With Convolutional Neural Networks
Elisa Di Nuovo and Marco Siino will present an interesting approach used to detect hate speech spreaders in the context of the shared task Profiling Hate Speech Spreaders (HSSs) proposed at PAN 2021.
Title: Detection of hate speech spreaders using convolutional neural networks
The speakers will describe a deep learning model based on a Convolutional Neural Network (CNN) to profile hate speech spreaders online. The model was developed for the Profiling Hate Speech Spreaders (HSSs) task proposed by PAN 2021 organizers and hosted at the 2021 CLEF Conference. The approach, used to classify an author as HSS or not (nHSS), takes advantage of a CNN based on a single convolutional layer. In this binary classification task, on the tests performed using a 5-fold cross validation, the proposed model reaches a maximum accuracy of 0.80 on the multilingual (i.e., English and Spanish) training set, and a minimum loss value of 0.51 on the same set. As announced by the task organizers, the model won the 2021 PAN competition on profiling HSSs, reaching an overall accuracy of 0.79 on the full test set. This overall accuracy is obtained averaging the accuracy achieved by the model on both languages. In particular, with regard to the Spanish test set the model achieves an accuracy of 0.85, while on the English test set the same model achieved an accuracy of 0.73.