  Click-through rate (CTR) is a key signal of relevance for search engine
results, both organic and sponsored. CTR of a result has two core components:
(a) the probability of examination of a result by a user, and (b) the perceived
relevance of the result given that it has been examined by the user. There has
been considerable work on user browsing models, to model and analyze both the
examination and the relevance components of CTR. In this paper, we propose a
novel formulation: a micro-browsing model for how users read result snippets.
The snippet text of a result often plays a critical role in the perceived
relevance of the result. We study how particular words within a line of snippet
can influence user behavior. We validate this new micro-browsing user model by
considering the problem of predicting which snippet will yield higher CTR, and
show that classification accuracy is dramatically higher with our
micro-browsing user model. The key insight in this paper is that varying
relatively few words within a snippet, and even their location within a
snippet, can have a significant influence on the clickthrough of a snippet.
