Spam Detection in Social Media Networks: A Data Mining Approach

Abstract

The ubiquitous use of social media has generated unparalleled amounts of social data. Data may be – text, numbers or facts that are computable by a computer. A particular data is absolutely useless until and unless converted into some useful information. It is necessary to analyze this massive amount of data and extracting useful information from it. There are more active internet users on social networks than search engines. Social media networks provide an easily accessible platform for users who wish to share information with others. Information can be spread across social networks quickly and effectively, hence have now become susceptible to different types of undesired and malicious spammer/hacker actions. Therefore, there is a pivotal need for security in social media and industry. In this demo, a scalable and online social media spam detection system for social network security using TFIDF algorithm is proposed. General Terms Porter Stemmer algorithm, TF-IDF algorithm.