Machine Learning Based Twitter Spam Account Detection: A Review

Online social networks (OSNs) are emerging communication medium for people to establish and manage social relationships. In OSNs, regularly billions of users are involved in social interaction, content and opinion dissemination, networking, recommendations, scouting, alerting, and social campaigns. The popularization of OSNs open up a new perspectives and challenges to the study of social networks, being of interest to many fields. Social network is a place where social activities, business oriented activities, entertainment, and information are exchanged. It establish a worldwide connectivity environment where communities of people share their interests and activities, or who are interested in interests and activities of others Although social network has given immense benefits to people at the same time harming people with various mischievous activities that take place on social platforms. This causes significant economic loss to our society and even threaten the national security. All the social networks Facebook, Twitter, LinkedIn, etc. are highly susceptible to malware activities. Twitter is one of the biggest microblogging networking platform, it has more than half a billion tweets are posted every day in average by millions of users on Twitter. Such a versatility and wide spread of use, Twitter easily get intruded with malicious activities. Malicious activities includes malware intrusion, spam distribution, social attacks, etc. Spammers use social engineering attack strategy to send spam tweets, spam URLs, etc. This made twitter an ideal arena for proliferation of anomalous spam accounts. The impact stimulates researchers to develop a model that analyze, detects and recovers from defamatory actions in twitter. Twitter network is inundated with tens of millions of fake spam profiles which may jeopardize the normal user’s security and privacy. To improve real users safety and identification of spam profiles become key parts of the research.