Cybercrime Analytics on Social Media

Online Social Networking (OSN) sites, such as Facebook and Twitter, process large amount of data on regular basis. By carefully studying the underlying patterns of these data sets, we can find out the disturbances that are relevant for cyber-criminal investigations. By using the Facebook API and Twitter API, extraction of data is performed based on the word related to the disturbance. The raw data collection set will be stored in huge volume, containing many from Facebook, like posts and information such as counts of like, shares and comments, etc. , and from twitter, like tweets/status, retweets count, follower count, friends count, etc. When the user enters a search text, the relevant post will be fetched by the machine learning based algorithms. Main goal of the project is to analyse such information which may be useful to support cybercrime investigations.