Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection

Then, I address the identified research problems Hate speech on social media has severe negative in hate speech detection, by investigating the impacts, not only on its victims (Sticca et al., impact of bias in NLP models on hate speech 2013) but also on the moderators of social detection models from three perspectives: 1) the media platforms (Roberts, 2019). This is why explainability perspective ( 4), where I address the it is crucial to develop tools for automated hate first research problem and investigate the impact speech detection. These tools should provide of bias in NLP models on their performance of a safer environment for individuals, especially hate speech detection and whether the bias in for members of marginalized groups, to express NLP models explains their performance on hate themselves online. However, recent research shows speech detection; 2) the offensive stereotyping that current hate speech detection models falsely bias perspective ( 5), where I address the second flag content written by members of marginalized research problem and investigate the impact of communities, as hateful (Sap et al., 2019; Dixon imbalanced representations and co-occurrences of et al., 2018; Mchangama et al., 2021). Similarly, hateful content with marginalized identity groups recent research indicates that there are social biases on the bias of NLP models; and 3) the fairness in natural language processing (NLP) models (Garg perspective ( 6), where I address the third research et al., 2018; Nangia et al., 2020; Kurita et al., 2019; problem and investigate the impact of bias in Ousidhoum et al., 2021; Nozza et al., 2021, 2022). NLP models on the fairness of the task of hate Yet, the impact of these biases on the task of speech detection. For each research problem, I hate speech detection has been understudied. In summarize the work done to highlight its main my thesis, I identify and study three research findings, contributions, and limitations. Thereafter, problems: 1) the impact of bias in NLP models on I discuss the general takeaways from my thesis and the performance and explainability of hate speech how it can benefit the NLP community at large ( 7).