Preventing Cyberbullying on Social Networks with Spanish Parental Control NLP System

Abstract

The boom in social networks and digital communi-cation has given place to innovative forms of social interaction. However, it has also made possible new forms of harassment of others anonymously and without repercussions. Such is the case of cyberbullying, an increasingly common problem, especially among young people. Its effects on individuals can be devastating, ranging from anxiety and depression to social isolation and low self-esteem. Furthermore, there is a wide variety of applications called parental control, which allow parents to show, the pages the child or adolescent has accessed, know how often the child or adolescent accesses them, and control the time spent on social networks or other entertainment platforms. Therefore, the present research aimed to analyze, design, and implement an intelligent application based on data mining algorithms and the Latent Semantic Analysis (LSA) method for the presumed detection of cyberbullying in social networks in adolescents. The methodological process of the study was carried out following the fundamentals of applied research with a qualitative-quantitative descriptive, and cross-sectional approach. As a result, a multi-platform application was obtained that alerts about suspected bullying to parents or guardians. For the validation of the application, the technique of expert judgment was applied. Also, the process of obtaining negative and positive text similarity was performed based on cosine similarity. In the analysis of Twitter accounts, values of 46% with negative texts and 6.71% with positive texts are obtained, which allows inferring that this is a presumed case of cyberbullying in this account.