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Efficient Clustering of Short Messages into General Domains
| Citations: | 4 - 1 self |
BibTeX
@MISC{Tsur_efficientclustering,
author = {Oren Tsur and Adi Littman},
title = {Efficient Clustering of Short Messages into General Domains},
year = {}
}
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Abstract
The ever increasing activity in social networks is mainly man-ifested by a growing stream of status updating or microblog-ging. The massive stream of updates emphasizes the need for accurate and efficient clustering of short messages on a large scale. Applying traditional clustering techniques is both inac-curate and inefficient due to sparseness. This paper presents an accurate and efficient algorithm for clustering Twitter tweets. We break the clustering task into two distinctive tasks/stages: (1) batch clustering of user annotated data, and (2) online clustering of a stream of tweets. In the first stage we rely on the habit of ‘tagging’, common in social media streams (e.g. hashtags), thus the algorithm can bootstrap on the tags for clustering of a large pool of hashtagged tweets. The stable clusters achieved in the first stage lend themselves for online clustering of a stream of (mostly) tagless messages. We evaluate our results against gold-standard classification and validate the results by employing multiple clustering eval-uation measures (information theoretic, paired, F and greedy). We compare our algorithm to a number of other clustering algorithms and various types of feature sets. Results show that the algorithm presented is both accurate and efficient and can be easily used for large scale clustering of sparse mes-sages as the heavy lifting is achieved on a sublinear number of documents.
Keyphrases
efficient clustering short message general domain first stage online clustering gold-standard classification social network massive stream applying traditional clustering technique tagless message information theoretic batch clustering feature set status updating efficient algorithm eval-uation measure various type large scale clustering large pool stable cluster sparse mes-sages hashtagged tweet heavy lifting large scale sublinear number distinctive task stage clustering task twitter tweet social medium stream
