• Aarthy.N
• Devi.P.P

Micro-blogging services like Twitter which allow users to post messages and follow activities are gaining in popularity. Part of the appeal is the ability to follow any user with a public profile, enabling them to communicate with each other and view each other’s posts directly. 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 purposed work is categorize the tweets into private and non-private, based on whether they reveal any private information or not. We train the model on novel features extracted from the labeled tweets and perform supervised classification. Our results show that detection of private data classification can be achieved with an accuracy of about 82.5% .Furthermore, we are able to differentiate between private tweets by classifying them into different categories with high accuracy.

Classification, Privacy, Social Networks, Twitter

"ANALYSIS OF PRIVACY DATA DETECTION AND CLASSIFICATION SYSTEM USING MULTI CLASSIFIERS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.a42-a46, December-2021, Available :http://www.jetir.org/papers/JETIR2112008.pdf

"ANALYSIS OF PRIVACY DATA DETECTION AND CLASSIFICATION SYSTEM USING MULTI CLASSIFIERS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppa42-a46, December-2021, Available at : http://www.jetir.org/papers/JETIR2112008.pdf