Jitter Search

Just In Time TwittER (Jitter) is a News-Based Real-Time Twitter Search Interface that enhances real-time microblog search by monitoring news sources on Twitter. We improve retrieval through time-aware ranking models that use behavioral dynamics of users. Jitter finds additional terms associated with the query terms to find more “interesting” posts using query expansion.

Time-aware

Jitter leverages on the behavioral dynamics of users to estimate the most relevant time periods for a topic. Our hypothesis stems from the fact that when a real-world event occurs it usually has peak times on the Web: a higher volume of tweets, new visits and edits to related Wikipedia articles, and news published about the event. In this paper, we propose a novel time-aware ranking model that leverages on multiple sources of crowd signals.

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Monitoring news

Jitter enhances real-time microblog search by monitoring news sources on Twitter. We improve retrieval through query expansion using pseudo-relevance feedback. However, instead of doing feedback on the original corpus we use a separate Twitter news index. This allows the system to find additional terms associated with the original query to find more “interesting” posts.

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