CHAMP：根据市场政策从用户评论中识别不受欢迎的应用行为
CHAMP: Characterizing Undesired App Behaviors from User Comments based on Market Policies
摘要：
数以百万计的移动应用程序已通过各种应用程序市场提供。尽管大多数应用市场都强制执行了一些自动化甚至人工机制来对每个应用进行审核，但在不同的市场中仍然存在成千上万的低质量应用，其中一些应用违反了明确规定的市场政策。为了准确及时地识别这些违规行为，我们采用了用户评论，这可以形成对应用市场维护人员的即时反馈，识别违反市场政策的不良行为，包括与安全相关的用户问题。具体来说，我们提出了第一个大规模的研究，以检测和表征用户评论和市场政策之间的相关性。首先，我们提出了CHAMP，一种方法，采用文本挖掘和自然语言处理（NLP）技术，通过半自动化的过程中提取语义规则，并将评论分为26个预定义类型的不受欢迎的行为，违反市场政策。我们对真实世界的用户评论的评估表明，它实现了高精度和召回率（$>0.9$）在分类评论不受欢迎的行为。然后，我们从Google Play和8个流行的Android应用市场中的应用程序中策划了一个大规模的评论数据集（超过300万条用户评论），并应用CHAMP来了解野外不受欢迎的行为评论的特征。结果证实了我们的猜测，即用户评论可以用来确定违反应用市场政策的可疑应用。该研究还显示，尽管许多应用程序市场进行了广泛的审查，但违反政策的情况仍然普遍存在。CHAMP可以是一个\textit{whistle blower}，它分配违反策略的分数，并为应用程序识别信息量最大的评论。
英文摘要：
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.
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