Natural Language Processing for Equality, Diversity and Inclusion

Abstract

Social media has experienced significant growth in the past decade and has enabled people to connect with people all over the world, have increased access to information and have an opportunity to express themselves and join like-minded communities. However, hate speech and online harassment are significant problems, with around two-thirds of adults under 30 having experienced some form of online harassment. Therefore, it becomes essential to regulate content on social media and it needs to be done automatically due to the large volume of daily content. In this thesis, we attempt to solve the above-mentioned problems by building best-in-class classifiers for novel datasets. One way to tackle harmful content online is to have a positive reinforcement approach and encourage positive and supportive messages. We propose a Hope Speech Detection model trained on a first-of-a-kind hope speech dataset. In the first approach, we used contextual embeddings to train classifiers using logistic regression, random forest, SVM, and LSTM based models. The second approach used a majority voting ensemble of 11 models obtained by fine-tuning pre-trained transformer models. Our model ranks first in terms of F1 score in the English language. While supporting and boosting positive content online is helpful, there should also be a distinction made between content that is positive and content that seems positive but encourages emotion suppression. Over the past few years, there has been a growing concern around toxic positivity on social media, a phenomenon where positivity is used to minimize one’s emotional experience. In this thesis, we create a dataset for toxic positivity classification from Twitter and an inspirational quote website. We then perform benchmarking experiments using various text classification models and show the suitability of these models for the task. While there are many hate speech classifiers trained on a generic hate speech definition, there is a lack of datasets that focus on homophobia and transphobia. In this thesis, we describe our approach to classify homophobia and transphobia in social media comments. We used an ensemble of transformer based models to build our classifier. Our classifier ranks 1st in terms of F1 score