Time Based Feedback and Query Expansion for Twitter Search

Abstract

Twitter is an accepted platform among users for expressing views in a short text called a “Tweet” Application of search models to platforms like Twitter is still an open-ended question, though the creation of the TREC Microblog track in 2011 aims to help resolve it. In this paper, we propose a modified language search model by extending a traditional query-likelihood language model with time based feedback and query expansion. The proposed method makes use of two types of feedback, time feedback by evaluating the time distribution of top retrieved tweets, and query expansion by using highly frequent terms in top tweets as expanded terms. Our results suggest that using both types of feedback, we get better results than using a standard language model, and the time-based feedback uniformly improves results whether query expansion is used or not.