A MULTI-PLATFORM APPROACH USING HYBRID DEEP LEARNING MODELS FOR AUTOMATIC DETECTION OF HATE SPEECH ON SOCIAL MEDIA

Abstract

Hate speech on online social networks is a general problem across social media platforms that has the potential of causing physical harm to the society. The growing number of hateful comments on the Internet and the rate at which tweets and posts are published per second on social media make it a challenging task to manually identify and remove the hateful commentsfrom such posts. Although numerous publications have proposed machine learning approaches to detect hate speech and other antisocial online behaviours without concentrating on blocking the hate speech from being published on social media. Similarly, prior publications on deep learning and multi-platform approaches did not work on the topic of detecting hate speech in Englishlanguage comments on Twitter and Facebook. This paper proposed a deep learning approach based on a hybrid of convolutional neural network (CNN) and long short-term memory (LSTM) with pre-trained GloVe words embedding to automatically detect and block hate speech on multiple social media platforms including Twitter and Facebook. Thus, datasets were collected from Twitter and Facebook which were annotated as hateful and non-hateful. A set of features were extracted from the datasets based on word embedding mechanism, and the word embeddings were fed into our deep learning framework. The experiment was carried out as a three independent tasks approach. The results show that our hybrid CNN-LSTM approach in Task 1 achieved an f1-score of 0.91, Task 2 obtained an f1-score of 0.92, and Task 3 achieved an f1-score of 0.87. Thus, there is outstanding performance in classifying text as Hate speech or non-hate speech in all the considered metrics. Based on the findings, we conclude that hatespeech can be detected and blocked on social media before it can reach the public.