Automatic Detection of Search Tactic in Individual Information Seeking: A Hidden Markov Model Approach
This provides a tool for analyzing large-scale datasets in information seeking behavior research, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of automatically detecting search tactics in individual information seeking by using a Hidden Markov Model (HMM) to model these tactics, showing that the identified tactics are consistent with Marchionini's Information Seeking Process model.
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.