Research: A Study of “Churn” in Tweets and Real-Time Search Queries (Extended Version)
Applicability: “A Study of “Churn” in Tweets and Real-Time Search Queries (Extended Version)” offers unique insight into the temporal dynamics of term distribution which may hold implications the design of search systems. As the growing importance of real-time search brings with it several information retrieval challenges; this paper frames one such challenge, that of rapid changes to term distributions, particularly for queries.
Abstract: The real-time nature of Twitter means that term distributions in tweets and in search queries change rapidly: the most frequent terms in one hour may look very different from those in the next. Informally, we call this phenomenon “churn”. Our interest in analyzing churn stems from the perspective of real-time search. Nearly all ranking functions, machine-learned or otherwise, depend on term statistics such as term frequency, document frequency, as well as query frequencies. In the real-time context, how do we compute these statistics, considering that the underlying distributions change rapidly? In this paper, we present an analysis of tweet and query churn on Twitter, as a first step to answering this question. Analyses reveal interesting insights on the temporal dynamics of term distributions on Twitter and hold implications for the design of search systems.
Analysis: Summarized analysis from this paper includes observations on:
Authors: Prepared by Jimmy Lin and Gilad Misne of Twitter, Inc., “A Study of “Churn” in Tweets and Real-Time Search Queries (Extended Version)” is a prepared paper submitted and accepted by the 6th International AAAI Conference on Weblogs and Social Media (ICWSM 2012).
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