Automatic Identification of Fake Profiles in Online Social Networks

In the modern era, the social life of individuals has shifted towards becoming connected with online informal communities. These communities have revolutionized the way we pursue our social life, making it easier to connect with them and keep up with their updates. However, with the rapid growth of these communities, there has been a rise in issues such as false profiles and online bullying. There is currently no practical solution to address these issues. To address this issue, we have developed a system with which the programmed identification of false profiles is feasible and efficient. This system uses order methods such as support vector machine, nave bayes and decision trees to classify the profiles into false or true classes. As this is a software-based recognition method, it can be easily connected to online informal organizations that have a large number of profiles whose profiles cannot be physically inspected. In today’s world, users are bombarded with unnecessary information during surfing, which are posted by false users. Research has shown that 20% to 40% of profiles in social networks like Facebook are fake profiles. This detection of fake profiles leads to solution using frameworks.