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Automatic Detection of Search Tactic in Individual Information Seeking: A Hidden Markov Model Approach

Han, Shuguang and Yue, Zhen and He, Daqing Automatic Detection of Search Tactic in Individual Information Seeking: A Hidden Markov Model Approach. In: UNSPECIFIED.

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Abstract

Information seeking process is an important topic in information seeking behavior research. Both qualitative and empirical methods have been adopted in analyzing information seeking processes, with major focus on uncovering the latent search tactics behind user behaviors. Most of the existing works require defining search tactics in advance and coding data manually. Among the few works that can recognize search tactics automatically, they missed making sense of those tactics. In this paper, we proposed using an automatic technique, i.e. the Hidden Markov Model (HMM), to explicitly model the search tactics. HMM results show that the identified search tactics of individual information seeking behaviors are consistent with Marchioninis Information seeking process model. With the advantages of showing the connections between search tactics and search actions and the transitions among search tactics, we argue that HMM is a useful tool to investigate information seeking process, or at least it provides a feasible way to analyze large scale dataset.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Han, Shuguang
Yue, Zhen
He, Daqingdah44@pitt.eduDAH44
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Library and Information Science
Refereed: Yes
Uncontrolled Keywords: cs.IR, cs.IR
Additional Information: 5 pages, 3 figures, 3 tables
Date Deposited: 17 Jun 2013 15:54
Last Modified: 01 Nov 2017 14:01
URI: http://d-scholarship.pitt.edu/id/eprint/18997

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