一句话：提出 CHAMP，用文本挖掘与 NLP 从用户评论识别违反应用市场政策的不良行为，并开展大规模实证分析。
应用市场虽有机审和人工审核，仍存在大量违反市场政策的低质量应用。本文首次大规模研究用户评论与市场政策之间的关联，提出 CHAMP：利用文本挖掘和 NLP 技术，通过半自动流程抽取语义规则，将评论分类为 26 种预定义的、违反市场政策的不良行为类型。在真实评论上，分类精确率和召回率均超过 0.9。作者进一步从 Google Play 和 8 个热门第三方 Android 市场收集超过 300 万条评论，用 CHAMP 刻画野外不良行为评论特征。结果表明用户评论可定位疑似违规应用，且政策违规在多市场普遍存在；CHAMP 可作为吹哨者给出违规评分并识别最有信息量的评论。
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 whistle blower that assigns policy-violation scores and identifies most informative comments for apps.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2103-00712,
title = {CHAMP: Characterizing Undesired App Behaviors from User Comments Based on Market Policies},
author = {Yangyu Hu and Haoyu Wang and Tiantong Ji and Xusheng Xiao and Xiapu Luo and Peng Gao and Yao Guo},
booktitle = {ICSE},
year = {2021},
doi = {10.1109/icse43902.2021.00089}
}