SPAM DETECTION FRAMEWORK IN SOCIAL NETWORK USING SUPERVISED MACHINE LEARNING PIPELINE TECHNIQUE
Keywords:
Social Media, Spam Messages, Fake Accounts, Spam Detection Mechanism, Supervised Machine Learning Algorithm, Training Models, sentiment classification, featured base- opinion miningAbstract
The Social media is the collective process which can be accomplished in the online significance channel. It is
indeed an enthusiastic approach towards community based contribution, announcement, content contribution, and group
effort. Illustration of community medium is Face book, Twitter, Google+. In social networks, users were often scraped by the
inappropriate or unwanted messages, which are popularly known as spam messages. The spam messages are sent by the
person whom we call as spammer. In fact, the spammer is may be considered to a person or an organization. A single
spammer may create dozens of thousands of fake monetary accounts in order to scale and ensue to reach the utmost number
of the constituent. So the spammers redirect the number of users and the details of the users are encroached by spammers.
This paper discusses about the spam detection mechanism. This mechanism made an attempt to recognize such kind of spam
posts, from a unfamiliar community bookmarking site. The second task is about assisting the users during redistribution a
new post, signifying them with the suitable tags that should go together along with their post. The related non spam data used
for the first task is also used for training models for the second task. Correspondingly extra spam messages are detected and
isolated from the valid communication in that way counters the intrusion in the social network data communication.
