  Millions of mobile apps have been available through various app markets.
Although most app markets have enforced a number of automated or even manual
mechanisms to vet each app before it is released to the market, thousands of
low-quality apps still exist in different markets, some of which violate the
explicitly specified market policies.In order to identify these violations
accurately and timely, we resort to user comments, which can form an immediate
feedback for app market maintainers, to identify undesired behaviors that
violate market policies, including security-related user concerns.
Specifically, we present the first large-scale study to detect and characterize
the correlations between user comments and market policies. First, we propose
CHAMP, an approach that adopts text mining and natural language processing
(NLP) techniques to extract semantic rules through a semi-automated process,
and classifies comments into 26 pre-defined types of undesired behaviors that
violate market policies. Our evaluation on real-world user comments shows that
it achieves both high precision and recall ($>0.9$) in classifying comments for
undesired behaviors. Then, we curate a large-scale comment dataset (over 3
million user comments) from apps in Google Play and 8 popular alternative
Android app markets, and apply CHAMP to understand the characteristics of
undesired behavior comments in the wild. The results confirm our speculation
that user comments can be used to pinpoint suspicious apps that violate
policies declared by app markets. The study also reveals that policy violations
are widespread in many app markets despite their extensive vetting efforts.
CHAMP can be a \textit{whistle blower} that assigns policy-violation scores and
identifies most informative comments for apps.
