimination is not explicit in electronic digital records, to infer
direct discrimination it is necessary to know the external and individual factors
of the study sample (sex, race, pregnancy status, age, ethnicity, religion, state
civil). Thus, some research papers [
The thesis work proposes two goals within the framework of state-of-the-art:
First, to develop models to measure discrimination based on digital records.
The guiding is the DADM, to build classi cation rules that recognise direct and
indirect discrimination, as long as there will two elements: the class that de nes
a discriminating rules and the context to validate the elements of the rule are
discriminant, as shown in Figure 1 [
Because discrimination records are scare, we include historical information
from two di erent periods and various geographical spaces. According
state-ofart, the rules generated from databases with a known class type, provide enough
information to classi ed the rules into potentially discriminant (PD) and
nondiscriminating(PND). However, is not the case of our data, so it is necessary
to develop new strategies to determine the class. These rules are relationships
and will add quality to the results like as co-occurrences, which means that it
is likely they would be related to social context. The second goal, refers to the
validation of the models carried out in the rst stage through socio-demographic
context. However, it would be necessary to demonstrate that these variables are
su cient or in any case, to experiment and applying engineering features.
1 Direct discrimination: explicit, impose barriers between a group. Ex. Ethnicity:
indigenous, History: Good Credit: Poor
2 Indirect discrimination: not explicit, but impose barriers between a group (conscious
or unconscious). Ex. Provenance: Rural, History: Good Credit: Bad; if it is known
that the rural population has a high percentage of indigenous people.
The KDD (Knowledge discovery databases) process of extracting patterns from
databases is composed of four stages [
The rst approach to the fusion of social sciences to data sciences for this work,
was the prepossessing of qualitative information extracted from interviews with
university females students [
The research mentioned above, aims to know which agents were involved
in some episodes of discrimination within university atmosphere. To this end,
a survey of open questions about events of discrimination was developed, such
as: "Did you ever hear in the university environment mentioning that women
are di erent concerning to their professional performance? Mention what you
heard and the person who said it". This survey was formulated by social experts
who manage the variables according to the-state-of-the-art mentioned in [
In this proposal, the propose method named A0 are more likely to contain sets of di erent words related to the topic. In contrast, Topic Modeling methods are memory expensive compared to methods based on TF-IDF. We conclude that the process A0 is e cient in the extraction of relevant words, optimising the retrieval information for qualitative research in simple and complex data. However, the e ciency of A0 is linked to the improvement of data pre-processing techniques.
The sample for that work is small (214 records). Although the extracted testimonies may represent the discrimination su ered by the students of that school, it is not possible to generalise what happens with all the women in the same space. Due to this, the decision was made to collect structured information available in di erent resources and to unify it to be processed later.
For this article, databases were compiled related to the individual and environmental factors that in uence gender violence. As seen in Tables 1, 2 and 3, each factor mentioned by the studies described in Section 2, it corresponds to a series of les that contain information related to the description of the variables.
The description of this data has been crucial to understand how the individual, community, social and institutional factors are related, as well as the recognition of variables between the automatic and socio-demographic information.
For instance, suppose that the amount of monthly expenditure sustains the purchasing power of the people and is de ned by the type of work that they have. A well-paid job requires specialised skills and a high degree of training or education. Although it is not possible to assure that there is a relationship between educational level and the amount of monthly expenses in our data. As Figure 2 shows, inequality in educational level between men and women from 0 to 89 years (y axis) in Peru in 2016, the green bars represent the data of women 3 Source code: https://github.com/bitmapup/violencePatterns who, as observed, have a numerical di erence with respect to their male pairs at each level of the x axis (blue bars). In contrast, Figure 5 shows population Factor Vfiaocletonrce Name of datasets Columns Records wFeiigleht tDyapteas Concatleenated Individual IWndomivaidnual ab..rreecch00151 ab.. 75 ccoolluummnnss ab.. 3354302002 ab.. 11..93 MMBB ab.. iinntt6644((64)),,oobbjejecct(t1(1)) CRWoeecliuogmrhdtns:s:2:3.8150M84B Home Community ab.. rreeccv081411 ab.. 752ccoolulummnnss ab.. 3334106082 ab.. 118.3.2MMBB ab.. inota6t46(44()7,1o)b,joebcjte(c1t)(1) CRWoeecliuogmrhdtns:s:5:1.68165M43B Table 1. Characteristics of the ENDES datasets associated with gender violence factors Violence Factor
Name
Weight
Columns Records of the le Institutional Chapter100 37 columns 1177 Data Concatenated types le
Weight: 285.1 MB 340.4 MB int64(28), object(3) Records: 1177
Columns: 31
Where n is the total of transactions and P u is the accumulated average monthly
expenditure per user; class indicates the social class associated with that type of
behaviour and monthly expense amount. This research will not speci cally deal
with the veri cation of this formula, but it is necessary to point out that in [
In contrast to DADM studies so far, information used in the thesis proposal is varied and voluminous, coming from di erent sources of known space-time contexts, which in some way allows knowing the continuity of particular phenomena (prediction of events). The discrimination metrics proposed by the social sciences were formulated from multivariate analysis and classical statistics with some aspects and factors. In this research, the maximum number of possible variables that could or could not appear according to the proposed model will be used simultaneously, forming strong association rules automatically and objectively. So, is recommendable begin through the de nition of discrimination measures in vast and varied information, that contributes to social research in the search for explanations of social events such as gender violence.