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).
This entry was posted on Tuesday, June 5th, 2012 at 2:39 pm. It is filed under chronology, discover and tagged with research, social media. You can follow any responses to this entry through the RSS 2.0 feed.
Comments are closed.
Since its 2007 introduction, kCura’s Relativity product has become one of the world’s leading attorney review platforms. One of the elements of Relativity’s strong growth and marketplace acceptance has been kCura’s focus on and support of partnerships. Provided as a by-product of review platform research and presented in the form of a simple and sortable table is an aggregation of kCura Premium Hosting Partners and Consulting Partners.
Taken from a combination of public market sizing estimations as shared in leading electronic discovery reports, publications and posts over time, the following eDiscovery Market Size Mashup shares general worldwide market sizing considerations for both the software and service areas of the electronic discovery market for the years between 2013 and 2018.
Provided as a non-comprehensive overview of key and publicly announced eDiscovery related mergers, acquisitions and investments to date in 2014, the following listing highlights key industry activities through the lens of announcement date, acquired company, acquiring or investing company and acquisition amount (if known).
A recent U.S. Department of Justice memorandum questioned the effectiveness of using technology-assisted review with non-English documents. The fact is that, done properly, such reviews can be just as effective for non-English as it is for English documents. This is true even for the so-called “CJK languages” — Asian languages including Chinese, Japanese and Korean.
Grossman and Cormack concluded that CAL demonstrated superior performance over SPL and SAL, while avoiding certain other problems associated with these traditional TAR 1.0 protocols. Specifically, in each of the eight case studies, CAL reached higher levels of recall (finding relevant documents) more quickly and with less effort that the TAR 1.0 protocols.
Grossman and Cormack argue, attorneys should rely on scientific studies of the efficacy of CAR/TAR systems based on an analogy to the Daubert standard. They argue that evaluating the success of eDiscovery is burdensome and can be misleading. They liken the process of eDiscovery to that of roasting a turkey.