Abstract
We present TweetMotif, an exploratory search applica- tion for Twitter. Unlike traditional approaches to in- formation retrieval, which present a simple list of mes- sages, TweetMotif groups messages by frequent signif- icant terms — a result set’s subtopics — which facili- tate navigation and drilldown through a faceted search interface. The topic extraction system is based on syn- tactic filtering, language modeling, near-duplicate de- tection, and set cover heuristics. We have used Tweet- Motif to deflate rumors, uncover scams, summarize sentiment, and track political protests in real-time. A demo of TweetMotif, plus its source code, is available at http://tweetmotif.com.
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