![]() ![]() Furthermore, because of lower literacy levels and a lack of cyber awareness, these people are readily motivated to spread toxic content such as Hate speech, fake news, and so on through social media platforms. People adopting their regional language or blending it with English during conversation or information exchange has become a typical phenomenon as the number of Internet users from rural India has grown. However, with the government’s push for digital India, rural India has also begun to embrace the digital economy. Urban consumers mostly drove this rapid expansion of the digital economy. India is one of the largest and fastest-growing marketplace for digital consumers. The experimental results found that our proposed model outperforms existing state-of-art methods for Hate speech identification in Hinglish language with an accuracy of 73%. Also, Transformer-based Interpreter and Feature extraction model on Deep Neural Network (TIF-DNN), is proposed in this work. ![]() ![]() In this study, we investigated the performance of transformer models like IndicBERT and multilingual Bidirectional Encoder Representation(mBERT), as well as transfer learning from pre-trained language models like ULMFiT and Bidirectional encoder Representation(BERT), to find hateful content in Hinglish. The majority of previous work focuses on high-resource language such as English, but very few researchers have concentrated on the mixed bilingual data like Hinglish. People in multilingual societies, such as India, frequently mix their native language with English while speaking, so detecting hate content in such bilingual code-mixed data has drawn the larger interest of the research community. It has a lot of benefits, but it also comes with a lot of risks and drawbacks, such as Hate speech. Many people have begun to use social media platforms due to the increased use of the Internet over the previous decade. ![]()
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