TweetMotif: Exploratory Search and Topic Summarization for Twitter

We present TweetMotif, an exploratory search application for Twitter. Unlike traditional approaches to information retrieval, which present a simple list of messages, TweetMotif groups messages by frequent significant terms — a result set’s subtopics — which facilitate navigation and drilldown through a faceted search interface. The topic extraction system is based on syntactic filtering, language modeling, near-duplicate detection, and set cover heuristics. We have used TweetMotif 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. Introduction and Description On the microblogging service Twitter, users post millions of very short messages every day. Organizing and searching through this large corpus is an exciting research problem. Since messages are so small, we believe microblog search requires summarization across many messages at once. Our system, TweetMotif, responds to user queries, first retrieving several hundred recent matching messages from a simple index; we use the Twitter Search API. Instead of simply showing this result set as a list, TweetMotif extracts a set of themes (topics) to group and summarize these messages. A topic is simultaneously characterized by (1) a 1to 3-word textual label, and (2) a set of messages, whose texts must all contain the label. TweetMotif’s user interface is inspired by faceted search, which has been shown to aid Web search tasks (Hearst et al. 2002). The main screen is a two-column layout. The left column is a list of themes that are related to the current search term, while the right column presents actual tweets, grouped by theme. As themes are selected on the left column, a sample of tweets for that theme appears at the top of the right column, pushing down (but not removing) tweet results for any previously selected related themes. This allows users to explore and compare multiple related themes at once. The set of topics is chosen to try to satisfy several criteria, which often conflict: Copyright c © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Figure 1: Screenshot of TweetMotif. 1. Frequency contrast: Topic label phrases should be frequent in the query subcorpus, but infrequent among general Twitter messages. This ensures relevance to the query while eliminating overly generic terms. 2. Topic diversity: Topics should be chosen such that their messages and label phrases minimally overlap. Overlapping topics repetitively fill the same information niche; only one should be used. 3. Topic size: A topic that includes too few messages is bad; it is overly specific. 4. Small number of topics: Screen real-estate and concomitant user cognitive load are limited resources. The goal is to provide the user a concise summary of themes and variation in the query subcorpus, then allow the user to navigate to individual topics to see their associated messages, and allow recursive drilldown. The approach is related to document clustering (though a message can belong to multiple topics) and text summarization (topic labels are a high-relevance subset of text across messages). We heuristically proceed through several stages of analysis.