2012/05/30 by Jimmy Lin, Lin, Jimmy, Gilad Mishne +1 · 1 citation
Computer Science · Physics and Astronomy · #Advanced Text Analysis Techniques #Artificial intelligence #Complex Network Analysis Techniques #Computer science #Context (archaeology) #Dynamics (music) #FOS: Computer and information sciences #Geography #Information Retrieval (cs.IR) #Information retrieval #Perspective (graphical) #Phenomenon #Ranking (information retrieval) #Social and Information Networks (cs.SI) #Term (time) #Web Data Mining and Analysis #cs.IR #cs.SI
paper · pdf · doi:10.48550/arxiv.1205.6855
published in arXiv (Cornell University) (Cornell University) · This is an extended version of a similarly-titled paper at the 6th International AAAI Conference on Weblogs and Social Media (ICWSM 2012)
arxiv created 2012/05/30 · openalex publication_date 2012/05/30 · arxiv updated 2012/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.