Spammer detection is designed to identify the fake user responsible for spreading unwanted and irrelevant data. Dissemination of a huge amount of deleterious and fake data by spammers is increasing due to social networking. Spammer detection in social media is based on analyzing the fake content, sources, and information shared by the users. Traditional learning models are insufficient to detect accurate spammers through the information from the social network.
Behavior modeling is the additional framework to the learning model that helps in analyzing the behavioral changes of the users to identify deceitful users. Advertiser behavior modeling uses identities of fake users and their social media shared information to investigate their malicious behavioral changes. Spammer detection in a social network is effectively performed while incorporating advertiser behavior modeling.