Machine Learning Techniques for Twitter Spam Detection: Comparative Insights and Real-Time Application

In today's world of digital communication, platforms like Twitter are essential for connecting people globally. But there's a problem – spam on Twitter can be a big issue for users. Our main goal is to make a system that can spot and stop spam using fancy computer techniques. We looked at many tweets on Twitter, trying to understand how spam and regular tweets are spread out. We found a problem with our data – there were way more regular tweets than spam ones. To fix it, we used something called Synthetic Minority Over-sampling Technique (SMOTE) to make the number of tweets more equal. After that, we made and tested 13 computer models, looking at how good they were using important measures like accuracy and recall. The result is a strong system that can tell the difference between spam and real tweets on Twitter. This helps make online talks better, keeps users safe, and makes sure Twitter stays a good place to be. Since spam tactics keep changing, our work is an important step in making social media safer. This means everyone can enjoy a safer and better time online.