Hate Speech and Stereotypes with Artificial Neural Networks

Information technology is ubiquitously integrated into all areas of human and social life. It becomes progressively critical to build trust in systems while exposing their limitations along with utility and values. The harmonic integration of applications into society will promote the ability of the individuals to positively adapt to change (resilience and response) only if, instead of imposing an AI-centric world on humans, the central goal is to rearrange AI to environments around all aspects of human life. Current AI architectures and applications are increasingly designed taking into account ethical issues, to support the educative role of advanced research tools in improving the interaction between individuals and ultimately in the betterment of society. This work aims at detecting hate speech and stereotypes in textual communication using Artificial Neural Networks and Natural Language Processing. Starting from data from the Evalita 2020 competition, we analyse 6851 tweets which include stereotypes and hate speech, including text and emoticons. Training our neural network with the Adam optimizer, we obtain very promising results, of accuracy for the hate speech task and the stereotype prediction task.