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<= !--[if gte vml 1]> https://doi.org/10.37815/rte.v35n2.1055

<= span lang=3DES>Artículos originales

 

Análisis de Clusterización en Datos de Encuestas sobre Ciberacoso

Clustering= Analysis on Cyberbullying Survey Data

 

César Vinueza-&Aacut= e;lvarez1 https://orcid.org/0009-0001-6731-3314,= María Inés Acosta-Urigüen1

https://orcid.org/0000-0003-4865-2983, Juan Fernando Lima1 https://orcid.org/0000-0003-3500-<= span style=3D'letter-spacing:-.2pt'>3968

 

1Departamento de Posgrados, Universidad de= l Azuay, Cuenca, Ecuador

cvinuezamsn@es.uazuay= .edu.ec

 =

2Laboratorio= de Investigación y Desarrollo en Informática (LIDI),

Universidad<= span style=3D'letter-spacing:-.05pt'> del Azuay, Cuenca, Ecuador

macosta@uazuay.edu.ec, flima@uazuay.edu.ec

 =

= .

 

 

<= /span>

Esta o= bra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0

Enviado: 2023/07/15 Aceptado: 2023= /08/22 Publicado: 2023/10/15

 

Resumen

Hoy en día, el ciberacoso se ha incrementado d= ebido al crecimiento y diversificación de tecnologías, resultando difícil una detección temprana en distintos sector= es, como en la academia. Por este motivo, esta problemática= requiere abordarse mediante métodos específicos de análisis de datos que permitan identificar su caracterización. El presente estudio se enfoca en la detección del ciberacoso en los estudiantes de una universidad de la ciudad de Cuenca, Ecuador, aplicando minería de datos para analizar la información obtenida de un cuestionario p= sicológico sobre el ciberacoso con aspectos sociodemográficos, y a través de la escala de Likert llegar a categorizar el nivel = de victimización y de agresión entre adolescentes al momento de utilizar dispositivos electrónicos. Mediante el uso del algoritmo de agrupamiento se evalúa y se obtienen patrones de comportamiento de l= os participantes de la encuesta del ciberacoso donde se identificaron a los actores y los sectores académicos con mayor incidencia.

 

Palabras clave: Ciberacoso, Entorno<= span style=3D'letter-spacing:-.15pt'> académico, Victimización <= /span>encuesta, Agrupamiento.

 

 


 

Escuela Superior Polit= écnica del Litoral, ESPOL

 

<= /span>    = ;            &n= bsp; 158  César Vinueza-Álvarez, = María Inés Acosta-Urigüen, <= /span>Juan Fernando Lima

 

Abstract

Nowadays, cyberbullying has increased due to the growth and diversification of technologies. This has made early detection difficult in different sectors, such as academia. For this reason, this issue requires addressing through specific data analysis methods that allow its characterization to be identified. This= study focuses on the detection of cyberbullying in students of a university in the city of Cuenca, Ecuador, applying data mining to analyze the information obtained from a psychological questionnaire on cyberbullyi= ng with sociodemographic aspects and through the scale of Likert to categorize the level of victimization and aggression among adolescents when using electronic dev= ices. Using the grouping algorithm, the behavior patterns of the participants in the cyberbullying survey are evaluated and obtained, and the actors and academic sectors with the highest incidence are identified.

 

Keywords: Cyberbullying, Acade= mic domain, Victimization survey, Clustering.

 

 

Introducción

El acoso, también conocido = como “bullying”, ha sido uno de los temas que más se presenta= n a nivel académico, preocupando a los educadores, psicólogos e incluso a los padres de familia, debido al incremento del acoso sin present= ar una distinción de edad, nacionalidad o género. Por lo tanto, llega a afectar a la sociedad trayendo consecuencias negativas en todo ámbito donde se presente (Garmendia Larrañaga et al., 2019).<= /span>

 = ;

El avance constante de la tecnología en el mundo moderno, así como la difusión y= uso de las redes sociales= , facilita el acoso constante a las víctimas por parte de los atacantes. Siendo así el surgimiento de un nuevo término denominado ciberacoso o “cyberbullying”, que no es más que trasladar el acoso físico a los distintos medios digital= es (Hosseinmardi et al., 2014). En general, el ciberacoso es considerado una amenaza psicológ= ica para los niños y adolescentes, = que hacen uso de dispositivos electrónicos, debido a que pueden afectar negativamente aspectos psicológicos, conductual= es, físicos y académicos en la víctima. Asimismo, se presentan ansiedad, autoestima baja, dependencia del alcohol y drogas, reducción de autocontrol, pensamientos suicidas y pés= imos resultados académicos tanto en el atacante como en la víctima (Johnson, 2022).

 

Por este motivo, es necesario enco= ntrar distintas alternativas para que las autoridades competentes puedan identificar a las víctimas del ciberacoso,<= span style=3D'letter-spacing:-.6pt'> que son constantemente acosadas por lo= s atacantes (Moya-Solís & Moreta-Herrera, 2022). Para lo cual, la minería de datos es una alternativa robusta para encontrar patrones de comportamiento y llegar a reducir el ciberacoso con la identificación de los actores, mediante el uso de distintas técnicas como clasificación, asociación, clasificador bayesiano inge= nuo (Naive Bayes), árbol d= e decisión, bosques aleato= rios (Random Forest), entre otras (Pamuji & Setiawan, 2022). Por eso, para e= sta investigación, se propone el análisis de los datos provenient= es de un cuestionario de ciberacoso mediante el algoritmo de Partici&oac= ute;n Alrededor de Medoids (PAM) que minimiza el resultado de la suma de las diferencias entre los objetos de un grupo y el centro de ese grupo (Botyarov & Miller,<= span style=3D'letter-spacing:-.75pt'> 2022). Para lograr esta propuesta, = es necesario analizar las respuestas del cuestionario de ciberacoso mediante el algoritmo PAM, para llegar a detectar aspectos característicos = donde se presentan los ciberacosos para los estudiantes universitarios de distintas carreras.<= /span>

 

El resto del artículo se estructura de la siguiente manera: la Sección 2 presenta los trabajos relacionados; la metodología del trabajo es presentada en la Sección 3; en la Sección 4 se muestran los resultados; y fina= lmente, en la Sección 5 se presentan las conclusiones y los trabajos futuros= .

 


<= /o:p>

 

Escuela Superior Polit= écnica del Litoral, ESPOL

<= span lang=3DES style=3D'font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:R= oboto; mso-hansi-font-family:"Times New Roman"'>

Análisis de Clusterización en Datos de Encuestas sobre Ciberacoso   159        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;        <= /span>

 

Trabajos Relacionados=

Existen estudios donde se presentan los patrones que conducen al acoso y al ciberacoso, que conducen a la generación de modelos para solucionar esta problemática social. En consecuencia, los principales desarrollos de= las investigaciones se centran en la detección, clasificación y predicción del ciberacoso en diferentes sectores sociales. A continuación, se presentan los trabajos relacionados a las técnicas utilizadas para la detección del acoso y el ciberacoso.

 

Los autores Sitnik-Warchulska et a= l. (2021) demuestran que utilizar redes bayesianas llega a modelar comportamientos de riesgo de acoso entre los escolares, así como la identificación de los actores que están involucrados. Por lo tanto, usar redes bayesianas que permitan visualizar y modelar las relaciones entre distintas hipóte= sis y variables, para cuantificar la incertidumbre en las inferencias basadas en el análisis de datos estadístic= os, permite predecir conductas de acoso. <= /span>Además, Sitnik-Warchulska et al. clasifican y predicen los estados y eventos del ac= oso aun cuando los datos son parciales e inciertos.

 

Mientras tanto, Meliana et al. (20= 19) identifican el ciberacoso en las conversaciones estudiadas de la red social= X (Twitter) mediante los métodos de agrupación. Estos autores u= san la minería de datos en esta red social para obtener los resultados y= su correlación con la identificaci= ón del ciberacoso. Además, en este estudio se utilizó el método de clasificación Naive Bayes y árboles de decisión (Decision Tree J48), los cuales identificaron exitosamente un<= span style=3D'letter-spacing:-.5pt'> 92% y un 100% respectivamente. Asimismo, los autores concluyen que la red social que presenta un alto ciberacoso es Twitter, relacionado con acosos psicológicos y violentos.

 

Por otro lado, Farag et al. (2019)= , en un estudio realizado, concluyen que es necesario utilizar diferentes métodos de análisis de datos para cada una de las redes sociales, debido a cómo entregan los datos estas plataformas. Por lo tanto, proponen utilizar seis métodos para analizar los datos recolectados de las redes sociales, como aprendizaje no supervisa= do, codificadores automáticos, aprendizaje profundo, aprendizaje semi supervisado, modelado de series de tiempo y clusterización. Además, señalan que l= os algoritmos de aprendizaje no supervisados están ganando una mayor atención por parte de la comunidad científica en este tema, debido a presentar un alto grado de eficiencia al momento de analizar los datos.

 

Los autores Parime & Suri (201= 4) analizan el ciberacoso, cuáles son sus impactos en la sociedad y sus principales características, por lo que utilizan técnicas de minería de datos para entender el comportamiento que presentan los acosadores y así prevenir aquellos actos. Por lo tanto, Parime & Suri analizan los sentimientos a través del procesamiento del lengua= je natural para identificar la presencia o ausencia del ciberacoso en una red social, a través de un conjunto de datos.

 

Mientras que, en otro estudio, rea= lizado por Zhang (2021), se propone un método que permite identificar la presencia de términos de ciberacoso y clasificar las actividades de acoso en las redes sociales como Flaming. Además, expone cuáles son los procesos para detectarlo, tales como el pre-procesamiento de los datos, extracción de características, algoritmos de aprendizaje y técnicas de clasificación Naive Bayes. De esta maner= a, es posible detectar el ciberacoso en las redes sociales con la lógica di= fusa que proporciona una ayuda para tomar decisiones dsobre los contenidos antes que los usuarios sean víctimas.

 

 

 


<= /o:p>

 

Revista Tecnológica Espol - RTE Vol. 35, N° 2 (Octubre, 2023) / e-ISSN 1390-3659

 

<= /span>    = ;            &n= bsp; 160  César Vinueza-Álvarez, = María Inés Acosta-Urigüen, <= /span>Juan Fernando Lima

 

Los autores Dedic et al. (2019) analiza= n los datos obtenidos con tres tipos de encuestas estandarizadas (“The Juvenile Victimization Questionnaire”,<= span style=3D'letter-spacing:-.25pt'> “The Defense Style Questionnaire”= ; y “Beck Depression Inventory= 221;) realizadas a estudiantes del Sur de Australia, Gran Bretaña y Suecia a través de una prueba no paramétrica denominada Kolmogorov-Smirnov que determina la bondad del ajuste de dos distribuciones de probabilida= d entre sí. Dedic et al. llegan a detectar los factores primordiales para cometer suicidios ocasionados en la niñez por haber sido víctimas de abusos sexuales de los compañeros.

 

Los estudios mencionados describen varias técnicas y modelos para la detección y predicció= ;n tanto del acoso como del ciberacoso. Presentan limitaciones al momento de detectar y predecir e= sta problemática ya que, si bien los estudios tienen un enfoque psicol&o= acute;gico para el acoso y el ciberacoso, = no llegan a la aplicación de técnicas de clusterización de los datos proporcionados con las distintas pruebas psicológicas.

 

Materiale= s y Métodos

La metodología empleada durante el proceso de aplicación de técnicas de clusterización de datos de una encuesta sobre el ciberacoso estuvo conformad= a por seis actividades detalladas en la Figura 1. Para estas actividades se utiliza= ron como entrada los datos provenientes de las encuestas psicológicas so= bre el acoso y el ciberacoso que fueron realizadas.

 

Figura 1

Actividades relacionadas a la metodología utilizada en el estudio


Recopilaci= ón de los datos

Esta fase inicial está primordialmente enfocada en la comprensión del acoso y el ciberacoso como un problema social actual, donde la implementación de una encuesta psicológica permite una clara detección, a través de los datos que esta herramienta brinda. Los roles que se presentan en esta fase, acorde a la Figura 1, son:

 


<= /o:p>

 

Escuela Superior Polit= écnica del Litoral, ESPOL

<= span lang=3DES style=3D'font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:R= oboto; mso-hansi-font-family:"Times New Roman"'>

Análisis de Clusterización en Datos de Encuestas sobre Ciberacoso   161        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;        <= /span>

 = ;

•      =    Encuestador: aquella persona que está encargada de aplicar los cuestionarios, escuchar, plantear las preguntas y registrar las respuestas de las personas entrevistadas.

•      =    Experto: persona que= tiene gran experienci= a en<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 03%; letter-spacing:-.25pt'> el<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 03%; letter-spacing:-.25pt'> ámb= ito psicol&oac= ute;gico y brinda soporte a la estructura de la encuesta.

•      =    Director del proyecto: aquella persona que dirige el estudio brinda apoyo y conocimiento de técnicas y métodos para evaluar la información y a su vez descartar la que no sea confiable.<= o:p>

•      =    Investigador: es quien está encargado de la construcción y definición de cada una de

las fases de la investigación que permitan una correcta gestión del proyecto.

 

Estudio= y comprensión de los datos

En esta fase se realizó la recopilaci&o= acute;n inicial de los datos y las tareas de tratamiento de la información obte= nida de las encuestas. Se llevó= ; a cabo la identificación de las características de los datos que puedan ser de interés para el estudio, que a simple vista no se pueden observar. Por lo tanto, en la fase de entendimiento de datos se focalizó en el análisis de las variables recolectadas por la encuesta de ciberacoso; esta incluye dos partes, la primera fue asociada a variables demográficas y la segunda parte fueron las variables propi= as de la encuesta.

 

Entre las variables demográ= ficas se incluyen aspectos como género, edad, lugar de nacimiento, estado civil, tipo de colegio, lugar de trabajo, entre otros. Mientras que las variables propias de la encuesta recolectaron valores basados en una escala de Likert, para medir el ciberac= oso a través del uso de dispositivos electrónicos más comu= nes.

 = ;

Los= datos = recolectados, que se usaron en esta investigación, corresponden a las encuestas realizadas en los meses de mayo y junio del 2022, a estudiantes entre 18 y 22 años= de edad de una universidad. Cada pregunta realizada se calificó dentro de una escala ordinal para obtener la frecuencia de victimizaci&o= acute;n, los actores del acoso y patrones de comportamiento que puedan presentarse en la encuesta (Useche et al., 2021).

 = ;

An&aacu= te;lisis de los datos y selección de características

Dentro de esta fase fue necesaria la limpieza y transformación de datos, debido a que los datos exist= entes se presentaron en diferentes formatos, incoherencias, = así como datos faltantes o incorrectos.

 = ;

Los datos recolectados en la encue= sta corresponden a un grupo de adolescentes con una edad entre los 18 y 22 años, con un nivel socioeconómico medio alto, que asisten a u= na universidad de la ciudad de Cuenca, Ecuador. Se aplicó la encuesta considerando una poblaci&oa= cute;n de 7,000 estudiantes con<= span style=3D'letter-spacing:-.6pt'> un nivel de confianza del 99% y un margen de error del 5%. Con estos parámetros, el tamaño de la muestra resultante es de 607 encuestas tomadas, que se dividen en 286 mujeres y 316 hombres, siendo solo en 5 encuestas donde prefirieron no responder. Dentro de esta muestra resultante no se encontraron valores faltantes, por lo que se pudo considerar como una muestra completa.<= /p>

 

Análisis exploratorio

En<= /span> este análisis de los datos se observaron las variables correspondientes a los resultados de la encuesta. Como se mencionó, de las 607 encuestas realizadas, cada una de ellas contiene 52 variables y se divide en dos secciones. La primera sección contiene varia= bles de información sociodemográfica de los alumnos y preguntas de encuesta en escala so= bre el ciberacoso, referentes a la victimización y la agresión. En cambio, en la segunda sección de la encuesta, las respuestas est= aban relacionadas con una escala de Likert que consta de cuatro valores cuyos identificadores son &#= 8220;nunca”, “algunas veces”, “bastantes veces”, “muchas veces”.=

 


<= /o:p>

 

Revista Tecnológica Espol - RTE Vol. 35, N° 2 (Octubre, 2023) / e-ISSN 1390-3659

 

<= /span>    = ;            &n= bsp; 162  César Vinueza-Álvarez, = María Inés Acosta-Urigüen, <= /span>Juan Fernando Lima

 

Dentro de las variables de la encu= esta existe una variable numérica correspondiente a la edad del encuestad= o; se analizó su distribución y los valores atípicos que pueda presentar. Como se observa en la Figura 2, la variable edad sigue una distribución normal y muestra valores atípicos que han sido tratados por un proceso de imputación de reemplazo por el límite superior.

 

Figura 2

Distribución de la edad de los encuestados


 

Sin embargo, el resto de las variables fueron definidas como categóricas, determinadas por el porcentaje de ca= da categoría. Por lo tanto, aquellas categorías que no llegaron = a un 5% de los valores fueron reasignadas a otras categorías, siendo un proceso lógico de agrupación. Como se observa, la Tabla 1 presenta un ejemplo de una variable categórica en conjunto con la cantidad de respu= estas y su correspondiente porcentaje.

 

Tabla 1

Ejemplo de la variable categórica con su respuesta y porcentaje

 

VARIABLE=

CATEGORIA

RESPUESTAS

PORCENTAJE (%)

 

 

=  

“He amenazado a alguien para meterle miedo”=

= Nunca=

552=

= 90.93=

Algunas veces

45=

= 7.41=

Bastantes veces

3

= 0.49=

Muchas veces

3

= 0.49=

No contesta

4

= 0.65=

 

De esta manera, se puede observar que en la Tabla 1, las categorías “Bastantes veces”, “Muchas veces” y “No contesta” no llegan a superar = el 1% de las respuestas del grupo entrevistado. Por lo tanto, estas categorías fueron agrupadas= para el proceso de generación del clúster. Este proceso fue repetido en todas las variables categóricas de la encuesta.

 

Selección<= span style=3D'letter-spacing:-.25pt'> de variables

Par= a la selección de las variables se utilizó el algoritmo de Stepwise (Görzig & <= span lang=3DES style=3D'letter-spacing:-.1pt'>Frumkin, 20= 13) sobre un proceso de regresión logística para brindar una relación entre una variable dep= endiente respecto de otras variables independientes. Por lo tanto, en este algoritmo se realiza una construcción= iterativa, paso a paso, de un modelo de regresión, que implica la selección de variables independientes, que se utilizan en un modelo final. En este estudio se usó la regresión logística ya que se requiere determinar si una persona sufrió o no ciberacoso; en otras palabras, las variables de interés para este estudio son dicotómicas (toman dos valores: sí o no). De este

 

 


 

Escuela Superior Polit= écnica del Litoral, ESPOL

<= span lang=3DES style=3D'font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:R= oboto; mso-hansi-font-family:"Times New Roman"'>

Análisis de Clusterización en Datos de Encuestas sobre Ciberacoso   163        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;        <= /span>

 = ;

modo, la aplicación del modelo de regresi&oacu= te;n logística permitió construir una función que se basa e= n el cálculo de la probabilidad donde la variable de interés adopt= a el valor del evento previamente definido a través de la ecuación= y =3D ln(p/(1-p)), donde p es la probabilidad que ocurra un evento Y, que implica a los coeficientes de los términos constantes y variables, dependientes e independientes.

&nbs= p;

Así, la nueva variable dependiente generada, que se va a estimar, puede llegar a tener cualquier valor dentro de un rango. En este sentido, al aplicar el modelo de regresión logística implica agregar o eliminar variables explicativas potenciales en sucesión y probar la significancia estadística después de cada iteración (Sultan et al., 2021). Por ende, se utilizaron todas las variables = numéricas y categóricas en el proceso de selección de variables, y además, la variable de interés para determinar la significancia final es “Personas que han recibido acoso= 221;. En la Tabla 2 se presentan las variables con mayor valor significativo.

 

Tabla 2

Selección de variables y sus valores de significancia

 

VARIABLE=

ESTIMACIÓN<= /b>

Z

PROBABILIDAD

“Ciclo”=

= 0.10390=

= 1.664=

= 0.09614=

“Celu= lar insultado riculizado”

= 27.85917=

= 0.012=

= 0.99043=

“Celular obligado a hacer cosas que no quería dinero = hacer tareas”

 

= 17.58782=

 

= 0.004=

 

= 0.99645=

“Celular contado mentiras rumores sobre mí&= #8221;

= 1.18124=

= 2.407=

= 0.01607=

“Celular han pasado manipulado fotos videos de mí sin mi permiso”

=  

= 2.20479=

=  

= 2.837=

=  

= 0.00455=

“Internet me han insultado o ridiculizado”

= 1.33325=

= 1.91=

= 0.05612=

“Internet han contado mentiras o ru= mores sobre mí”

= 0.29532=

= 0.962=

= 0.33594=

“Móvil internet he obligado a alguien a hacer cosas = con amenazas”

=  

= 19.37827=

=  

= 0.009=

=  

= 0.99283=

“Móvil internet he mentido en el Messen= ger o en cuentas privadas de otros”

=  

= 1.20013=

=  

= 0.937=

=  

= 0.34889=

“Cree= alguna vez haber sufrido burlas o acoso en su lugar= de estudio”

=  

= 1.82379=

=  

= 7.036=

=  

1.98E-12

 

Definici&o= acute;n y aplicación del modelo

En esta fase, se aplicó la técnica de agrupamiento para identificar el patrón de comportamiento de quienes cometen acoso, detectando los grupos que pueden variar según diversos criterios demográficos. Una vez que se tuvieron las variables seleccionadas, se procedió a la aplicación de la clusterización de los elementos que contienen las clases de objetos similares. Esta técnica descriptiva intenta reconocer patrones similares dentro de un conjunto de datos, llegan= do a ser un algoritmo de aprendizaje no supervisado (Carballido et al., 2018). Sin embargo, todas las variables utilizadas son de tipo categórico, por lo= que se debe utilizar un proceso de cálculo de distancias que permita calcular estos valores para las variables categóricas y luego puedan= ser usadas en un algoritmo adecuado para variables categóricas.

 


<= /o:p>

 

Revista Tecnológica Espol - RTE Vol. 35, N° 2 (Octubre, 2023) / e-ISSN 1390-3659

 

<= /span>    = ;            &n= bsp; 164  César Vinueza-Álvarez, = María Inés Acosta-Urigüen, <= /span>Juan Fernando Lima

 

Pues así, en este estudio se utilizó la distancia de Gower que puede medir las diferencias de dos registros distintos (Meaney & Fikes, 2023). Estos registros pueden presentarse como una combinaci= ón de datos lógicos, categóricos, numéricos o texto. Para obtener la distancia de Gower se realiza un promedio de l= as diferencias parciales entre los individuos.

&nbs= p;

Des= pués de obtener las distancias, <= /span>se procedi&oacu= te; a determinar cuál es el númer= o de clústeres óptimos para los individuos que realizaron la encuesta. Para ello, se utilizó una métrica que calcula la calidad del agrupamiento realizado con algoritmos de clusterización denominada coeficiente de silueta. Esta métrica oscila sus valores entre 1 siendo el mejor agrupamiento y -1 represent= ando un mal agrupamiento. Además, los valores que llegan a ser cercanos a 0, indican que el<= span style=3D'letter-spacing:-.5pt'> agrupamiento está su= perpuesto o indiferente. De esta manera, el primer resultado para la selección de variables demostró que el número óptimo de agrupamiento para el grupo encuestado es dos clústeres, como se puede apreciar en la Figura 3.

 

Figura 3

Número óptimo de clústeres


 

Una vez que se estableció el número de clústeres, se aplicó el algoritmo de asignación del cl&uacut= e;ster para cada uno de los individuos. Se empleó la distancia de Gower, para encontrar las medidas entre los sujetos estudi= ados; y se utilizó el algoritmo PAM, para la generación de los clústeres. Este algoritmo encuentra una secuencia de objetos denominados medoides que están localizados en la parte central del clúster (Schubert & Rousseeuw, 2021) y con base en estos medoides, los individuos son asignados a cada uno de los clústeres definidos.

 

 

Resultados

En esta parte se validó el análisis de los clústeres realizados para luego evaluar los resultados obtenidos. Para lo cual, se elaboraron varios modelos que poseen= la suficiente información desde una perspectiva de análisis de datos, de tal modo que se puedan analizar y verificar los resultados de man= era efectiva en relación con las variables demográficas. Por lo tanto, se asignaron los<= span style=3D'letter-spacing:-.45pt'> clústeres a cada uno de los individuos de la encuesta, y se pudo observar que la carrera de Estudios Internacionales, que se imparte en la universidad de estudio, presentó un alto índi= ce de acoso y ciberacoso frente a otras carreras. Esto se puede observar en la Figura 4, que ofrece una perspectiva de un incremento de los niveles de acoso con una tendencia hacia el rojo.

 


 

Escuela Superior Polit= écnica del Litoral, ESPOL

<= span lang=3DES style=3D'font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:R= oboto; mso-hansi-font-family:"Times New Roman"'>

Análisis de Clusterización en Datos de Encuestas sobre Ciberacoso   165        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;        <= /span>

 

Figura 4

Nivel de acoso y ciberacoso por carrera


 

Del mismo modo, se puede visualizar por ciclo el índice de acoso y ciberacoso con una marcada tendencia hacia el rojo, como se observa en la Figura 5. Así, el sexto ciclo fue dond= e se presentó un mayor índice de acoso, seguido del cuarto y segundo ciclo, pero el octavo ciclo tiene un menor grado de acoso.

 

Figura 5

Nivel de acoso y ciberacoso por ciclo académico


 

Continuando con el análisis de los resultados, lo primordial fue determinar si la variable de interés está claramente identificada e= n algún clúster. Por lo tanto, las personas que recibieron acoso o ciberacoso fueron representadas con un valor de uno, y a aquellos estudiantes que estuvieron absueltos del acoso o ciberacoso se los representó con un cero, como se puede observar en la Figura 6.

 

Siendo así, un 29.49% de la cantidad de estudiantes encuestados habían recibido ciberacoso, por lo que se pudo identificar las características que se presentaron en este grupo de estudiantes. Dado que la variable de interés identifica claramente dos características dicotómicas (sí/no), los dos clústeres obtenidos fueron suficientes para obtener una conclusión relevante para el estudio.

 

 

 

 


<= /o:p>

 

Revista Tecnológica Espol - RTE Vol. 35, N° 2 (Octubre, 2023) / e-ISSN 1390-3659

 

<= /span>    = ;            &n= bsp; 166  César Vinueza-Álvarez, = María Inés Acosta-Urigüen, <= /span>Juan Fernando Lima

 

Figura 6

Cantidad <= /span>de personas que recibieron ciberacoso


 

Sin embargo, para brindar un análisis con mayor detalle, fue necesario emplear cada una de las variabl= es utilizadas en el modelo con la designación del clúster para l= os individuos del estudio. Para ello, se enfocó en las características de la agrupación de los dos clústeres utilizando gráficas porcentuales para su análisis respectivo.

 

Una de las primeras variables que = se analizó fue las carreras de la universidad que presentaron un mayor índice de ciberacoso<= span style=3D'letter-spacing:-.3pt'> en base a las características de la agrupación de los clústeres. Por lo tanto, existe una mayoría relativa en ambos clústeres con las carreras de Estudios Internacionales, Derecho= y Ciencias de la Computación como se puede observar en la Figura 7.

 

Figura 7

Porcentaje = de ciberacoso por carreras


 

Debido a la existencia de una mayoría relativa del ciberacoso en las carreras, como se presenta en= la Figura 7, fue necesario conocer en cuáles ciclos lectivos se presenta una mayor incidencia del ciberacoso en la universidad de estudio. La tendencia que se presentó en el análisis de esta variable fue que los estudiantes del sexto ciclo presenciaron más ciberacoso que el resto de ciclos lectivos estudiados, como se puede observar en la Figura 8.

 

 


 

Escuela Superior Polit= écnica del Litoral, ESPOL

<= span lang=3DES style=3D'font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:R= oboto; mso-hansi-font-family:"Times New Roman"'>

Análisis de Clusterización en Datos de Encuestas sobre Ciberacoso   167        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;        <= /span>

 

Figura 8

Porcentaje = de ciberacoso por ciclos lectivos


 

 

Conclusiones

El<= /span> presente estudio sobre la detección del ciberacoso en los estudiantes de una universidad de la ciudad de Cuenca, Ecuador, a través de la clusterización de l= os datos provenientes de las encue= stas psicológicas, evidencia un claro panorama de la existencia de cibera= coso como un problema social y psicológico para los estudiantes. Gracias a los avances en inteligencia artificial y en minería de datos, se puede detectar e identificar el ciberacoso de forma acertada para afrontar esta problemática.

 

Por esta razón, al aplicar = la minería de datos se obtienen resultados que son evidentes y se ajust= an a la realidad de la problemática. Siendo así, este estudio fue analizado con el algoritmo de agrupamiento, separando cada una de las categorías de la encuesta y se obtuvieron comportamientos de los encuestados en cada pregun= ta analizada. Por lo tanto, los resultados demuestran claramente que la carrer= a de Estudios Internacionales es donde se presenta una mayor tasa de estudiantes implicados en el ciberacoso, con un 25.23%. Asimismo, en el sexto ciclo lectivo se detectó que un 42.86% de los estudiantes re= ciben ciberacoso en la universidad de estudio.

 

En trabajos futuros, se puede anal= izar el ciberacoso que reciben los estudiantes, brindando más información a la clusterización para identificar características particulares de esta problemática social. De igual forma, se puede realizar el mismo procedimiento para trabajar en otras problemáticas sociales y psicológicas como la violencia contra la mujer, y llegar a establecer las posibles víctim= as de femicidio e incluso evitarlo de antemano.

 

 

Reconocimientos

Este trabajo ha sido financiado parcialmente por el Vicerrectorado de Investigaciones de la Universidad del Azuay, y forma parte de la Tesis titulada Aplicación de técni= cas de clusterización en datos provenientes de la aplicación de u= na encuesta de cyberbullying. Los autores desean expresar su agradecimiento al Laboratorio de Investigación y Desarrollo en Informática por = su participación en el trabajo.

 


 

Revista Tecnológica Espol - RTE Vol. 35, N° 2 (Octubre, 2023) / e-ISSN 1390-3659

 

<= /span>    = ;            &n= bsp; 168  César Vinueza-Álvarez, = María Inés Acosta-Urigüen, <= /span>Juan Fernando Lima

 

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<= /o:p>

 

Revista Tecnológica Espol - RTE Vol. 35, N° 2 (Octubre, 2023) / e-ISSN 1390-3659

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