Recapitulization of tweets using graph-based clustering

Abstract

Twitter, a well-liked online social networking site, facilitates millions of users on a daily basis to dispatch and orate quick 140-character notes named tweets. Nowadays, Twitter is cogitated as the fastest and popular intermediate of communication and is used to follow latest events. Tweets pertaining to a specific event can be effortlessly found using keyword matching, but there are numerous tweets that are likely to contain information that is semantically identical. Moreover, there exist many systems for recapitulating tweets related to a particular event, but they have numerous limitations and are unable to provide accurate results. This work aims to overcome the limitations of existing systems by developing a system for recapitulating tweets using graph-based clustering.