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<= /span>

 

 

<= span lang=3DES>https://doi.org/10.37815/rte.v35n2.1050

<= span lang=3DES>Artículos originales

 

Síntesis Text= ual de Evaluación para Acoso y Ciberacoso Textual Synthesis of Test<= span style=3D'letter-spacing:-.7pt'> for Bullying and Cyberbullying

Marcos Orellana1 https://orcid.org/0000-0002-3671-9362,= Jorge Luis Zambrano-Martinez1<= /p>

https://orcid.org/0000-0002-5339-7860, Patricio Santiago Garcia Montero1 ht= tps://orcid.org/0009-0007-4113-8400, Liliana Marilu Lojano1 https://orcid.org/0009-0006-4993-8747, Mateo Sebastian Zea1 https://orc= id.org/0009- 0005-4209-8143, T= upak Pacjakutik Japon1 https://orcid.org= /0009-0005-3239-992X

 

1Laboratorio de Investigación y Desarrollo en Informática (LIDI) Universidad del Azuay, Cue= nca, Ecuador = marore@uazuay.edu.ec<= /span>, = jorge.zambrano@uazuay.edu.ec,

santyg20@es.uazuay.ed= u.ec, liliana1998@es.uazuay.edu.ec mzea582@es.uazuay.edu.ec, tupak.japon@es.uazuay.edu.ec

 =


<= !--[if gte vml 1]> .

 

 

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

Enviado: 2023/07/14 Aceptado: 2023= /08/15 Publicado: 2023/10/15

 


Resumen

En los últimos años, el acoso = y el ciberacoso son problemas que han aumentado vertiginosamente afectando escuelas, colegi= os y universidades. Debido a los avances en las tecnologías de la información, cualquier persona está expu= esta a ser atacada; por esta razón, es necesario crear soluciones a través de técnicas adecuadas que ayuden a prevenir el acoso y ciberacoso. En consecuencia, en este artículo se propone crear una síntesis textual a partir de datos de encuestas que permita desarrol= lar modelos para clasificar o predecir tanto a víctimas como agresores de acoso y ciberacoso. Para ello, se utilizaron técnicas de miner&iacut= e;a de datos, árboles de de= cisión y técnicas de agrupación, dando como resultado una síntesis textual. Esto permitió la creación y evaluación <= /span>de un modelo de aprendizaje supervisado y otro modelo con técnicas de agrupamiento, aplicadas a los datos de las encuestas realizadas a estudiantes universitarios. Los resultados demostraron la importancia de la síntesis textual para la generación de modelos de clasificación o predicción de víctimas y agresores del acoso y ciberacoso, con una exactitud mayor al 75%, siendo el m= odelo de agrupamiento con mejor rendimiento.

 

Palabras clave: Acoso, Ciberacoso, Minería de datos, Árbol de decisi&= oacute;n, Agrupamiento.

 

 

 

Abstract

In recent years, bullying and cyberbullying= have increased, affecting schools, colleges, and universities. Due to advances in information technology<= /span>, any person is exposed to being attacked. Therefore, it is necessary to create solutions through appropriate= techniques that help prevent bullying and cyberbullying. Consequently, this article proposes to create a textual= synthesis from survey data that allows<= span style=3D'letter-spacing:-.35pt'> the development of models to classify= or predict<= span style=3D'letter-spacing:-.35pt'> both victims<= span style=3D'letter-spacing:-.35pt'> and aggressors= of bullying and cyberbullying. = Data mining<= span style=3D'letter-spacing:-.75pt'> techniques, decision trees,<= span style=3D'letter-spacing:-.75pt'> and grouping techniques were used, resulting in a textual synthesis. This allowed <= span class=3DSpellE>the creation = and evaluation of a supervised learning model and another = model with clustering techniques applied = to the data= from the surveys performed on university students. The = results demonstrated the importance of textual synthesis for generating models for the classification or <= span class=3DSpellE>prediction of victims and aggressors of <= span class=3DSpellE>bullying and cyberbullying, with an accuracy greater than 75% for the grouping model with the best performance.

 

Keywords: Bul= lying, Cyberbullying, Data mining, Decision tree, Clustering.

 

 

Introducción

La violencia es un fenómeno global que afecta a la sociedad en varios escenarios de la vida cotidiana, como los hogares, colegios, lugares de trabajo y relaciones personales. Su pr= esencia trae consecuencias negativas en cualquier ámbito, ya sea social, económico, político o gubernamental. No tiene distinción por edad, género, raza, o cultura y puede expresarse de varias formas. Además, tiene un impacto deva= stador en la vida de la persona que la sufre, alterando aspectos de su salud física y mental. La violencia puede ser verbal, física, sexual, psicol&o= acute;gica o de género (Nielsen y E= inarsen, 2018). No obstante, una forma que se encuentra a menudo en la sociedad moderna es el acoso, un tipo de violencia que puede ir desde el ac= oso verbal hasta la agresión física o psicológica e incluso digital, como el ciberacoso (Mollo et al., 2018= ).

 

El auge de las redes sociales, jue= gos en línea y aplicaciones de mensajería instantánea ha evolucionado la forma en que las personas interactúan, sin embargo, = han dado lugar a una creciente forma de violencia. El ciberacoso utiliza cualqu= ier medio de difusión digital para acosar, intimidar, chantajear o difam= ar a una persona, a diferencia del acoso tradicional, que utiliza medios físicos o verbales para el mismo fin. Ambos tipos de violencia están presentes en los= mismos escenarios, pero el ciberacoso posee un mayor alcance debido a las crecientes redes digitales y de telecomunicaciones que conectan en la actualidad a la mayoría de la población = (Castilla, 2021). Sin embargo, toda esa interacción digital deja grandes cantid= ades de información que puede ser explorada y explotada mediante técnicas y algoritmos de minería de datos para combatir y mit= igar estas formas de violencia.

 

La minería de datos permite analizar información con el fin de procesar y explorar cualquier patrón o relación no evidente a simple vista (Han et al., 202= 2). En este estudio se determinó el uso de la minería de datos co= mo una herramienta para analizar y sintetizar los datos de una encuesta aplicada a estudiantes universitarios sobre sus experiencias con el acoso y ciberacoso, aplicando técnicas como el preprocesamiento de datos, técnicas de agrupamiento= y modelos de clasificación.= La finalidad del estudio es generar una síntesis textual de las ideas primordiales obtenidas de las encuestas que actúe como punto de partida para la generación de conocimiento sobre el acoso y el ciberacoso.

 

El resto del documento se estructu= ra de la siguiente manera: Sección 2, presenta los trabajos relacionados, la Secció= ;n 3 plantea los fundamentos teóricos de la investigación, la

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Sección 4 expone los materiales y métod= os utilizados, la Sección 5 explica los resultados que se han obtenido luego de aplicar los métodos y la Sección 6 presenta las conclusiones y trabajos futuros.

 

Trabajos relacionados=

La<= /span> presencia del acoso= y el ciberacoso genera gran discusi&oacu= te;n en la= comunidad académica, lo que ha llevado a varios investigadores a trabajar para beneficio de las personas que padecen a diario estas agresiones (Arce-Ruelas et<= span style=3D'letter-spacing:-.7pt'> al., 2022). Los trabajos que se exponen a continuación demuestran el uso de la minería de datos y textos en la generación de modelos de detección y clasificación de estos fenómenos.

 

En el trabajo de Shaikh et al. (20= 20), desarrollan una revisión sistemática de literatura para identificar los factores que impulsan a estudiantes universitarios hacia el ciberacoso. Su trabajo pretende ser= vir de guía para futuras investigaciones en el análisis del ciberaco= so. Identificaron cerca de 35 factores en 32 estudios, siendo los más reportados problemas emocionales (depresión, ansiedad y estrés), autoestima, agresión, personalidad, malas relaciones, estilo de crianza, rendimiento académico, falta de empatía, exposición tecnológica y facilidad de acceso a internet.

 

Del mismo modo, Namane y= Kyobe (2017), a través de la Universidad de Cape Town, relataron el desarrollo de un análisis con respecto al comportamiento y características de = las personas agresoras. En el análisis participaron 3,621 personas (víctimas y agresores) con un promedio de edad entre 14 y 18 años. A travé= s de una encuesta, los investigado= res demostraron que 407 participantes f= ueron víctimas de acoso y se separaron en tres índices de riesgo social. Estos índices<= span style=3D'letter-spacing:-.45pt'> se comprenden por la probabilidad que una persona sufra de algún perjuicio dentro del entorno que le rodea (Jorgensen y Siegel, 2019). Así, según Namane y Kyobe (2017), 107 participantes fueron víctimas de acoso provenientes de sectores con = bajo riesgo social, 114 participantes se ubicaron dentro del medio riesgo social y 186 participantes dentro del alto riesgo social sufrieron acoso. Además,<= span style=3D'letter-spacing:-.4pt'> enfatizaron que los comportamientos violentos se presentan con mayor frecuencia en adolescentes, debido a que es una etapa con mucha variación y cambios constantes en la personalidad, actitudes y emociones.

 

Por= lo tanto, los adolescentes = se sitúa= n como <= span lang=3DES style=3D'letter-spacing:-.1pt'>uno de los mayores grupos de consumidores y creadores de contenido en las redes sociales, así en el trabajo de (Bozyiğit et al., 2019) se evaluaron ocho modelos dist= intos de redes neuronales para la detección de ciberacoso en tweets de Turquía. Los investigadores emplearon técnicas de minería de textos para procesar la información como la tokenización, transformación y la eliminación de símbolos, en conjunto con otras técnicas como frecuencia de término – frecuencia inversa de documento (TF-IDF) y N-gramas para proces= ar 3,000 tweets y entrenar los distintos modelos. Finalmente, solo un modelo presentó una exactitud del 91%, debido a la utilización de va= rias configuraciones. Los investigad= ores también demostraron que incrementar el número de capas oculta= s no necesariamente mejora el rendimiento del modelo.

 

Adicionalmente, para implementar u= n tipo de red neuronal denominada memoria a corto-largo plazo (LSTM) en el trabajo de = Mahat (2021), los investigadores utilizaron datos de Twitter= , Wikipedia y Formspring. Un total de 9,000 registros fueron procesa= dos con técnicas de minería de textos (eliminación de caracteres especiales, espacios y ruido en general). Al final el modelo tuvo una exactitud del 77.9%. Otra aplicación de modelos de aprendizaje profundo en redes sociales se aprecia en el trabajo de = Banerjee et al. (2019), donde utiliza un modelo basado

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en una red neuronal convolucional para la detecci&oac= ute;n del ciberacoso en India. Cerca de 69,874 tweets fueron extraídos y procesados para eliminar palabras vacías y signos de puntuació= ;n. Posteriormente, los textos fueron vectorizados para alimentar al modelo, que logró una exactitud de 93.97%, superando a otros modelos de aprendizaje automático, como la máquina de vector de s= oporte (SVM).

 

En contraste con lo anterior, Dalvi et al. (2020) presenta un método para la detección de ciberacoso que utiliza una SVM y un clasificador Naive Bayes. Recuperaron tweets de locaciones en tiem= po real y utilizaron técnicas de preprocesamiento de datos (tokenización, eliminación de signos de puntuación y palabras vacías, lematización <= /span>y transformación) a través de un paquete de herramientas de lenguaje natural (NLTK). Al final, el algoritmo SVM es el que presenta una mayor exactitud, con el 71.25%, a diferencia del clasificador bayesiano, con 52.70%.

 

Si bien las redes sociales son utilizadas a menudo en el análisis del acoso y ciberacoso, también existen estudios enfocados a detectarlos en otras plataformas, como los juegos en línea. Cornel= et al. (2019) desarrollan una red neuronal convolucional para detectar la presencia de ciberacoso en los registros de mensajería de dos juegos en línea: Dota y Ragnarok. <= /span>A través de varias interfaces de programación de aplicaciones (APIs) recogieron 230,394 frases de Dota pertenecientes a usuarios de Filip= inas y 534,328 de usuarios que jugaron Ragnarok en Japón y Singapur. Luego = de eliminar las palabras vacías y caracteres especiales, vectorizaron l= os registros a través = del algoritmo word= embedding para alimentar al modelo. La red neuronal tuvo una exactitud de 99.93%, sin embargo, los investigadores concluyeron que el modelo tiende a sobre ajustarse, por lo que recomendaron explorar otros mod= elos de aprendizaje profundo.

 

Como se evidencia en los trabajos mencionados, las redes neuronales son utilizadas a menudo en el aná= ;lisis de estos fenómenos utilizando distintos modelos. De igual forma, Rah= man et al. (2021) evalúan los siguientes modelos de aprendizaje automático: SVM, árboles de decisión, = bosque aleatorio, regresión logística y clasificador bayesiano. A través de varias plataformas (Kaggle, T= witter, Wikipedia y YouTube) recuperaron 31,403 registr= os etiquetados como inofensivos, y= 23,663 etiquetados como ciberacoso. Para este análisis, se utilizan técnicas de preprocesamiento= de datos (limpieza, derivación, eliminación de ruido y palabras vacías) y el valor TF-IDF para construir los modelos. = Como resultado, el bosque aleatorio presentó la mayor precisión de todos, con el 89%.

 

En base a los estudios mencionados= , los avances en la detección y clasificación del acoso y ciberacoso van desde el análisis de comportamientos hasta la aplicación de modelos de aprendizaje profundo. Todos ellos emplearon técnicas de minería de datos o textos y algunos de ellos coincidieron en varias tareas de preprocesamiento. Sin embargo, la ta= rea de síntesis previa de los datos es fundamental, debido a que los datos suelen contener ruido y afectan al rendimiento de los modelos (Tapia et al., 2018). = Por lo tanto, son escasos los trabajos que resaltan la importancia y el uso de diferentes técnicas en la síntesis de texto en el anál= isis del acoso y el ciberacoso.

 

 

Marco= Teórico

El acoso y el ciberacoso son temas extensos por tratar y abarcan varios campos de la psicología, desde condu= ctas humanas hasta rasgos de personalidad que surgen en edades tempranas, como la niñez y adolescencia. Cuando se presenta, suele repetirse a menudo en ambientes donde el agresor siente confianza y que el agredido suele frecuentar como las

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escuelas, colegios, universidades y demás instituciones educativas. Las agresiones van desde insultos, golpes, discriminaciones, burlas, hasta publicaciones de información, fotografías y videos &ia= cute;ntimos que buscan difamar y avergonzar a la persona agredida. El ciberacoso utiliza la tecnología como su herramienta principal para agredir de manera pública o anónima= mente a su víctima desde cualquier parte del mundo. Estas agresiones pueden incurrir en varios problemas físicos y mentales de la persona agredida, desde fa= lta de confianza, problemas para socializar, depresión hasta lesiones físicas o suicidios (Li et al., 2022).

 

Tapia et al. (2018) describen al a= coso como una conducta intencional carente de ética, inmoral e impropia, que se basa en una serie de amenazas físicas o verbales hacia otra persona, generando angustia en la víctima y un desequilibrio de poder entre su persona y el agresor. Mientras tanto, el ciberacoso es una amenaza que afecta a la sociedad moder= na gracias al surgimiento de nuevas tecnologías de información. = Si bien el avance tecnológico ha sido de beneficio en áreas críticas como la salud y la educación, también ha generado un problema social grave. El anonimato en la era digital en conjunto con problemas o trastornos de una persona puede resultar en posibles agresores, que perciben la violencia como una salida a su conflictiva realidad. En contraste, la<= span style=3D'letter-spacing:-.05pt'> persona agredida tiende a dificultar la comunicación con su entorno, lo que reduce la posibilid= ad de que algún familiar o una persona de su círculo cercano logre identificar la presencia de ciberacoso fácilmente.

 

De acuerdo con Herrera et al. (201= 8), la mayoría de ciber atacantes cursan el segundo y tercer año de = secundaria, a diferencia de la mayoría de sus víctimas, que cursan el pri= mer y segundo año. Adicionalmente, la diferencia de edad, el contenido y el tiempo de navegación en internet tam= bién resultaron ser factores a tener en cuenta a la hora de identificar a una persona agresora o víctima= . Según Martin-Criado e= t al. (2021), las plataformas y actividades de mayor visita para los menores de edad se centran en las redes sociales y plataformas de video. Su alto consumo hace que los menores tiendan a recrear las situaciones, comportamientos y léxico<= span style=3D'letter-spacing:-.5pt'> que se exhiben en dichas plataformas= , lo que incrementa la probabilidad de que un menor pueda recrear comportamientos violentos en su entorno.

 

Por otro lado, la minería de datos es una técnica qu= e se ha popularizado en los últimos años; pe= rmite analizar grandes conjuntos de datos con el fin de encontrar y esclarecer cualquier patrón o relaci&oa= cute;n no evidente a simple vista. Entre sus tareas más conocidas se encuen= tran el análisis, sín= tesis y visualización de datos. Su finalidad es generar conocimiento sobre temas de interés, ayudar en la tom= a de decisiones empresariales u optimizar procesos industriales. Con la generación masiva de información actual, las fuentes de datos se pueden encontrar en línea, a través de gestores de bases de datos o se generan mediante encuestas, grupos focales y otras técnicas= de recolección. Entre los sectores más interesados en la minería de datos se encuentra el sector empresarial, que aprovecha este proceso para detectar anomalías, fraudes o mejorar sus estrategias comerciales. Existen metodologías maduras que facilitan y estandariz= an sus fases, como el descubrimiento de conocimientos en las bases de datos (K= DD) con sus seis etapas: selección<= /span> de datos, preprocesamiento, transformación, minería de datos, evaluación y finalmente interpretación (Schröer et a= l., 2021).

 

El preprocesamiento de datos es una tarea que consiste en la limpieza y eliminación del ruido de los datos para ajustarse a técnicas y modelos de minería de datos. Estas tareas mejoran la calidad, consistencia y confiabilidad de los datos al corregir, reemplazar y eliminar datos incorrectos, irrelevantes o redundantes. La transformación, por otra parte, incluye la selección de atributos relevantes en el estudio, así como técnicas de transformación como la discretización, normalización y<= span style=3D'letter-spacing:-.3pt'> aumento o reducción de dimensionalidad. Adicionalmente, en caso de tratarse

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de un tipo de aprendizaje supervisado, se debe establecer el atributo de salida o etiqueta (Castro R et al., 2018).

 

El<= /span> aprendizaje supervisado se utiliza en situaciones específicas en donde<= span lang=3DES style=3D'letter-spacing:-.5pt'> se quiere inferir conocimiento a partir de datos etiquetados, es decir= , que se conoce su variable a predecir o clasificar. En esta técnica s= e utilizan los datos etiquetados para entrenar el modelo, y se evalúa con datos no etiquetad= os para medir su rendimiento. Uno de los modelos utilizados en este estudio y más populares de aprendizaje supervisado es el árbol de decisió= ;n, un modelo fácil de implementar e interpretar cuando se configura adecuadamen= te. Su funcionamiento se basa en la estructura de un árbol, y representa= a las características o atributos seleccionados mediante nodos, y a su vez, ramifica las opciones de cada una (Ramirez= y Ccallohuari, 2020).

 

Por otra parte, el aprendizaje no supervisado no dispone de una variable de salida preestablecida, sino que t= rata de encontrar patrones o relaciones en el conjunto de datos que permitan clasificar, categorizar o<= span style=3D'letter-spacing:1.35pt'> etiquetar los registros.= Una de las técnicas más utilizadas en el aprendizaje no supervisado y por ende en e= ste estudio es el agrupamiento. Esta técnica trata de generar grupos con registros similares y separar los grupos con características ajenas.= Su funcionamiento se basa en el uso de métricas de distancia o similitu= d, como la euclidiana, coseno, man= hattan, entre otras (Roux, 2018). Existen variaciones de esta técnica con diferentes algoritmos internos y la elección de alguna dependerá del tip= o de datos y el problema que se quiera afrontar (Bracco, 2018).

 

 

Materiales y Métodos

Los= materiales necesarios para la aplicación de técnicas y algoritmos de minería de datos en este estudio se describen a continuación:

 

•

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•      =    Transformación de los datos: muchos algoritmos y modelos de minería de datos requieren ciertos tipos de valores para funcionar correctamente o dar mejores resultados. Algunas de = las técnicas de transformació= ;n de datos incluyen la discretización, codificación de valores y asignación de nuevos valores. Otra parte fundamental en este proceso es la división del conjunto de datos para entrenamiento y pruebas. En esta técnica es frecuente dividir los datos en 80% para entrenamiento y 20% para pruebas. Esta técnica garantiza la transparencia del mode= lo, debido a que será evaluado con datos desconocidos.

 

En RapidMiner, los operadores permiten al usuario llevar a cabo los procesos existentes en el programa, siempre y cuando se establezcan las conexiones correctas, los formatos y los tipos de datos que requieren. Los operadores utilizados para llevar a cabo dichos procesos y generar la síntesis textual se presentan en la Figura 1.

 

Figura 1

Esquema de operadores para preproces= amiento de datos


 

A partir de ahora se pueden aplicar modelos de clasificación o predicci&oacut= e;n, técnicas de agrupamiento y evaluación. En este estudio se utilizaron dos modelos de árboles de decisión, un modelo estándar, aplicado a partir de los 18 atributos seleccionados, y otro modelo generado a partir de un agrupamiento previo, etiquetando a cada grupo (clúste= r). En ambos modelos se utilizó el mismo proceso de síntesis previa, donde fueron evaluados y comparados en su tarea de clasificación. <= span style=3D'letter-spacing:-.1pt'>El agrupamiento jerárquico aglomerativo (HAC), árboles de decisión y métricas de evaluación fueron los modelos y algoritmos utilizados. Otros modelos,= métricas de evaluaci&oa= cute;n y procesos de optimización quedan fuera del alcance de este estudio.=


 

Implementa= ción de la técnica de agrupamiento=

Las técnicas de agrupamient= o se utilizan con frecuencia en la minería de datos para identificar grup= os naturales en los datos a partir de su similitud (Mamani Rodríguez et al., 2017). Existen varios algoritmos de agrupamiento, pero para este estud= io se utilizó el HAC debido a su estructura y simplicidad. Este agrupamiento jerárquico considera que cada punto de datos en un grupo individual, luego, en un algoritmo aglomerativo= de “abajo hacia arriba” agrupa los puntos hasta formar grupos más grandes mientras sube en su jerarquía (Sharma et al., 2019). Las variables que se utilizaron para= implementar la técni= ca de agrupamiento fueron los 18 atributos seleccionados que componen el cuerpo de la encuesta realizada a los estudiantes universitarios, debido a ser los más relevantes para realizar= la síntesis textual. En conjunto, se utilizó un operador adicional que reduce en una sola jerarquía los grupos que se quieren generar, en este caso cuatro grupos. Los operadores que permiten implementar esta téc= nica se muestran en la Figura 2.

 

Figura 2

Operadores = de agrupamiento


 

Los parámetros de configuración del operador de agrupamiento se modificaron para traba= jar con los datos procesados. El modo o criterio que indica el tipo de enlazar = cada punto en el agrupamiento, el= tipo de medida indica la medida que se utilizará para medir la distancia entre los puntos, puede ser nominal, numérica o mixta, y a su vez la medida a utilizar. Este parámetro depende del anterior, ya que despl= iega distintas medidas en función de su tipo. Los parámetros establecidos en RapidMiner se presentan en la T= abla 1.

 

Tabla 1

Configuración de paráme= tros del agrupamiento

 

MODO

ENLACE PR= OMEDIO

Tipo de medida

= Numérica=

Medida numérica.<= /p>

Coeficiente de similitud de Sorensen-Dice<= /span>

 

Impleme= ntación de árboles de decisión

Los árboles de decisi&oacut= e;n son un modelo de aprendizaje automático supervisado. Su estructura se compone de nodos, que representan las características o atributos de los datos de entrada, y ramas, que representan las posibilidades de esos atrib= utos (Fletcher e Islam, 2020). RapidMiner ofrece la posibilidad de generar árboles de decisión y modificar alguno= s de sus parámetros para optimizar su rendimiento.

 

Asimismo, como en la técnica anterior se utilizaron los 18 atributos preprocesados con la finalidad de obtener resultados más eficientes. Los parámetros que fueron modificados corresponden al criterio, que especifica la forma de selección y división de las ramas, así como la profundidad máxi= ma, que limita el número de ramificaciones. Los valores modificados y el resto de = los parámetros se evidencian en la Tabla 2.

 

Tabla 2

Configuración de parám= etros del árbol de decisión=

 

CRITERIO

RELACIÓN DE GANANCIA (GAIN RATIO)

Profundidad máxima

5

= Confianza=

= 0.10=

Ganancia mínima

= 0.01=

Tamaño mínimo de hojas

2

 

Evaluac= ión de modelos

Para validar la eficacia de los modelos genera= dos, se utilizaron operadores que permiten e= valuar el rendimiento de los modelos a través de varios criterios como la precisión, la exactitud (= Accuracy) y la recuperación (Recall)= . En RapidMiner, previo a la evaluación de los modelos, es necesario un operador que aplique el modelo con los datos de entrenamiento y prueba. Posteriormente,<= span style=3D'letter-spacing:-.75pt'> se conecta el operador que mide el rendimiento del modelo. Ambos operadores no disponen de parámetros de configuración.

 

Fin= almente, el rendimiento de los modelos fue evaluado a travé= s de la matriz de confusión generada. De esta manera, fue posible comparar su rendimiento, así como los aciertos y errores en la clasificación de cada modelo, evidenciando cuál presenta las mejores prestaciones. Por consiguiente, los métodos descritos en esta sección son exclusivos de la tarea de síntesis textual sobre el acoso y el ciberac= oso.

 

Resultados y Discusión

Los procesos y técnicas de preparación de los datos resultaron en la síntesis textual que se describe a continuación:

 

•      =    Selección de atributos o variables: se seleccionaron 18 atributos del cuerpo de la encuesta que incluyen datos ordinales (ítems= tipo Likert), un identificador (número de encuesta) y un atributo de salida. Este atributo etiqueta al estudiante que ha sufrido de acoso o ciberacoso mediante dos valores (Sí y No).

•


 

•

 

•

 

En RapidMiner= es posible visualizar los modelos de árboles de decisión a través de gráficos que representan los nodos y ramificaciones del modelo. Aunque en algunas ocasiones, los modelos resultan exte= nsos debido a la cantidad de atributos o su configuración, como ha ocurri= do en este caso, por lo que una representación gráfica de los árboles no es adecuada en esta ocasión.

 

Sin= embargo, la matriz de confusión expone el rendimiento del modelo a travé= s de varias m&ea= cute;tricas, como la precisión para las clases, que indica la proporción de verdaderos positivos sobre el total de calificaciones positivas. Y la métrica de recuperación (Recall) = de cada clase, que indica la proporción de verdaderos positivos que se han clasificad= o correctamente. Esto es realiza= do a través de la ecuación 1 y 2:

 

=3D=

 
precisión

=          = TP&nb= sp;        TP + FP

(1)

 

TP + FN
 
recuperación =3D    &nb= sp;     TP&n= bsp;       


(2)

 

Donde TP es verdadero positivo, FP es falso positivo y FN es falso negativo.

 

El<= /span> modelo de clasificaci&= oacute;n sin agrupamiento previo presentó una exactitud del 78.52%, y el modelo de clasificación con agrupamiento= presentó una exactitud del 86.57%. En las Tabla 3 y la Tabla 4 exponen la matriz de confusión de ambos modelos, así como los valores de precisión y el porcentaje de recuperación.

 

Tabla 3

Matriz de confusión del modelo sin agrupamiento

 

 

 

SI

NO

PRECISIÓN DE LA CLASE

PRED. NO=

92=

24=

= 79.31%=

PRED. S&= Iacute;

5

14=

= 73.68%=

RECUPERACIÓN DE LA CLASE

= 94.85%=

= 36.84%=

 

 

La precisión de ambas clases de acuerdo con la Tabla 3 es superior al 70%. Por lo tanto, el modelo puede clasificar con precisión al menos esa proporci&oacut= e;n de registros de estudiantes que sufren de acoso o ciberacoso. Sin embargo, la recuperación de la clase Sí presenta algunas dificultades al modelo, ace= rtando en 14 ocasiones de un total de 38 registros pertenecientes a la clase Sí, dando como resultado una recuperación del 36.84%. Por el contrario, la clase No, que pr= esenta una proporción del 94.85%, acertó en 92 ocasiones de un total de 97 registros de e= sta clase.

 

De acuerdo con la Tabla 4, el mode= lo con agrupamiento previo pudo clasificar a tres de los cuatro grupos formados con una precisión de al menos 75%. Sin embargo, al dividir los datos aleatoriamente en un grupo de entrenamiento y de prueba, no quedaron registros existentes del grupo tres en los datos de prueba. En cuanto a la recuperación, el grupo dos presentó una proporción del 100%, mientras que el grupo uno fue del 97.70%. Lo que acertó en= clasificar 85 casos de 87 y finalmente el grupo cero cuya una proporción fue del 56.76%, llegó a clasificar 21 casos correc= tos de un total de 37 casos pertenecientes a ese grupo.

 

Tabla 4

Matriz de confusión del modelo con agrupamiento

 

 

CLUSTER = 0

CLUSTER = 1

CLUSTER = 2

CLUSTER = 3

PRECISIÓN <= /span>DE LA CLASE

PRED. CLUSTER 0

21=

2

0

0

= 91.30%=

PRED. CLUSTER 1

13=

85=

0

0

= 86.73%=

PRED. CLUSTER 2

3

0

10=

0

= 76.92%=

PRED. CLUSTER 3

0

0

0

0

= 0.00%=

RECUPERACIÓN DE LA CLASE

= 56.76%=

= 97.70%=

= 100.00%=

= 0.00%=

 

 

La síntesis textual facilitó la generación de modelos de clasificación; ambos modelos

<= span lang=3DES>presentaron una exactitud por encima del 75%. Sin embargo, los resultados evidencian una

&nbs= p;

mayor exactitud al implementar el modelo con agrupami= ento, lo que indica que el proceso de síntesis tuvo una influencia positiv= a en su rendimiento al predecir los casos correctos de las personas que han sufrido de acoso y ciberacoso. Aun así existen otros factores que pueden alterar los resultados de un modelo de minería de datos, como las tareas de preprocesamiento de datos, la calidad de los datos y el balance de las clases o etiquetas.

 

 

Conclusiones

La minería de datos se encuentra en un proceso continuo de avance y transformación, al igual que sus herramientas de software. La generación masiva de información permite la creación de nuevos algoritmos y procesos que permiten analizar y generar conocimiento de cualquier tipo de información, incluso de problemas sociales tan delicados como el acoso y el ciberacoso. Cada día, más jóvenes y adolescentes sufren estos tipos de violencia en= sus actividades cotidianas; esto s= e evidencia en el número de estudiantes= universitarios que respondiero= n afirmativamente a la encuesta sobre= sus experiencias con el acoso y el ciberacoso.

 

Por lo tanto, en este estudio se utilizaron técnicas y algoritmos de minería de datos como árbol de decisión y de agrupamiento a las encuestas aplicadas a los estudiantes universitarios para generar una síntesis textual que permita generar conocimiento sobre el acoso= y el ciberacoso. De este modo, al aplicar el modelo de aprendizaje automático supervisado denominado árbol de decisión = con los datos obtenidos de las encuestas presen= tó una exactitud del 78.52%. Sin embargo, al evaluar con los mismos datos sobre el acoso y el ciberacoso= , el modelo de clasificación<= span style=3D'letter-spacing:-.5pt'> con agrupamiento alcanzó una exactitud del 86.57% al estimar cuando se presentan estos problemas sociales a través de la encuesta realizada. No obstante, es imprescindible realizar tareas de preprocesamiento de datos para lograr un buen desempe&ntil= de;o de los modelos, debido a que pueden alterar los resultados del modelo que se está utilizando.

 

Como trabajos futuros, se plantea = el uso de otros modelos de aprendizaje automático y aprendizaje profundo como las redes neuronales; así<= span style=3D'letter-spacing:1.85pt'> como otras técnicas de evaluaci&oa= cute;n de modelos como la validación cruzada que pueden ser de utilidad par= a la generación de conocimiento sobre el acoso y el ciberacoso.

 

Reconocimientos

Los autores desean agradecer al Vicerrectorado de Investigaciones de la Universidad del Azuay por el apoyo financiero y académico, así como a todo el personal de la escuela de Ingeniería de Ciencias de la Computación, y el Laboratorio de Investigación y Desarrollo en Informática (LIDI).

 

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