Analyzing the Effectiveness of Reducing Hate Speech in Bengali Language Using BERT Model

In this article, we explore the problem of hate speech on social media platforms and propose a method for reducing its intensity. The surge in social media usage has led to an increase in hateful content, which can have severe consequences such as social violence, riots, and other forms of retaliation. To address this issue, governments around the world are introducing laws against hate speech, and digital media platforms are becoming more concerned about filtering out hate, sexual abuse, and harmful content.
Our proposed method involves asking three annotators to rate a set of sentences on a 1-5 Likert scale to measure their level of hate intensity. We obtain average scores of 4.1, 4.2, and 3.8 corresponding to the three annotators, indicating that our model performs well in reducing hate speech. The high levels of agreement among the annotators and the high average scores per annotator suggest that our proposed method is effective in removing hate content from social media platforms.
To understand how our method works, imagine a kitchen where a chef prepares dishes for guests. In this metaphor, the sentences containing hate speech are like spoiled ingredients that need to be removed from the dish to make it palatable and enjoyable for everyone. Our proposed method is like a filter that separates the spoiled ingredients from the rest of the dish, ensuring that only clean and hateful-free content remains.
In conclusion, our article presents a practical solution to address the growing problem of hate speech on social media platforms. By using annotators to rate sentences on a 1-5 Likert scale, we can identify and remove hate content from social media platforms, creating a safer and more enjoyable online environment for everyone.