Analysis of Privacy Leak on Twitter

Abstract

© 2017 IEEE. Micro-blogging services like Twitter which allow users to post messages and follow activities are gaining in popularity. The content of the posted tweets is wide ranging, and sometimes includes private information like email addresses, physical addresses, birthdays and medical history. Such private data, if leaked through public posts, could be used by stalkers, foes, or unintended parties. Detecting the presence of private data in tweets is a first step towards analyzing the privacy risk associated with it. In this context, the purpose of our work is twofold. First, we categorize the tweets into private and non-private, based on whether they reveal any private information or not. Second, we try to gain more insights into categorized private tweets by identifying the type of private data being revealed. We train the model on novel features extracted from the labeled tweets and perform supervised classification. Our results show that detection of leak of private data can be achieved with an accuracy of about 80% and false positive rate of 18%. Furthermore, we are able to differentiate between private tweets by classifying them into different categories with high accuracy.