Summary of Analysis and Detection Of Multilingual Hate Speech Using Transformer Based Deep Learning, by Arijit Das et al.
Analysis and Detection of Multilingual Hate Speech Using Transformer Based Deep Learning
by Arijit Das, Somashree Nandy, Rupam Saha, Srijan Das, Diganta Saha
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel transformer-based model for hate speech detection on social media platforms has been proposed. This approach leverages the power of deep learning to identify harmful content, taking into account actual or perceived aspects of identity such as racism, religion, and sexual orientation. The model’s language independence allows it to be applied across various social media platforms like Twitter, Facebook, WhatsApp, Instagram, and others. Evaluations on datasets from renowned researchers demonstrate improved performance over existing baseline and state-of-the-art models. Specifically, the accuracy rates for hate speech detection are 89% in Bengali, 91% in English, 91% in German, and 77% in Italian. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to identify hateful messages on social media. They created a special computer program that can understand different languages and find mean or hurtful words online. This helps keep people safe from harmful messages and makes the internet a better place. The program was tested on lots of different types of messages, including those in Bengali, English, German, and Italian. It did a really good job, especially when it came to identifying hate speech in some languages. |
Keywords
» Artificial intelligence » Deep learning » Transformer
