Abstract:

Many people around the globe utilise online social networks. The rare unforeseen consequences that come from user interactions in our daily lives have a big impact on social media sites like Twitter and Facebook. Social networking platforms are used as a target by spammers to spread a lot of unreliable and perilous material. Twitter is an excellent example of how it has evolved into one of the most important places for excessive amounts of spam at all times for fake individuals to tweet and advertise businesses or services that have a substantial influence on real users while also disrupting resource utilisation. This system provides instructions on how to spot spam tweets and phoney user accounts on the social media platform like Twitter. In order to identify bogus content, this system employs the Twitter dataset and four separate algorithms: Fake Content, Spam URL Detection, Spam Trending Topic, and Fake User Identification. Utilizing the four stated earlier methods, this system can assess if a tweet is legitimate or spam. After that the system train the Random Forest data mining algorithm on the dataset to identify the proportion of legitimate and fraudulent accounts as well as spam and non-spam tweets. Several data mining techniques are used by the creators of each methodology to classify tweets as spam or not, however in this case, this system employ the Random Forest classifier.

Description:

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