Measuring Online Hate on 4chan using Pre-trained Deep Learning Models
This work addresses the issue of online hate on non-moderated platforms like 4chan, which can harm individuals and groups, but it is incremental as it applies existing methods to new data.
This study tackled the problem of measuring online hate speech on 4chan's /pol/ board by using pre-trained NLP models like RoBERTa and Detoxify for multi-class classification and topic modeling, finding that 11.20% of the dataset contained hate speech in categories such as racism and sexism.
Online hate speech can harmfully impact individuals and groups, specifically on non-moderated platforms such as 4chan where users can post anonymous content. This work focuses on analysing and measuring the prevalence of online hate on 4chan's politically incorrect board (/pol/) using state-of-the-art Natural Language Processing (NLP) models, specifically transformer-based models such as RoBERTa and Detoxify. By leveraging these advanced models, we provide an in-depth analysis of hate speech dynamics and quantify the extent of online hate non-moderated platforms. The study advances understanding through multi-class classification of hate speech (racism, sexism, religion, etc.), while also incorporating the classification of toxic content (e.g., identity attacks and threats) and a further topic modelling analysis. The results show that 11.20% of this dataset is identified as containing hate in different categories. These evaluations show that online hate is manifested in various forms, confirming the complicated and volatile nature of detection in the wild.