  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.
