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https://doi.org/10.37815/rte.v36ne1.1207

<= span lang=3DES>Artículos originales

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Extracción de= Palabras Clave de Ciberacoso de Textos Breves: un Enfoque de Aprendizaje Automático

Extracting Cyberbullying Keywords<= span style=3D'letter-spacing:-.6pt'> from Short Texts: A= Machine Learning Approach

 

William Hermel<= /span> Astudillo Quituisaca1 https://orcid.org/0009-0009-6189-7492, Priscila Cedillo2 <= span lang=3DES>https://orc= id.org/0000-0002-6787-0655, Marcos Orellana= 1 https://orcid.org= /0000-0002-3671-9362

 

 

1Laboratorio= de Investigación y Desarrollo en Informática (LIDI), Cuenca, Ecuador

wastudillomsn@es.uazuay.edu.ec marore@uazuay.edu.ec<= span lang=3DES style=3D'font-family:"Courier New";mso-hansi-font-family:"Times N= ew Roman"; mso-bidi-font-family:"Times New Roman"'>

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

2Universidad de Cuenca, Cuenca, Ecuador

priscila.cedillo@ucuenca.edu.ec<= /a>

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Enviado: 2024/07/12 Aceptado: 2024= /10/02 Publicado: 2024/10/15

 


Resumen

El cibera= coso impacta negativamente a la sociedad debido a las consecuencias que sufren las víctimas, acosadores y espectadores. El acceso generalizado a Internet y redes sociales, especialmente entre jóvenes sin herramien= tas para enfrentar estas situaciones, hace necesaria una formación social que mitigue los efectos del ciberacoso. Este estudio busca contribuir a esa formación mediante la creación de guiones para cápsulas educativas. Para ello, se desarrolló un modelo que automatiza la búsqueda y extracción de datos de la red social X utilizando Python y Selenium Web Driver. Tras un proceso de pre-procesamien= to de textos utilizando técnicas = de Procesamiento de Lenguaje Natural, se aplicó el modelo de Asignación Latente de Dirichlet (LDA) para identificar las palabras clave. Finalmente, se utilizó el modelo pre-entrenado<= /span> “text- davinci-003” a través de la API de la empresa OpenAI<= span style=3D'letter-spacing:-.65pt'> para generar el contenido de las cápsulas educativas, asignando un contexto y utilizando las palabras clave identificadas. El resultado de esta investigación propues= ta es la generación de un guion que contiene temas de educación y prevención del acoso y ciberacoso. Para garantizar una confiabilidad del texto generado por el modelo pre-entrenado generativo, se evaluó con un exp= erto en la materia mediante el enfoque de <= /span>Meta-Pregunta-Respuesta (GQM), lo que valida su potencial en la generación de contenido edu= cativo en la lucha contra el ciberacoso.

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Palabras clave: Acoso, Asignaci&oacut= e;n Latente de Dirichlet, Ciberacoso, Inteligencia artificial,

Modelos de lenguaje.

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Abstract

Cyberbullying has a negative impact on society due to the consequences suffered by victims= , bullies, and bystanders. Widespread access to the internet and social networks,<= span style=3D'letter-spacing:-.5pt'> especially among young people w= ithout the tools to deal w= ith these s= ituations, makes social education n= ecessary to mitigate the effects = of cyberbullying. <= /span>This study seeks to contribute to this tra= ining through= the creation of scrip= ts for educational capsules. To this end, a model was developed that automates the search and extraction of d= ata from the social network X using Python an= d Selenium Web Driver. After a text preprocessing process using Natural Lan= guage Processing techniques, the= Latent D= irichlet Assignment (LDA) model was applied = to identify keywords. Finally= , the pre- trained model "text-davinci-003" was used through<= span style=3D'letter-spacing:-.15pt'> the OpenAI<= span style=3D'letter-spacing:-.75pt'> API to generate the content = of the educational capsules, assigning a cont= ext and using the identified keywords. The outcome of this proposed research is the generation of a script that includes topics<= span style=3D'letter-spacing:-.15pt'> on education and the prevention of bullying= and cyberbullying. <= /span>To ensure the reliability of the text generated by the pre-trained generative model, it was evaluated = by an expert in the field using the Goal-Question- Metric<= span style=3D'letter-spacing:-.1pt'> (GQM) approach, which validates its potential in generating educational content<= span style=3D'letter-spacing:-.1pt'> in the fight against cyberbullying.

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Keywords: Artificial Intelligence, Bullying, Cy= berbullying, Latent Dirichlet Assignmen= t, Language models.

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Introducción

El uso de las Tecnologías d= e la Información y Comunicación (TIC) se ha integrado profundament= e en la vida diaria, especialmente entre los jóvenes. En Ecuador, el 92% = de la población mayor a cinco años utiliza redes sociales (Peña y Herrera, 2021). Si bien las TIC ofrecen múltiples beneficios, también facilitan comp= ortamientos negativos como el ciberacoso. Garaigordobil (2014) de= fine el ciberacoso como una forma de acoso que utiliza las TIC, y el acoso escolar tradicional como una violencia reiterada entre iguales,= donde uno o más agr= esores ejercen poder sobre una víctima para causarle daño. En estas dinámicas se identifican tres roles:

i) el agresor, la o las personas que practican el aco= so; ii) el espectador, la o las personas que presencian situaciones de acoso; y iii) la víctima,= la o las personas acosadas. Este incremento en el uso de las TIC y esta forma de acoso resaltan la importancia de promover una educación apropiada en el uso de la tecnología, especialmente entre los niños y jóvenes, quienes son los más expuestos a estos riesgos.

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El acoso escolar no solo afecta la salud mental, física y psicológica de las víctimas, sino que tambi= én aumenta el riesgo de deserción escolar. Las víctimas experimentan un incremento = en la ansiedad y la depresión, mientras que los espectadores pueden desarrollar miedo, sumisión, desensibilización e incluso interiorizar conductas antisocial= es y delictivas como medio para obtener lo que desean, además de experimentar sentimientos de culpa (Lugones Botell y Ramírez Bermúdez, 2017). Bajo este contexto, el ciberacoso se ha convertido en un problema global con <= /span>graves consecuencias sociales, por lo que su mitigación se ha convertido en una necesidad urgente. Herramientas como= los programas y campañas de prevención han contribuido en la reducción de estudiantes víctimas de acoso escolar y ciberaco= so (Salmivalli et al., 2021). Sin embargo, una educaci&oa= cute;n adecuada tanto de estudiantes como de padres en temas relacionados con el acoso y el fortalecimiento de la autoestima es importante para que modelos de prevención de acoso y ciberacoso sean efectivos.

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Una forma de promover el aprendiza= je, especialmente en niños y jóvenes, es a través de nuevas metodologías como el micro aprendizaje, donde la atención de = la audiencia es captada mediante<= span style=3D'letter-spacing:-.35pt'> contenidos breves y fáciles de consumir en periodos cortos. Por lo tanto, una herramienta popular en este enfoque son las cápsulas educativas, que= combinan las TIC con la generación de contenidos digitales educativos (Vidal Ledo et al., 2019). Dado que los contenidos son concisos, es fundamental que sean relevantes, precisos y presenten la información de manera clara y comprensible (Kamilali y Sofianopoulou, 2015).

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Bajo este contexto, X (anteriormente Twitter) se ha consolidado com= o un espacio ideal para la expresión de opiniones, comentarios y la difusión de contenido digital, gracias a= su característica de “hilos” que permite interconectar publicaciones en base a temas o tópicos similares (Guallar<= span style=3D'letter-spacing:-.65pt'> y Traver, 2020). Además, A= razzi et al. (2023) señalan que el lenguaje en redes sociales está en constante evolución y que existe una relación entre la forma en que las personas se expresan en estas plataformas y el impacto que generan en las comunidades digitales. Siendo así, esta dinámica ha convertido a la plataforma en un valioso recurso para la investigación en diversos temas sociales, facilitando el filtrado y difusión de contenidos. El formato predominante de datos en X, basado en texto plano, simplifica el análisis = de contenido. Sin embargo, la plataforma= ofrecía una interfaz de programación de aplicaciones (API, por sus siglas en inglés) gratuita para la búsqueda y extracción de contenido; ahora este acceso es de pago, lo que presenta limitaciones a las investigaciones que solían utilizar datos = de X a gran escala.

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Por otra parte, la ciencia de datos busca generar conocimiento a través<= span style=3D'letter-spacing:-.2pt'> de la extracción y análisis = de información, utilizando técnicas estadísticas y matemáticas que facilitan la interpretación precisa de los datos y resultados (Orellana et al., 2023). Esto la convierte en una herramienta funda= mental para la toma de decisiones informadas (Azuela y Ayala, 2019).

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El Procesamiento del Lenguaje Natural (PLN) se enfoca en proporcionar métodos p= ara analizar, modelar y comprender el lenguaje humano. Esta disciplina nos perm= ite aprovechar la vasta cantidad= de textos disponibles en el entorno digital para extraer información valio= sa y enriquecer nuestra compre= nsión de diversos temas. Dentro del campo del PLN, existen técnicas como la= Asignación Latente de Dirichlet (LDA, por sus siglas en inglés), una técnica destacada para el análisis de grandes volúmenes de texto. Este modelo probabilístico transforma un corpus de texto en un conjunto reduc= ido de temas o tópicos, cada uno representado por una distribución de palabras. Es= ta técnica asume que los documentos son una mezcla de tópicos que generan palabras según su distribución de probabilidad. Esta representación procesa eficientemente grandes cantidades de datos, preservando las relaciones estadísticas clave. LDA ha sido utilizada en varias tareas, como clasificación de textos, modelado de tópicos, detección de anomalías textuales, anál= isis de similitud, extracción de palabras clave, entre otras (Blei et al., 2003).

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En este trabajo, se propone utiliz= ar textos de la red social X relacionados con el acoso escolar y el ciberacoso para analizar las palabras y frases más relevantes. A partir de este análisis, se busca contribuir a la generación de guiones para combatir estas problemáticas empleando procesos y herramientas que faciliten = la búsqueda y generación de información. Para ello, se utiliza como base la metodología definida en el proceso transversal estándar para la minería de datos (CRISP-DM, por sus siglas en inglés), considerada un estándar en proyectos de minería de datos. Esta metodología destaca por su simplicidad, estructura, confiabilidad y amplia aplicación en diversos modelos de procesos en varias áreas de conocimiento<= span style=3D'letter-spacing:-.35pt'> (Mancilla-Vela et al., 2020).

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Adicionalmente, el presente documento se estructura<= span style=3D'letter-spacing:-.65pt'> de la siguiente manera: la Sección 2 presenta los trabajos relacionados, la Sección 3 detalla los materiales y métodos utilizados, la Sección 4 expone los resultados del estudio y la Sección 5 plantea las conclusiones y trabajos futuros.

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Trabajos relacionados=

La red social Twitter o ahora llam= ada X es una plataforma de gran importancia para la investigación y el abordaj= e de diversas problemáticas, incluyendo el acoso escolar y el ciberacoso.= Numerosos estudios se han centrado en examinar la información de X relacionada con estas temáti= cas, ya sea obtenida a través de su API, o de conjuntos de datos disponib= les en repositorios digitales. Estos análisis brindan una mejor comprensión de las dinámicas del acoso escolar y el ciberacoso, además de= identificar patrones de comportamiento y desarrollar estrategias de prevención e intervención efectivas.

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Es así como Chen et al. (2022) evaluaron el potencial de X como fuente de datos en la investigación, analizando aspectos como el acceso, costo, habilidades requeridas y calidad de los da= tos. Concluyeron que, si bien los datos de X son relevantes= para diversas áreas de estudio, su obtención puede requerir conocimientos de programación que no todos los investigadores poseen, especialmente con la transición hac= ia una API de pago.

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Alim (2015) investigó tweets relaciona= dos con el ciberacoso, analizando carac= terísticas como el uso de hashtags, URLs, antigüedad = de perfiles y número de seguidores. El estudio reveló que algunos tweets ofrecían consejos sobre cómo enfrentar el ciberacoso, = lo cual podría ser valioso = para prevenir y combatir este problema. De igual manera, Gu= allar y Traver<= /span> (2020) investigaron la curación de contenidos digitales en X, analiz= ando cómo los usuarios buscan, organizan y filtran información en = la plataforma. Su estudio resaltó la utilidad de X, particularmente a través de hilos, para acceder y enriquecer información de diversas temáticas, agregando valor al contenido original.

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Por otra parte, Sanchez y Kumar (2011) realizaron un análisis de sentimientos en tweets relacionados con el acoso escolar, utilizaron la API de X para acceder a los datos y crear un conjunto de entrenamiento p= ara un clasificador Naïve Bayes. No obstante, encontraron dificultades debido a las medidas de protección de X, que eliminaba tweets antes de que pudieran ser analizados, lo que limitó el alcance de su estudio.

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Bayari= y Bensefia (2021) llevaron a cabo un= estudio sobre la detección automática de ciberacoso basado en el análisis de contenido textual. Para ello, exploraron diversas t&eacut= e;cnicas de minería de textos, como la Bolsa de Palabras (BoW, por sus siglas en inglés) y las Características Léxicas y Sintácticas (LSF, por sus siglas en inglés). Utilizaron la red social X como fuente principal de datos y resaltaron la importancia de considerar el idioma en las reglas y estructuras de análisis, ya que este influye significativamente en la forma en que se manifiesta el ciberacoso.

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Un<= /span> enfoque distinto es presentado por Vázqu= ez et al. (2017), donde = realizaron una revisión exhaustiva de modelos emplead= os en la generación automática de diálogos, centrándo= se en la generación de lenguaje natural. Examinaron modelos bayesianos, probabil&ia= cute;sticos, estocásticos y de redes neuronales, y concluyeron que los modelos basados en redes neuronales generan

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respuestas más adecuadas y coherentes en el contexto de un diálogo. Bajo este contexto, Fatima = et al. (2022) llevaron a cabo una revisión exhaustiva sobre la generación de texto utilizando modelos de redes neuronales profundas= . En su estudio, resaltaron la relevancia de GPT-3, un modelo pre-entrenado con una vasta cantidad de textos y parámetros, lo que le permite generar textos de alta calidad en di= versas tareas.

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De esta manera, el presente estudio propone un novedoso enfoque para la prevención del acoso escolar y el ciberacoso, basado en el análisis de grandes volúmenes de datos textuales provenientes de la red social X. A travé= s de técnicas de minería de texto, se busca identificar patrones lingüísticos y semánticos recurrentes en las conversacio= nes relacionadas con estas problemáticas, con la finalidad de generar guiones para cápsulas educativas en temas de educación y prevención del ciberacoso a través de modelos pre-entrenados generativos.

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Materi= ales y Métodos

Para el desarrollo de las cápsulas de aprendizaje para el combate y la prevención del <= span style=3D'letter-spacing:-.2pt'>ciberacoso se ha utilizado como guía la metodología de la empresa IBM definida como el proceso transversal estándar para la minería de datos (CRISP-D= M). Esta metodología consta de seis fases: i) comprensión del negocio, ii) comprensión de los datos, <= span class=3DSpellE>iii) preparación de los datos,

= iv) modelado, v) evaluación, y vi) despliegue. El ciclo de vida tradicional de un proyecto con

esta metodología se describe en la Figura 1.

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Figura 1

Ciclo de vida de proyecto con CRISP-DM


Nota. Adaptado del proceso de la Metodología CRISP-DM por Mancilla-Vela et al (2020).

 

Adicionalmente, se adoptó= ; la especificación del Metamodelo de Ingeniería de Procesos de Sistemas 2.0 (SPEM, por sus siglas en inglés) para representar las fases de la metodología basada = en el ciclo de vida de CRISP-DM. Por lo tanto, la metodología fue representada= en seis fases: i) definición de criterios de búsqueda, ii) extracción de datos, = iii) pre-procesamiento de datos, iv) extracción de palabras clave, v) generación del guion y vi) validación. La Figura 2 ilustra la representación de la metodología.

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Figura 2

Diagrama SPEM de la metodología basada en CRISP-DM=


 

Definici&o= acute;n de criterios de búsqueda

En esta fase fueron establecidos los criterios y parámetros de búsqueda en la plataforma X para asegurar que los d= atos recolectados sean relevantes para el estudio. Estos criterios proporcionan<= span style=3D'letter-spacing:-.2pt'> la información necesaria para la creación = de cadenas de búsqueda específicas de textos en la red social X y son fundamentales para obtener los datos requeridos. Fue necesario aj= ustar estos criterios a las capacidades de la opción de búsqueda avanzada de la plataforma. Entre los criterios que han sido definidos con l= os expertos en el dominio de acoso escolar y ciberacoso están el tipo de publicación, palabras relevantes, hashtags, lenguaje y secció= n.

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Extracc= ión de datos

El objetivo de esta fase es la extracción de tweets desde la plataforma X utilizando los criterios de búsqueda definidos en la fase anterior. Esta fase es crucial para recopilar datos relevantes a ser analizados y utilizados en etapas posteriores del estudio. El proceso de búsqueda y extracción de datos de la plataforma fue automatizado a través de una interfaz gráfica desarrollada en el lenguaje de programación Python y Selenium<= span style=3D'letter-spacing:-.6pt'> Web Driver. Esta interfaz admite el ingreso de un texto de búsqueda, la descripci&oac= ute;n, la cantidad máxima de tweets a recuperar y las opciones para buscar en tweets recientes e incluir las respuestas.

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Adicionalmente, para explorar<= span style=3D'letter-spacing:-.05pt'> los resultados de búsqueda en la página de X, se utilizó la función de desplazamiento continuo. Esto significa que, al llegar= al final de la página, se cargarán automáticamente m&aacu= te;s post si están disponibles. Cuando no haya más resultados, el = desplazamiento se detendrá en la parte inferior de la página. Internamente, la interfaz automatiza los siguientes procesos: i) validación de sesión, ii) petición web, iii) incl= usión de respuestas y

iv)  visualizaci&= oacute;n de resultados. A continuación, se describen los pasos de la automatización.

 

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•      =    Validación de sesión: Se accede a la página de inicio de X a través de su URL. Si la solicitud es exitosa, se confirma la sesión activa y se procede al siguiente paso. Caso contrario, si la sesión no ha sido iniciada, la solicitud redirigirá automáticamente a la página de inicio de sesión. De ser este el caso, automáticamente se rellenan los campos de credenciales y la cookie de sesión es almacenada en un arc= hivo para futuros eventos.

•      =    Petici&oac= ute;n web: Una= solicitud<= /span> web= es<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 03%; letter-spacing:-.65pt'> generada con= los= pará= ;metros y criterios<= /span> definidos.= Una vez cargados los tweets, se procedió a extraer y almacenar la información relevante. P= ara ello, ha sido aprovechada la estructura de la página web de X, donde cada tweet se encuentra en un elemento HTML con el atributo data-testid =3D “tweet”. Todos los elementos similares fueron identificados y se obtuvieron los detalles de cada tweet, como el texto, la cantidad de “me gusta”, re-tweets, enlaces, etc. Además, la función de desplazamiento fue utilizada para cargar los resultados hasta alcanzar la cantidad requerida o alcanzar el número de tweets existentes.

•      =    Inclusión de respuestas:= En caso de haber seleccionado la opción de incluir respuestas, a través de una petición web, se extraen las respuestas de cada tweet hasta alcanzar el número máximo de respuestas definido. Son almacenadas únicamente las respuestas al tweet original realizado por el mismo usuario, evitando así las respuestas irrelevantes o inapropiadas que= no aportan al desarrollo del hilo.

•      =    Visualización= de r= esultados: Los r= esultados obtenidos e= n la b= úsqueda son v= isualizados<= /span>

en la interfaz gráfica.

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Pre-procesamiento de datos

En esta etapa, tras finalizar la e= tapa de búsqueda y extracción, los datos fueron almacenados en un conjunto de datos y posteriormente en un objeto DataFrame de la librería Pandas para su manipulación media= nte Python. Luego, se inició el= pre-procesamiento de los datos recopilados con el obj= etivo de limpiar y preparar los textos con el objetivo de optimizar los resultado= s en la generación del guion. Para ello, en la Tabla 1 se describen las técnicas de PLN utilizadas.

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Tabla 1

Técnicas de pre-procesamien= to utilizadas

 

= TÉCNICA

= DEFINICIÓN

Convertir el texto a minúsculas.=

Esta técnica transforma t= odas las letras del texto a minúsculas con el objetivo de normalizar y facilitar el análisis textual.

Eliminar hashtags.

Consiste en eliminar las palabras o frases precedidas con el símbolo “#” o hashtag. Si bien se utilizan

para agrupar publicaciones, no tienen relevancia para el resto del estudio= .

Eliminar correos electrónicos.

Esta técnica consiste en eliminar los correos electrónicos presentes en los registros, ya que no contribuyen al análisis posterior.

Eliminar <= /span>signos de puntuación y caracteres especiales.

= Tiene como = objetivo reducir = la dimensionalidad, = simplificar y = normalizar los = datos. Consiste = en eliminar

de = los registros todos los signos de puntuación<= span style=3D'letter-spacing:-.1pt'> como puntos, comas, signos de interrogación, etc.

Eliminar números.

Los números presentes en los registros no aportan ninguna información val= iosa para el estudio, por lo tanto, con esta técnica, fu= eron eliminados todos los caracteres numéricos de los registros.

Eliminar Stopwords.

Consiste en remover palabras comunes de un texto que no aportan un significado relevante al estudio,<= span style=3D'letter-spacing:2.0pt'> como “el”, “la”, “de”, etc. Esto reduce la dimensionalidad de los datos y destaca las palabras relevantes.

Lematizar.

= Se reducen = las palabras = a su = forma base = o lema, = que es = la forma = en la = que se = encuentra en = un diccionario.

Por ejemplo, el lema de “corriendo” es “correr”.

Análisis de sentimientos.

Esta técnica tiene como objetivo determinar l= a actitud o emoci&oacu= te;n que expresa un texto. Esta actitud o emoción puede ser positiva, neutral o negativa, dependiendo de la herramienta utilizada.

 

Extracc= ión de palabras clave

En esta etapa, se utilizó el modelo LDA de la librería Gensim en Python para identificar las palabras más rel= evantes en los textos. Para determinar el número óptimo de tópi= cos ha sido utilizado el indicador de coherencia LDA, buscando el valor más cercano a 1. Las pruebas fueron realizadas con un rango de 1 a 30 tópicos. Una vez identifica= do el número óptimo de tópicos con el mayor indicador de coherencia, se utilizaron las palabras más importantes de cada tópico. Para este estudio, se ha establecido un límite de 50 palabras clave en total. Por ejemplo, si el número óptimo de tópicos fuera 10, serían seleccionadas 5 palabras de cada tópico.

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Generac= ión del guion

Para la generación del guio= n, fue utilizado el modelo generativo pre-entrenado Transformer de tercera generación denominado “text-davinci-003” de la empresa OpenAI a través de su API y el lenguaje de programación Python. Este modelo es ideal para tareas que requieren seguir instrucciones detalladas sin necesidad<= span style=3D'letter-spacing:-.75pt'> de ejemplos previos, además de manejar una amplia ventana de conte= xto de hasta 4,097 tokens. Su entrenamiento previo garantiza la generación = de contenido relevante y coherente.

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OpenAI<= span lang=3DES style=3D'letter-spacing:-.05pt'> ofrec= e recomendaciones para el uso efectivo de sus modelos de generación de texto. Para esta tarea, fueron consideradas las siguientes:

 = ;

•      =    Utilizar un modelo adecuado: “text-davinci-003” es recomendado para generación de tex= to y modelos como “code-davinci-002” para generación de código.

•      =    Poner instrucciones al= inicio: Separar las instrucciones del contexto mejora la efectividad del modelo.

•

&nb= sp;

Tabla 4 expone las preguntas formuladas al experto en= la materia utilizando el enfoque antes mencionado. En caso de responder afirmativamente las preguntas,= se considera el modelo como válido.

 = ;

Tabla 3

Meta de la evaluación con el enfoque GQM

 

= META

COMPONENTE

Evaluar

Modelo de generación de guion para cápsulas educativas.

Con el propósito de<= /o:p>

Evaluar la calidad del guion generado.

Desde el punto de vista de

Experto en la materia.

Tabla 4

Preguntas <= /span>y métricas de la evaluación con el enfoque GQM

 

= PREGUNTA

MÉTRICAS

¿El guion generado cumple con sus expectativas en el contexto solicitado?

- Promueve el fortalecimiento de la autoestima.

- Promueve la= empatía.

¿Cumple las características de una cápsula educativa?

- Es un = contenido que = se puede = dar en = unidades pequeñas = de tiempo.

- Es fácil de comprender.

¿Usa las palabras solicitadas?

- El número de palabras solicitadas.

- El número de palabras utilizadas.

 

Resul= tados y Discusión

Para una mejor comprensión,= los resultados fueron divididos en cuatro etapas: i) definición de criterios de búsqueda, donde se exponen los criterios definidos luego del análisis con el experto en la materia; ii)= extracción y pre-procesamiento de datos, donde se exponen los resultados de la extracció= ;n de datos y la aplicación de las técnicas de pre-procesamiento; iii) extracción de palabras clave, donde se exponen los resultados de la extracción de palabras clave con la técnic= a LDA y iv) gener= ación y validación del guion, donde se expone el guion generado con el modelo pre-entrenado y su validaci&oacut= e;n.

&nbs= p;

Definic= ión de criterios de búsqueda

Los criterios de búsqueda f= ueron establecidos en colaboración con un experto en la materia de acoso escolar y ciberacoso, seleccionando información relevante para el estudio sobre el acoso escolar y el ciberacoso en la plataforma X. Se decidió excluir cualqu= ier tipo de enlace de la búsq= ueda, ya que los usuarios suelen compartir contenido externo que no es relevante para este estudio. La Tabla 5 detal= la los criterios definidos para la búsqueda.

&nbs= p;

Tabla 5

Resultados= de los criterios de búsqueda=

 

= CRITERIO

= DEFINICIÓN

CRITERIOS DEFINIDOS

Tipo de publicación

El tipo de publicación en la red social X: Tweets, Retweets, Hilos, Respuestas, etc.

= Hilos.=

Palabras relevantes

El contenido de<= span style=3D'letter-spacing:-.15pt'> las publicaciones a extraer.

R= 20;Bullying”, “Cyberb= ullying”, “Acoso escolar”, = “Ciberacoso”.

Hashtags

Palabras o frases precedidas por el símbolo “#”

utilizadas para categorizar y agrupar tweets.

#bullying, #cyberbullying, #acosoescolar, #ciberacoso, #ciberbullying, #acoso.

Lenguaje

Lenguaje de las publicaciones.

Español.

Sección

Nombre de la sección de la plataforma.

Secciones “Dest= acado” y “Más reciente”.

 

Adicionalmente, se decidió excluir las respuestas de la búsqueda, ya que el enfoque se centra en los hilos de conversación, que son respuestas a un tweet inicial. De esta manera,= los textos de búsqueda que fueron utilizados son los siguientes:<= /p>

•= ;      =    “hilo (#bullying) Lang:es<= span style=3D'letter-spacing:-.55pt'> -filter:links<= /span> -filter:replies”

•      =    “hilo (#cyberbullying) Lang:es<= span style=3D'letter-spacing:-.55pt'> -filter:links<= /span> -filter:replies”

•      =    “hilo (#acosoescolar) Lang:es<= span style=3D'letter-spacing:-.55pt'> -filter:links<= /span> -filter:replies”

•      =    “hilo (#ciberacoso) Lang:es<= span style=3D'letter-spacing:-.55pt'> -filter:links<= /span> -filter:replies”

•      =    “hilo (#acoso) Lang:es<= span style=3D'letter-spacing:-.55pt'> -filter:links<= /span> -filter:replies”

•      =    “hilo (#ciberbullying) Lang:es<= span style=3D'letter-spacing:-.55pt'> -filter:links<= /span> -filter:replies”

Extracción y pre-procesamie= nto de datos

Durante el proceso de automatización de la búsqueda de datos, se filtraron varios anuncios entre los resultados; por ende, la cantidad de caracteres permitidos en un tweet aumentó de 280 a 4,000 caracteres. Debido a esto, se realizaron ajustes durante<= span style=3D'letter-spacing:-.3pt'> el proceso de extracción para mitigar estos problemas.= De este modo, 1,645 tweets fueron recuperados. En la Tabla 6 se aprecia el número de tweets recuperados de acuerdo con los hashtags utilizados.

 = ;

Tabla 6

Cantidad de tweets por hashtag

 

= HASHTAG

CANTIDAD DE TWEETS

#bullying

921

#acosoescolar

342

#acoso

237

#ciberacoso

97

#cyberbullying

32

#ciberbullying

16

Tras el pre-procesamiento de los datos, el análisis de sentimientos reveló que 146 tweets fueron clasificados como positivos y 1,325 fueron clasificados como negativos. Esto indica que el tema del acoso escolar y ciberacoso se asocia predominantemente con sentimientos negativos en la plataforma X. Adicionalmente, la Figura 3 ilustra los hashtags más utilizados en los resultados de la búsqueda, y la Figura 4 ilustra las palabras más comunes en los resultados de la búsqueda.

&nbs= p;

Figura 3

Hashtags más utilizados en los resultados de la búsqueda

 

Figura 4

Palabras <= /span>más populares en los resultados de la búsqueda=


 

Es importante subrayar que el acceso a los datos de la plataforma X se ha convertido en un proceso complejo y costoso. Inicialmente, la API ofrecía un acceso sencillo y gratuito, pero los cambios recientes han limit= ado su uso. Esto ha impulsado a los usuarios a recurrir al web scraping como alternativa para obtener datos. Durante un tiempo, esta alternativa se convirtió en la principal fuente para obtener datos de X. Sin embargo, la plataforma identificó estos puntos <= span style=3D'letter-spacing:-.1pt'>de acceso y tomó medidas para restringirlos. Este cambio en la política de acceso a datos plantea nuevos desafíos para las investigaciones que dependen de la información de X.

&nbs= p;

Extracc= ión de palabras clave

Utilizando la técnica LDA y un límite de 50 palab= ras clave, se calculó el indicador de coherencia para determinar el número óptimo de tópicos. La Figura 5 muestra los indicadores en un rango de 1 a 30 tópicos, donde un valor cercano a 1 indica la cantidad óptima de tópicos.

 = ;

Figura 5

Indicador <= /span>de coherencia para el número óptimo d= e tópicos


 

Como se observa en la Figura 5, el indicador de coherencia alcanza su punto máximo

alrededor del número 11, lo que sugiere que el número óptimo = de temas a considerar es 11.

&nb= sp;

Generac= ión y validación del guion

Con el modelo seleccionado “text-davinci-003” de la empresa OpenAI se ha generado el guion para la cápsula educativa. Este modelo responde a instrucciones específicas para la creación = de contenido que cumpla con los requisitos solicitados. Adicionalmente, el mod= elo sugirió recursos visuales que acompañan al texto del guion; estos elementos se presentan en la Tabla 7.=

&nbs= p;

Tabla 7

Guion generado con el modelo “text-davinci-003”<= /span>

 

= NARRADOR

RECURSOS

Esta es la historia de un niño de 8 años.

(Imagen de un ni&ntil= de;o de 8 años)<= /span>

Él <= /span>vive con su familia en una casa normal.

(Imagen de una famili= a feliz)

Va a la escuela y trabaja duro para aportar algo a su comunidad.<= /o:p>

(Imagen de un ni&ntil= de;o en la escuela)

Un = día, él publicó una foto en una red social.

(Imagen de un ni&ntil= de;o en una computadora)

Pero alguien comienza a acosarlo.<= o:p>

(Imagen de un ni&ntil= de;o con una mirada triste)

El niño se siente triste y confu= ndido.

(Imagen de un ni&ntil= de;o llorando)

Él = decide hablar con alguien de su familia para pedir ayuda.

(Imagen de un ni&ntil= de;o hablando con un adulto)<= /o:p>

Él = denuncia el caso y lleva pruebas para respaldar su denuncia.

(Imagen de un ni&ntil= de;o hablando con un policía)

Él habla con sus amigos sobre el tema y les ex= plica lo que deberían hacer si alguna vez se enfrentan a una situación similar.<= /o:p>

(Imagen de un niño hablando con un grupo de amigos)

El niño se siente mejor al saber que hay gente que lo entiende y que puede ayudarlo.

(Imagen de un ni&ntil= de;o sonriendo)

Él <= /span>aprende que siempre puede contar con su familia y amigos para seguir adelante.

(Imagen de un ni&ntil= de;o abrazando a un adulto)

Él aprende que la autoestima es importante y que siempre hay alguien que puede ayudar.

(Imagen de un ni&ntil= de;o sonriendo)

Gracias por trabajar ju= ntos para entender este tema y ayudar a los niños a pensar de forma positiva.

(Música de fondo)

 

A simple vista, el guion = generado cumple con las caracter&iac= ute;sticas esenciales de las cápsulas educativas: es conciso, claro y enfatiza los aspectos clave del tema, alineándose con el contexto requerido. Además, el experto en la materia ha respondido afirmativamente todas las peguntas de la evaluación pre= parada con el enfoque GQM, lo cual val= ida que el mensaje transmitido es adecuado para el objetivo de contribuir al aprendizaje y la prevención del ciberacoso con el formato de cápsulas educativas, tal como se muestra en la Tabla 8.

 = ;

Tabla 8

Evaluación de resultados con el enfoque GQM

 

PREGUNTA=

MÉTRICAS

RESULTADOS

 

&iqu= est;El guion generado cumple con sus expectativas en el contexto solicitado?

<= span lang=3DES style=3D'font-size:8.0pt;mso-bidi-font-size:11.0pt;line-height:= 103%'>- Promueve el fortalecimi= ento de la autoestima.

- Positivo.

- Promueve la empatía.

- Positivo.

 

PREGUNTA=

MÉTRICAS

RESULTADOS

 

&iqu= est;Cumple las características de una cápsula educativa?

<= span lang=3DES style=3D'font-size:8.0pt;mso-bidi-font-size:11.0pt;line-height:= 103%'>- Es un contenido que se puede dar en unidades pequeñas de tiempo.

- Positivo.

- Es fácil de comprender.

- Positivo.

 

¿Usa las palabras solicitadas?

- = El número = de palabras = solicitadas.=

- 27 palabras de 36.

- El número de palabras utilizadas.

- = Usa el = 75% de = las palabras.

 

Conclusiones

A pesar de las limitaciones para la extracción de datos, este estudio ha demostrado la factibilidad de utilizar modelos pre-entrenados en la creación de cápsul= as educativas para el combate del<= span style=3D'letter-spacing:-.1pt'> ciberacoso. La técnic= a LDA resultó efectiva en la extracción de palabras clave de textos cortos, siempre y cuando se realice un adecuado pre-procesamiento de los datos. En cuanto a la generación de contenido textual, se demostró la eficacia del modelo “text-davinci-003” en la creación de guiones coherentes y relevantes a partir de instrucciones precisas.

&nbs= p;

Adicionalmente, el proceso de extracción de datos relacionados con el acoso escolar y el ciberacoso = ha sido automatizado. Se destacó la importancia de definir adecuadamente los términos de búsqueda para obtener datos relevantes, considerando= la evolución y actualización continua de la plataforma. A través de un análisis riguroso y= la colaboración con un experto en la materia, se validaron los resultad= os obtenidos, confirmando que las cápsulas educativas generadas transmi= ten un mensaje adecuado para prevenir y combatir el ciberacoso.

&nbs= p;

Este estudio demuestra el potencial de combinar técnicas de minerí= ;a de texto y modelos de lenguaje pre-entrenados para generar conteni= do educativo relevante y efectivo en la lucha contra el ciberacoso. Sin embarg= o, es necesario considerar los costos tanto de la extracción de datos como del uso del modelo pre-entrenado. Actualmente, existen varios modelos pr= e- entrenados que no requieren suscripción, son de acceso gratuito y pu= eden ser utilizados para propósitos<= /span> similares. Igualmente, es posible incorporar nuevas técnicas de extracción de palabras clave y comparar su efectividad. De esta manera, las cápsulas educativas pueden ser generadas de una forma más económica y en mayor cantidad, contribuyendo activament= e a la lucha contra el ciberacoso.

&nbs= p;

A futuro, se propone ampliar el al= cance de este estudio explorando alternativas más accesibles y diversas. En particular, se plantea evaluar el potencial de modelos de lenguaje libres p= ara la generación de guiones de cápsulas educativas. Este tipo de modelos libres podrían representar una solución viable en términos de recursos económicos, democratizando la creación de contenido educativo. Además, se propone la experimentación de otras técnicas de extracción de palabras clave o modelos pre-entrenados basados en la arquitectura Transformers. La comparación del rendimiento de estas técnicas con la técnica LDA permitir&aa= cute; determinar la más adecuada para el objetivo de la investigació= ;n. Finalmente, se propone la exploración de diversas fuentes de datos, = como otras plataformas de redes sociales, donde se podría extraer conteni= do relacionado con el acoso escolar y el ciberacoso.

&n= bsp;

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).

&nb= sp;

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