Hate Speech Detection in Marathi Tweets using Stacked Deep Learning models

Digital platforms have revolutionized communication, but they have also led to an increase in hate speech, making it challenging to create welcoming online groups. The purpose of this study is to close this gap by using deep learning models to detect hate speech in Marathi, a regional language of India. The researchers use advanced word embeddings and DL models like Long Short-Term Memory and Bidirectional LSTM to demonstrate the efficacy of these methods. The study aims to build strong hate speech detection systems that can be extended to other low-resource languages, creating safer online spaces. The study emphasizes the importance of contextually and culturally appropriate data in enhancing model accuracy. In order to strengthen their models’ training and validation and guarantee reliable performance in real-world applications, the researchers also intend to integrate real-world social media data. The study closes a significant gap in regional language processing by concentrating on Marathi and opens the door for more inclusive and thorough hate speech detection frameworks across linguistic landscapes.