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.
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.
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.