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<=
/span>
<=
span
lang=3DES>https://doi.org/10.37815/rte.v35n2.1049
<=
span
lang=3DES>Artículos originales
Generación de<=
span
style=3D'letter-spacing:-.7pt'> Texto Guía para la Detección Automatizada d=
el
Acoso y el Ciberacoso
Text Generation Guide for Automated Detection of Bullying =
and Cyberbullying
Marcos Orellana1 https://orcid.org/0000-0002-3671-9362 , =
Jorge Luis Zambrano-Martinez1 <=
/p>
<=
span
lang=3DES>https://orcid.org/0000-0002-=
5339-7860 , Ronny Marcelo Calle Andrade 1 https://orcid.org/0009-0004-9825-<=
span
style=3D'letter-spacing:-.1pt'>155X ,
Amanda Roldan 1 https://orcid.org/0009-0009-3548-2=
269 , =
span>Andrés Nicolas
Tirado Jarama 1
https://orcid.org/0009-0002-2269-4608
1 Laboratorio de Investigación y Desarrollo en
Informática (LIDI) Universidad del Azuay , Cue=
nca,
Ecuador =
marore@uazuay.edu.ec<=
/span>, jorge.zambrano@uazuay=
.edu.ec ,
=
ronnycalle@e .=
s.uazuay.edu.ec , amy.3107@es.uazuay.ed=
u.ec , =
andresnicolas1231@es.uazuay.edu.ec
=
=
Esta o=
bra
está bajo una licencia internacional Creative
Commons Atribución-=
NoComercial
4.0
Enviado: 2023/07/14 Aceptado: 2023=
/08/22
Publicado: 2023/10/15
Resumen
En la actualidad, el acoso entre
los niños es un grave
problema social e incluso actúa como uno de los
principales problemas de la salud mental. Por lo tanto, se han detectado va=
rios
tipos de acoso, entre ellos están el acoso físico, verbal, so=
cial
y el cibernético que utiliza la tecnología al alcance
del agresor para difundir el daño y hacer que la víctima esté co=
nstantemente acosada. La inteligencia artificial=
y el
acoso son temas ampliamente estudiados e investigados, pero regularmente no=
son
utilizados de manera conjunta para llegar a mitigar la problemática =
del acoso. Con esto en mente, el propósito de esta investigación es identificar las palabras claves que son extraídas de diferentes<=
span
style=3D'letter-spacing:-.75pt'> medios
audiovisuales cuyo tema principal es el acoso
y el ciberacoso para crear campos semánticos correspondie=
ntes
al texto extraído del video y generar un texto guía para la detección automatizada del=
acoso y el ciberacoso como apoyo a los profesionales.
Palabras <=
/span>clave: Acoso, Ciberacoso, Inteligencia ar=
tificial, OpenAI ,<=
span
style=3D'letter-spacing:-.65pt'> YAKE.
<=
/o:p>
Abstract
C=
urrently , bullying among children is a serious social problem and even =
span> acts as one of the primary mental health problems . Therefore , various types of bullying
have been detected , including physical <=
span
style=3D'letter-spacing:-.1pt'>, verbal, social, and cyberbullying , which uses technology within reach of the aggressor =
to broad=
cast the damage and make the victim
constantly harassed =
. Artificial inte=
lligence
and bullying are widely studied <=
span
style=3D'letter-spacing:-.4pt'> and researched , but they are<=
span
style=3D'letter-spacing:-.35pt'> not regularly used together =
to mitigate =
the bullying problem . Nevertheless , <=
/span>this research aims to identify the keyw=
ords extracted from different =
audiovisual media whose central theme is bullying and cyberbullying t=
o create s=
emantic fields <=
span
class=3DSpellE>corresponding to the text extracted
from the video and =
generate a guide for automated bullying detection as support for professionals .=
Keywords: Bullying , Cyberbullying<=
/span>, Artificial intelligence , OpenAI , YAKE.
Introducción
El acoso o comúnmente conoc=
ido en
la actualidad como “bullying ” se ha
convertido en uno de los temas que más preocupa a los padres de fami=
lia, educadores de las instituciones y
psicólogos, debido a que se ha incrementado el acoso a las
víctimas sin distinción de edad, género o nacionalidad,
afectando tanto en el ámbito social como económico.
&nbs=
p;
Las víctimas de acoso tienen
mayor probabilidad de presentar dificultades con la interacción soci=
al
en el entorno y ser internadas en centros de salud mental por experimentar
problemas de ansiedad, depresión, ideación e intentos suicida=
s (Heikkilä et al., 2013; Lucas- Molina et al., 202=
2).
Los jóvenes que acosan a otras personas demuestran conductas m&aacut=
e;s inapropiadas que aquellos que son intimidados. Por lo tanto, varias investigaciones han estudiado
el impacto de la intimidación que suelen presenciar los indiv=
iduos
o espectadores del acoso. Estos espectadores reportan
un aumento de la ansiedad,
la hostilidad, la depresión, la paranoia,
y el aislamiento emocional (Zang et al., 2020;<=
span
style=3D'letter-spacing:-.2pt'> Alrajeh =
et al.,
2021).
Es importante considerar que exist=
en
programas enfocados específicamente sobre la intimidación, en
algunos países existen leyes para afrontar el acoso que ocurre en
distintos sectores, en especial
en las escuelas. Esta=
s políticas guían l=
as acciones
e intervenciones dentro de la organización
estudiantil referentes a la intimidación. Así, en un estudio
realizado por Landstedt y Pearson (2014), en el=
que
se invitó a participar a las escuelas secundarias con estudiantes de edades comprendidas entre 12 y 16 años, a realizar una encuesta con el objetivo de determinar el<=
span
style=3D'letter-spacing:-.05pt'> número de víct=
imas y victimarios. Los resultados determinaron que el 15.1% de los adolescentes fueron
víctimas de acoso, el 17.2% informó haber recibido ciberacoso=
y el 19.6% de los encuestados sufrió =
tanto acoso
como ciberacoso. Otro resultado importante es el acoso en la población femenina, ya que el 59.4=
% de
las mujeres han recibido acoso por la imagen corporal, frente al 26.2% en l=
os
hombres.
En la actualidad, la fusión=
de
investigaciones en varios campos, como la informática y las ciencias sociales, ayuda a ana=
lizar
distintos problemas en la sociedad. Por ello, varios investigadores han
comenzado a estudiar los distintos roles de intimidación en redes
sociales, internet y correos electrónicos a través del uso de algoritmos de aprendizaje automático tal como
lo mencionan (Akhter et al., 2018; Chatzakou et al., 2017; Chelmis et al., 2017; Dhungana S=
ainju et al., 2021; N=
ikiforos
et al., 2020).
Con la ayuda de la inteligencia
artificial es posible encontrar patrones de este tipo de comportamiento para
llegar a mitigar y reducir el acoso de los atacantes a las víctimas.=
Por
lo que en esta investigaci=
ón se propone generar
textos guías, a través de herramientas que utilizan
&nbs=
p;
técnicas de minería de texto aplicadas a
transcripciones de videos-audios de calidad para la creación de material audiovisual que facilite la reducción del acoso y ciberacoso en la sociedad. Para lograr esta propuesta, es necesario desarrollar las siguientes=
etapas:
i) Extracción del texto
de los videos alojados en la plataforma YouTube que estén relacionad=
os
con el ciberacoso mediante interfaces de programación de aplicaciones
(API) especializadas; ii ) Extracción de =
palabras o frases que sean mayormente representativas =
de los textos extraídos; iii )
Creación de campo=
s semánticos para alimentar=
la API de OpenIA ; =
y iv ) Generación del guion con base a la
combinación de las palabras o frases.
El artículo se estructura d=
e la
siguiente manera: los trabajos relacionados se presentan en la siguiente
sección, luego es presentada la metodología utilizada en el
trabajo, después se exponen los resultados y en la última sección se presentan las conclusiones y los trabajos futuros.
Trabajos Relacionados=
Existen trabajos enfocados al estu=
dio de
las características del acoso y ciberacoso con la finalidad de gener=
ar
modelos o herramientas para combatirlas. Los principales avances se refiere=
n a la detección, clasificación =
span>y predicción del ciberacoso en plataformas en línea, como las redes sociales. A
continuación, se exponen los estudios relacionados a la
generación de herramientas y modelos utilizando varias fuentes de da=
tos.
&nbs=
p;
Dinakar et al. (2011) proponen una novedosa técnica para la
detección del ciberacoso, que consiste en descomponer los textos en
varios temas sensibles y generar subproblemas de clasificación.
Construyeron un corpus por medio de la extracción de 4,500 comentari=
os
de videos controversiales =
de YouTube para generar modelos de clasificación binarios y multiclases.
Luego de comparar
los modelos, los de clasificación binaria
tuvieron una mayor exactitud que los multiclases, logrando una
exactitud del 80%.
Por=
otra <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>parte, Zhao et al. (2016) =
desarrollaron =
un marco <=
span
lang=3DES style=3D'letter-spacing:-.5pt'> de aprendizaje para la detección de
ciberacoso en redes sociales, específicamente en Twitter. Para ello,
definieron una lista de palabras ofensivas y la ampliaron a través de
incrustaciones de palabras (Word Embeddings ). <=
span
style=3D'letter-spacing:-.1pt'>Estos datos alimentaron un modelo de clasificación basado en una máquina de vector de soporte
(SVM), logrando una precisión de 76.8%. Por otro lado, Singh =
et
al. (2016) implementaron un mar=
co de
fusión de información probabilística que utilizó
puntajes de confianza y factores referentes al ciberacoso. Utilizaron un
subconjunto de datos etiquetado de 4,865 mensajes de Twitter sobre la tendencia
a sufrir ciberacoso. Finalmente, generaron tres modelos, un clasificador
basado en la mayoría o categoría principal denominado ZeroR, e=
l método de fusió=
;n y su modelo de marco de fusión
probabilística, este último demostró un 89% de exactit=
ud.
Mahlangu et al. (2018) revisaron distintos m&eac=
ute;todos en cuanto a la detección automática de ciberacoso, y además, se
identificaron algunos factores, categorías y fuentes de datos en
internet. La mayor limitación que encontraron los investigadores
corresponde a las escasas fuentes de datos provenientes de imágenes o
videos, que pueden ser de utilidad para nuevos estudios en la detecci&oacut=
e;n
de ciberacoso.
 =
;
Uludasdemir y Kucuk (2019) =
realizan un estudio sobre la experiencia =
span>de los adolescentes con el ciberacoso y el nivel de
conciencia de los padres sobre este problema. Por lo que identificaron que el uso del internet
por parte de los adolescentes y el uso de las redes sociales presenta un alto índice de ciberacoso y ciber victimización experimentados por los adolecientes en un desconocimiento p=
or
parte de los padres sobre este problema.
&nbs=
p;
Rezvani et al. (2020) presentan un método para orquestar los proc=
esos,
modelos y resultados en la detección inteligente del ciberacoso. El =
uso
del método para orquestar les permitió maximizar el uso del
contexto y aprovechar fuentes externas. Además,
utilizaron un modelo de red de memoria
a corto-largo plazo
(LSTM) que fue alimentado mediante
conjuntos de datos de Twitter e Instagram. Como resultado, el modelo presentado por los autores
presenta una exactitud<=
span
style=3D'letter-spacing:-.05pt'> del 87%. De igual manera,
Gada et al. (2021) presentan una arquitectu=
ra basada en redes neuronal=
es convolucionales con redes de memoria a corto-largo plazo
(LSTM-CNN) para la detección de ciberacoso. El modelo fue cre=
ado
a partir de la extracción de textos de Twitter, además generaron un bot =
para la aplicación de Telegram que alerta al usuario al detectar
la presencia de palabras ofensivas
o groseras. El rendimiento del modelo fue evaluado a través
del
análisis de la curva característica operativa del receptor (ROC), dando una exactitud de 97%.
Otro enfoque para la detecci&oacut=
e;n de
ciberacoso es descrito por Caicedo (2023), que desarrolla un método =
semisupervisado para detectar, clasificar y anotar te=
xtos
extraídos de entornos digitales mediante un proceso de evaluaci&oacu=
te;n
automática de su calidad para detectar
y brindar apoyo especializado a usuarios de redes sociales que publi=
can
textos con contenido suicida. =
span>
&nbs=
p;
Los trabajos mencionados describen algu=
nas de las técnicas y modelos utilizados para la detección, predicción y ayuda para el ciberacoso a los profesionales en solucionar este problema
social. Además,<=
span
style=3D'letter-spacing:-.75pt'> mencionaron las limitacion=
es existentes al afrontar estos problemas, por lo que no consiguen la información generada de otras fuentes multimedia, como los videos e imágenes.
Sin embargo, no existen estudios
que utilicen generadores de textos modernos
en la creación de
textos guía para videos que promuevan el combate contra el acoso y
ciberacoso.
Materiale=
s y Métodos
En esta investigación, la
metodología para realizar la gener aci&o=
acute;n
de guiones base obtenidos a través de la reproducción de vide=
os
con temáticas de acoso y ciberacoso, con la finalidad de prevenir y
disminuir esta problemática a nivel escolar fue empleada utilizando<=
span
style=3D'letter-spacing:2.0pt'> las siguientes fases: i) Extracci&oa=
cute;n
del texto de diferentes videos relacionados con el acoso y ciberacoso, i=
i ) extracción de las palabras más relevantes e importantes a través de la herramienta
Yet =
=
Another Keyword Extractor (YAKE), iii ) Construcción de los campos semánticos utilizando
el generador de texto de OpenAI y<=
span
style=3D'letter-spacing:-.1pt'> iv ) Generación del guion adecuado
combinando palabras o frases para el acoso y ciberacoso.=
Extracción=
del texto proveniente de videos seleccionados
Como fase inicial, se tuvo que rea=
lizar
una búsqueda de los videos en la plataforma YouTube, seleccionando los primeros videos que presentó el algoritmo que utiliza la plataforma y alineados
con la temática de este estudio. Luego, se elaboró una videoteca que contenía distintos
elementos audiovisuales sobre el acoso y el ciberacoso conjuntamente=
con
la duración y el código=
con el que se puede encontrar dichos element=
os audiovisuales, como se puede
apreciar en la Tabla 1.
Para la extracción del texto
desde los videos seleccionados se utilizaron tres APIs, YouTube translate =
, Amberscript y Chequeabot . Estas aplicaciones extraen los textos del
elemento audiovisual ut=
ilizando algoritmos e inteligencia=
artificial con una alta precisión para la creación de párrafos =
con
sentido semántico, cuya finalidad es reducir el número de err=
ores
posibles al realizar la transcripción del video al texto en varios
lenguajes y sus distintas pronunciaciones particulares de individuos de un
mismo idioma.
Tabla 1
Videoteca <=
/span>sobre acoso y ciberacoso
NÚMERO VIDEO
TÍTULO
DURACIÓN
CÓDIGO
1 =
p>
“Cómo ev=
itar
el acoso escolar”. =
3 min 27 sec.
nTRZIMfprBI
2
“Cómo actuar
frente al acoso
escolar. José Antonio Luengo, psicólogo y profesor”. <=
/p>
4 min 51 sec.
=
2Un_J3EhUqk =
3
“Cómo prevenir el bullying en el aula”.
3 min 53 sec.
=
FHzAnNQBzA8 =
4
“C=
yberbullying : qué es y cómo prevenirlo”.
1 min 59 sec.
=
zdJIsMbwU08 =
5
“Cyberbullying - ¿Qué es y qué hacer?”.
4 min 22 sec.
=
Gfsuj6wlg8Y =
6 =
p>
“¿Qu&eac=
ute; es el
Bullying =
?”.
2 min 07 sec.
=
YGMpU35TwNY =
7
“C=
yberbulling , Ciberacoso y depresión=
8221;.
1 min 18 sec.
=
3_e_M0ntZpM =
8 =
p>
“Qué hac=
er
contra el Bully=
ing ”.
3 min 06 sec.
=
AwEqxDbTJu0 =
Sin embargo, los párrafos fueron
minuciosamente revisado=
s y supervisados al momento
de la extracción por los expertos en temas de acoso y ciberacoso con la finalidad de disminuir l=
as
probabilidades de errores semánticos y errores de transcripció=
;n.
Extracción=
de palabras clave
Una vez obtenidos
los textos transcritos de los videos,
es necesaria la extracción
de las palabras claves provenientes de los párrafos creados por los
transcriptores. Por este motivo, se utilizó un método de
extracción automática de palabras importantes basado en
características estadísticas de texto extraídas de tex=
tos
individuales sin ninguna supervisión para seleccionar las<=
span
style=3D'letter-spacing:-.5pt'> palabras
claves más relevantes de un texto denominado YAKE (Campos et al., 2020). Además, esta herramienta no necesita de un entrenamie=
nto
previo con un conjunto particular de documentos, ni de diccionarios, o corp=
us
externos (Campos et al., 2018). Con la herramienta YAKE se facilita la extracción de palabras clave que
presentan un vínculo con el acoso y el ciberacoso, siendo las palabr=
as
de mayor relevancia y frecuencia. Para brindar una mejor interpretaci&oacut=
e;n
de las palabras claves obtenidas de los videos, se desarrolló un res=
umen
representado en la Tabla 2.
Además, como se observa en =
la
Tabla 2, se obtuvo un puntaje de las palabras clave a través de la
Ecuación 1 presentada por Campos et al. (2020):
S(kw ) =
=3D Πw =
ϵ kw <=
span
lang=3DES style=3D'font-size:10.0pt;line-height:36%;font-family:"Cambria",s=
erif;
mso-fareast-font-family:Cambria;mso-bidi-font-family:Cambria;letter-spacing:
-.8pt'> S(w))  =
; <=
/i> =
span>
TF(kw )×(1+∑w =
ϵ =
sub> kw S(w))
(1) <=
/span>
Donde S(w) es el peso o la puntuación de la palabra y TF(kw ) es la
frecuencia de la palabra clave que se presenta en el texto analizado acorde=
a
ecuaciones previas del trabajo de Campos et al. (2018). Esta ecuación
nos indica cuáles palabras tienen un mayor significado semánt=
ico
dentro del texto analizado acerca del tema de acoso y ciberacoso, para
facilitar la creación de los campos semánticos.
Tabla 2
Matriz de las palabras usadas
con mayor frecuencia y relevancia
NÚMERO
VIDEO
POSICIÓN
PALABRAS
PUNTAJE
RELEVANTE
=
1
1 =
p>
acoso escolar
=
0,02346838 =
Si =
2 =
p>
=
escuela =
=
0,051890499 =
No =
2 =
p>
=
acoso =
=
0,058818566 =
Si =
4 =
p>
=
sociedad =
=
0,07814722 =
No =
5 =
p>
=
maltrato =
=
0,081237283 =
Si =
…
…
…
…
…
=
8
1 =
p>
tus amigos recuerdan
=
0,000132623 =
No =
2
agresiones físicas segura=
mente
=
0,000202302 =
Si =
3
obtener ayuda profesional
=
0,000202302 =
No =
4
apodos insultos amenazas
=
0,000242478 =
Si =
5
hacerte
sentir
insignificante =
=
0,000242478 =
Si =
Como YAKE
extrae las palabras claves basado en estadísticas, existen palabras
repetidas, por lo que fue necesario aplicar una
función de deduplicación de datos para la eliminació=
;n de
duplicados exclusivos de los datos y reducirlos. Entre ellos se tiene distancia de Leve=
nshtein ,
distancia de Jaro-Winkler y Sequence Matcher =
i>. Además, otros parámetr=
os
para la extracción de las palabras importantes fueron el máxi=
mo
tamaño de n-gramas, el lenguaje del texto, el límite de la
puntuación normalizada de la función de =
deduplicación , y el
número máximo de palabras clave.
Construcció=
;n de los cuerpos
semánticos =
Esta fase corresponde a la creación de los c=
ampos
semánticos una vez que se ha co=
mpletado la extracción de las palabras clave. Para ello, se seleccionaron las palabras clave más
relevantes a través de la evaluación de sus puntajes, tomando en cuenta que, a menor puntuación,
mayor es la relevancia de la palabra
dentro del texto.
Adicionalmente, algunas<=
span
style=3D'letter-spacing:-.2pt'> palabras
fueron filtradas por un experto en temas de acoso y ciberacoso, debi=
do a
que presentaban una baja relevancia en la construcción de los cuerpos
semánticos.
Una ve=
z que las palabras
claves fueron seleccionadas, se utiliz&oacu=
te; el módulo generativo de texto de la compañía OpenAI . Esta empresa se caracteriza por promover y desarrollar productos
relacionados con Inteligencia Artificial (IA=
) como transformadores pre-entrenados=
generativos (GPT).
Este m&=
oacute;dulo de OpenAI es=
capaz de generar
textos a través de palabras o porciones de texto proporcionadas por el usuar=
io. Además, existen varios modelos
disponibles a diferentes precios y capacidades, siendo así Text-Davinci-003 , el más
sofisticado, preciso, costoso y basado
en la familia GPT versión 3.5. Este modelo se especializa en
tareas de lenguaje natural como la traducción y la generación
textual. Además, se
modificaron algunos de los parámetros más relevantes dentro
del módulo de generación de textos, como la temperatura, el núme=
ro máximo de tokens y el modo, la finalidad de estos parámetros se describe en la Tabla 3. También existen otros modelos con menor precisión, pero
más rápidos y económicos, como Text-Curie-001 y Text-B=
abbage-001
(Efrat et al. 2022). Además de seleccionar el modelo Text-Davinci-003.
Tabla 3
Parámetros
del módulo generativo de textos
PARAMETROS
DESCRIPCIÓN =
=
Temperatura =
Oscila entre 0 y 1, a medida que aumenta el valor, aumenta la creatividad de la respu=
esta.
Número de tokens
El máximo número de tokens a generar, entre
1 y 4000 tokens permitidos para el modelo Text-Davinci-003.
=
Modo =
Especifica la tarea que va a realizar el modelo: completar,
responder, agregar
o editar texto.
En<=
/span> la Figura 1 se evidencian los cuerpos semánticos generados por el módulo=
de OpenAI =
que puede ser implementado mediante un lenguaje
de programación a través de una
API o a través de su
plataforma en línea. Los cuerpos semánticos generados fueron
evaluados por expertos en las temáticas de acoso y ciberacoso para
verificar su contenido y consistencia.
Figura 1
Construcción
de campos semánticos en el módulo de OpenAI
Generaci&o=
acute;n del guion combinando frases o palabras
extraídas <=
/h1>
Una=
vez que las palabras claves y los cuerpos semánticos fueron validados por el experto, como fase final =
de la metodología realizada para este <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>estudio, se generó =
el guion =
para <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>el combate del acoso y ciberacoso a través de los cuerpos semá=
nticos
generados en la fase anterior. Para la generación del guion se
desarrolló un script en el lenguaje de programación Python que
concatena las palabras claves=
con sus correspondientes cuerpos sem&aa=
cute;nticos a través de las APIs descritas anteriormente.
Resultados
En los resultados se encontr&oacut=
e;
que, la relación entre el acoso o el ciberacoso, con la
extracción de datos o variables
para su posterior proce=
samiento aplicando IA son muy escasas.
Sin embargo, se logra rescatar algunos documentos donde se evidencia esta
relación, y esto brindó =
los indicios
para la elaboración de esta investigación. Esto se debe a que la vida humana
se está trasladando cada=
vez a lo virtual, presentando menos frecuencia=
de un acoso físico y la
facilidad del mundo tecnológico para realizar acosos.
&nbs=
p;
El<=
/span> resultado primordial en este estudio es la creación de un texto guía para la elaboraci&oac=
ute;n de un guion de video <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>contra el acoso <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>y el ciberacoso. =
span>Para lo cual, <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>primero se realizó =
la selecci&oacut=
e;n de algunos videos que explican
sobre el tema de acoso y ciberacoso, y luego fue transcrito
el texto completo de los videos seleccionados con herramientas especializad=
as.
Después, se extrajeron l=
as palabras
claves con un sistema basado
en característica=
s para la extracción de las palabras<=
span
style=3D'letter-spacing:-.7pt'> claves plurilingües y seguidamente, la construcción de los cuerpos
semánticos a través de las palabras o fras=
es con
mayor relevancia del texto.
As&=
iacute; pues, el algoritmo YAKE que extrae las palabras clave=
s,
requirió de parámetros como el
idioma en español, el tamaño=
de las gramas =
de tres a cinco, =
con un algoritmo de deduplicación Sequence Meter de un límite igual a 0.9, y que presente las primeras 20 palabras obtenidas. Las palabras cla=
ves candidatas fueron mostrada=
s con un peso o puntaje
cuya mayor relevancia para el texto procesado es la menor posible, tal como se pu=
ede
observar en la Figura 2.
Figura 2
Palabras =
span>claves extraídas de un texto transcrito de
video
Asimismo, se tuvo la necesidad de realizar un filtrado por parte de expertos en el tema a
las
palabras claves que fueron extraídas de las transcripciones. Esto es debido a palabras o frases que no presentan una relevancia para la generación del texto guía como “soci=
edad”, “escolar”, “Cristina del barrio”, entre otros, que, aunque presenten un puntaje inferior marcado, no destacan
para el estudio de esta investigación. Por ende, se redujo la
cantidad de palabras importantes extraídas para la creación de
los cuerpos semánticos y su posterior generación del texto
guía.
No obstante, para la generación del guion para el combate
del acoso y el ciberacoso se desarrolló =
span>un script en Python que permitió la agrupaci&oacu=
te;n correcta
de las palabras clave con sus
propios cuerpos semánticos. Sin embargo, al presentarse una gran cantidad de datos relacionados
al acoso y ciberacoso en la creación de texto guía, se
tuvo que refinar por los expertos para encaminar el contexto del guion y que esté centrado direct=
amente en la prevención del acoso y ciberacoso. En la Figura 3, se puede observar el guion generado utilizando las palabras claves y los cuerpos semánti=
cos
obtenidos con anterioridad.
Figura 3
Generación
del texto guía a través<=
span
style=3D'letter-spacing:-.15pt'> de los campos
semánticos con OpenAI =
p>
Conclusiones
Para concluir, los avances en mate=
ria de
inteligencia artificial han demostrado su capa=
cidad para afrontar problemas y retos de la sociedad
actual, brindando soluciones efectivas en diversos temas como el acoso y el ciberacoso. =
De
acuerdo a estudios previos, son escasas las investigaciones que utilizan
fuentes de datos provenientes de materiales audiovisuales; es así que en este trabajo se resaltó el uso de datos obtenidos
a través de transcripciones de videos
relacionados al acoso y ciberacoso. El guion generado en esta
investigación pretende ser un apoyo para la creación de futur=
os
guiones para combatir el acoso y el ciberacoso.
De este modo, se utilizó el módulo=
de generación de texto de la compañía OpenAI en la
construcción de un guion de video para combatir el acoso y el
ciberacoso, utilizando datos de videos relacionados a estas temática=
s. Además, el uso de la herramie=
nta YAKE simplificó los procesos
más tradicionales de minería de textos, facilitando la
extracción de palabras claves. No obstante, fue necesaria la
intervención de expertos en el tema de acoso y ciberacoso para comprobar
tanto las palabras claves como el guion generado, y evitar inconsistencias semánticas
en el guion generado.
Como trabajos futuros, se pretende
extraer más información de videos relacionados con el acoso y
ciberacoso, generar más cuerpos semánticos y construir un cor=
pus
que pueda utilizarse en distintos modelos de aprendizaje automático y aprendizaje profundo. Y, finalmente,
que dichos modelos
puedan ser implementados en centros educativos para brindar apoyo
a los profesionales, do=
nde la
presencia de acoso y ciberacoso es mayor
Reconocimientos =
h1>
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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