• shruthishree S.H
• P Sravani
• Akashdeep
• Satyaphani pavan
• Yadhunanda kishore

Abstract: Due to increased popularity in online social networks ,spammers used to find these platforms easily accessible to find users in malicious activities by posting spam messages. for this we have taken twitter platform and performed spam tweets detection. To stop spam comments, twitter bot maker and google safe browsing will detect and will block spam tweets. these tools will block spam links as well ,still they cant protect the user in real time.so that researchers applied many approaches to give spam free social platform. In that some are based on user based features and others are based on tweet based features. we can solve this issue by proposing a framework which will take user and tweets features along with tweet text to make tweets classified. the advantage of using tweet text is we can easily find the spam tweets even if spammer can create new account which cannot be possible only with user and tweet based feature. we find the solution with four machine learning algorithms i.e,, Random forest ,gradient boosting and support vector machine ( SVM ) , & neural network. In the recent news Elon musk has virtually put on hold of $44 billion twitter deal as he seeks clarity on spam bots. Musk asked twitter INC to show proof that spam bots account for less than 5%of its total users. This deal cannot move forward until twitter CEO shows proof.

KEYWORDS: Random forest, gradient boosting, support vector machine, spam bots, hacking, daniel of services, stop-word, spam drift, real time spam detection, porter stemming.

"REVIEW PAPER ON REAL TIME SPAM DETECTION IN TWITTER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.c732-c735, July-2022, Available :http://www.jetir.org/papers/JETIR2207292.pdf

"REVIEW PAPER ON REAL TIME SPAM DETECTION IN TWITTER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppc732-c735, July-2022, Available at : http://www.jetir.org/papers/JETIR2207292.pdf