Abstract:
Since more than 300 million monthly users send 500 million tweets daily,
Twitter is a popular social networking platform. This is the main reason why
spammers use Twitter to spread malicious software that steals user personal
information, tweets with faulty or fake URLs, assertively following or un-following
users, trending fake tweets to attract users' attention, and spreading pornographic
advertisements and among other reprehensible activities. The research clearly
demonstrates that over 32 million people have engaged with the server for casual
information on a daily basis. Twitter is said to have collected data on active users in
previous years and studied their actions. Therefore, today's social media landscape, it
is crucial to recognize and filter out the damaging or unwanted trends or malicious
tweets. This technique suggests analyzing tweets and categorizing them as spam or
ham based on the words they include. While there are several machine learning and
deep learning techniques for categorizing and detecting spam tweets, this system will
employ the clustering and binary detection model from KNN. This system is
implemented using ASP. Net programming language with Microsoft SQL Server
Database Engine.
