Ml-based Offensive Tweet Accuracy Detector on Social Media

Abstract

Global access to the internet has surprisingly changed how we see the arena. SM is one of the children of the internet, which can be found in many bureaucracies: online gaming platforms, dating apps, boards, online information services, and social networks with extraordinary functions. On Twitter and Facebook, you can share opinions make business contacts on LinkedIn, share photos on Instagram, send videos on YouTube, and court someone on Meetic. However, they all have one thing in common: they aim to attach people. Social networks have such great potential that by the year 2021, it is projected that there will be 3.02 billion active social media users globally. The rapid rise of social networks and microblogging sites has caused direct communication between people of diverse cultures and mentalities, leading to an increasing number of "cyber" conflicts. As a consequence, hate speech is used more and more, to the point where it has become a serious problem invading these public spaces. Hate speech is defined as the use of competitive, violent, or abusive words directed towards a specific group of people who share a common set of assets, whether that set of assets includes their gender, ethnic group, race, or beliefs and faith. While most internet social networks and microblogging services prohibit hate speech, the sheer size of these networks and websites makes controlling all of their content extremely difficult. As a result, there is a need to detect such speech automatically and remove any content that contains hateful or inciting language.