An automatic topic ranking approach for event detection on microblogging messages

Microblog is a social network service which is able to aggregate messages to explore new knowledge. Nowadays, more and more users contribute what they found and what they thought by posting short messages. This phenomenon makes people spend lots of time monitoring text streams of microblogging messages, in order for acquisition of information regarding real-time events and news. In this work, we adopted several algorithms for mining topics on microblogs, and developed a novel algorithm for ranking topics and messages on microblogs. The developed ranking solution is expected to assist people to quickly identify emerging topics and events which might yield the most recently trending topics, with the hope to effectively resolve the information overload.