Detecting Hate Speech in Roman Urdu Using Convolutional BiLSTM Based Deep Hybrid Neural Network
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Date
2023
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UMT, Lahore
Abstract
The way we interact and engage with culture has been completely transformed by the Internet. It has altered how we obtain news, communicate with friends, and go about our daily lives. Because the Internet is decentralised, anybody may generate and exchange anything, including ideas, information, photos, movies, music, and more. Internet sites that incite hate towards certain racial, religious, racial, or sexuality inclined minorities, such as women, Jews, African-Americans, Muslims, and the Transgender community, are also present despite the fact that it is a democratic medium.. In recent years, political discourse has seen an increase in hateful and discriminatory messages. This thesis focuses on the current issue of hate speech in the specific context of Roman Urdu. Given the growing importance of online communication and the role of social media, this research aims to analyze tweets posted by users to detect hate speech. A linguistic-based approach has been adopted, without considering any legal or academic definition of hate speech. Critical discourse analysis and the definition of soft hate speech have been used to identify implicit forms of hate speech through linguistic tools. We propose a convolutional BiLSTM-based deep hybrid neural network for detecting hate speech in Roman Urdu.