On the Challenges of Building Datasets for Hate Speech Detection

Detection of hate speech has been formulated as a standalone application of NLP and different approaches have been adopted for identifying the target groups, obtaining raw data, defining the labeling process, choosing the detection algorithm, and evaluating the performance in the desired setting. However, unlike other downstream tasks, hate speech suffers from the lack of large-sized, carefully curated, generalizable datasets owing to the highly subjective nature of the task. In this paper, we first analyze the issues surrounding hate speech detection through a data-centric lens. We then outline a holistic framework to encapsulate the data creation pipeline across seven broad dimensions by taking the specific example of hate speech towards sexual minorities. We posit that practitioners would benefit from following this framework as a form of best practice when creating hate speech datasets in the future.

bitsa_nlp@LT-EDI-ACL2022: Leveraging Pretrained Language Models for Detecting Homophobia and Transphobia in Social Media Comments

Online social networks are ubiquitous and user-friendly. Nevertheless, it is vital to detect and moderate offensive content to maintain decency and empathy. However, mining social media texts is a complex task since users don't adhere to any fixed patterns. Comments can be written in any combination of languages and many of them may be low-resource. In this paper, we present our system for the LT-EDI shared task on detecting homophobia and transphobia in social media comments. We experiment with a number of monolingual and multilingual transformer based models such as mBERT along with a data augmentation technique for tackling class imbalance. Such pretrained large models have recently shown tremendous success on a variety of benchmark tasks in natural language processing. We observe their performance on a carefully annotated, real life dataset of YouTube comments in English as well as Tamil. Our submission achieved ranks 9, 6 and 3 with a macro-averaged F1-score of 0.42, 0.64 and 0.58 in the English, Tamil and Tamil-English subtasks respectively. The code for the system has been open sourced.