Modelos de aprendizaje supervisado como apoyo a la toma de decisiones en las organizaciones basados en datos de redes sociales: Una revisión sistemática de la literatura.

Las redes sociales se han convertido en la herramienta de comunicación e interacción más utilizada entre las personas y se han diversificado para cumplir funciones importantes dentro de la organización. En consecuencia, las redes sociales se han vuelto una fuente inmensa de datos que son procesados a través de modelos de aprendizaje supervisado para producir información que sea competente para la toma de decisiones como la predicción de campañas electorales, la predicción de consumo de un producto y/o servicio, la reputación de una empresa entre otros. De manera que el presente estudio tiene como objetivo identificar los modelos de aprendizaje supervisado como apoyo a la toma de decisiones en las organizaciones basados en datos de redes sociales. Para la identificación de modelos de aprendizaje supervisado se realizó una revisión sistemática de la literatura(RSL) en bases de datos reconocidas y revistas indexadas. De un total de 1614 artículos se identificaron 32 artículos que hacen referencia a 6 modelos de aprendizaje supervisado y las funciones que cumplen como apoyo a la toma de decisiones en una organización. Se puede concluir que existen diversos modelos de aprendizaje supervisado siendo el de Support Vector Machine de mayor grado de precisión. También se han encontrado en las investigaciones modelos de: Naive Bayes, Decision Tree, Regression: Logistic y lineal, k-Nearest Neighbors, y finalmente Neural Network.Trabajo de investigaciónLIMAEscuela Profesional de Ingeniería de SistemasTecnología de información e innovación tecnológic

A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content.  Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of  cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying  along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed