Burst-aware data fusion for microblog search

We consider the problem of searching posts in microblog environments. We frame this microblog post search problem as a late data fusion problem. Previous work on data fusion has mainly focused on aggregating document lists based on retrieval status values or ranks of documents without fully utilizing temporal features of the set of documents being fused. Additionally, previous work on data fusion has often worked on the assumption that only documents that are highly ranked in many of the lists are likely to be of relevance. We propose BurstFuseX, a fusion model that not only utilizes a microblog post’s ranking information but also exploits its publication time. BurstFuseX builds on an existing fusion method and rewards posts that are published in or near a burst of posts that are highly ranked in many of the lists being aggregated. We experimentally verify the effectiveness of the proposed late data fusion algorithm, and demonstrate that in terms of mean average precision it significantly outperforms the standard, state-of-the-art fusion approaches as well as burst or time-sensitive retrieval methods.