Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Short text classification in twitter to improve information filtering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Classifying microblogs for disasters
Proceedings of the 18th Australasian Document Computing Symposium
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Many microblog messages remain useful only within a short time, and users often find such a message after its informational value has vanished. Users also sometimes miss old but still useful messages buried among outdated ones. To solve these problems, we develop a method of classifying messages into the following three categories: (1) messages that users should read now because their value will diminish soon, (2) messages that users may read later because their value will not largely change soon, and (3) messages that are not useful anymore because their value has vanished. Our method uses an error correcting output code consisting of binary classifiers each of which determines whether a given message has value at specific time point. Our experiments on Twitter data confirmed that it outperforms naive methods.