Darkness cannot drive out darkness: investigating the impact of bias in NLP models on hate speech detection models
Creators: name: Elsafoury, Fatma Amin
Publisher: name: Zenodo
Year: 2025

description: Hate speech on social media could have severe negative effects. This is why it is crucialto develop tools for automated hate speech detection. These tools should provide a saferenvironment for individuals, especially from marginalised groups, to express themselvesonline. However, recent research shows that current hate speech detection models falselyflag content written by members of marginalised communities, as hateful. Similarly, recentresearch indicates that there are biases in natural language processing (NLP) models. Yet,the impact of these biases on the task of hate speech detection has been understudied.I identify three research problems: 1) the lack of studying the impact of bias in NLPmodels on the performance and explainability of hate speech detection models; 2) the lackof studying the impact of the imbalanced representation of hateful content on the bias inNLP models; and 3) the lack of studying the impact of bias in NLP models on the fairness ofhate speech detection models. Investigating and understanding the impact of bias in NLP onhate speech detection models will help the NLP community develop more reliable, effective,unbiased, and fair hate speech detection models.In this thesis, I first critically review the literature on hate speech and bias in NLP models.Then, I address my research problems by investigating the intersection of bias in NLP andhate speech detection models from three perspectives: 1) The explainability perspective,where I address the first research problem and investigate the impact of bias in NLP modelson their performance of hate speech detection and whether the bias in NLP models explainstheir performance on hate speech detection. I run a series of experiments to investigatepre-training bias in 3 contextual word embeddings and 5 static word embeddings and testthat impact of these models on five hate speech related datasets; 2) the offensive stereotypingbias perspective, where I address the second research problem and investigate the impactof imbalanced representations and co-occurrences of hateful content with marginalisedidentity groups on the bias of NLP models. I prospose two metrics to measure the offensivestereotyping bias in static and cotnextual word embeddings. I used 15 static word embeddingsand 3 contextual word embeddings. Then I invetigate the impact of the measured bias onthe downstream task of hate speech detection on 6 hate speech related datasets; and 3) thefairness perspective where I address the third research problem and investigate the impact of 3 sources of bias in NLP models on the fairness of the task of hate speech detection. I alsoinvestigate the impact of removing the different sources of bias on the fairness of hate speechdetection.The findings of this thesis provide evidence that the bias in NLP models has an impact onhate speech detection models from all three perspectives. This means that we need to mitigatethe bias in NLP models so that we can ensure the reliability of hate speech detection models.On the other hand, I argue that the limitations and criticisms of the currently used methods tomeasure and mitigate bias in NLP models are direct results of failing to incorporate relevantliterature from the social sciences.