SPAMMER AND FAKE ACCOUNT DETECTION USING DIFFERENT MACHINE LEARNING TECHNIQUES (International Journal of Food and Nutritional Sciences, 2023)
Abstract_Social networking platforms are used by a large number of people all around the world. Social media platforms such as Twitter and Facebook have a significant impact on the uncommon unintended effects that occur in our daily lives as a result of user interactions. Spammers use social networking sites as a target stage to disseminate a significant amount of improper and dangerous information. Twitter is a prime example of how it has evolved into one of the most important platforms for an excessive amount of spam in all tomes for phoney persons to tweet and promote businesses or services that have a big impact on legitimate users while also disrupting resource utilisation. The author of this paper describes a technique for detecting spam tweets and false user accounts on the online social network Twitter. Author uses Twitter dataset and four different algorithms to detect fake content: Fake Content, Spam URL Detection, Spam Trending Topic, and Fake User Identification. Using the aforementioned four strategies, we can determine whether a tweet is normal or spam, and then train the dataset using the Random Forest data mining algorithm to classify the amount of spam and non-spam tweets, as well as false and non-fake accounts. To categorise tweets as spam or non-spam, the authors of each technique use different data mining techniques, however here we use the Random Forest classifie