一种用于分析（地下）论坛的基于图的分层抽样方法
A Graph-based Stratified Sampling Methodology for the Analysis of (Underground) Forums
AI总结：
针对地下论坛帖子标注成本高的问题，提出基于图度中心性的分层抽样方法，均匀度中心性样本可在保持精确率的同时将召回率提升30%。
摘要：
[背景]研究人员分析地下论坛以研究滥用和网络犯罪活动。由于论坛规模庞大，且识别犯罪讨论需要领域专业知识，大多数方法采用监督机器学习技术来自动分类感兴趣的帖子。[目标]人工标注成本高昂。如何选择待标注样本以兼顾论坛结构？[方法]我们提出一种基于总体中心性属性信息生成分层样本并评估分类器性能的方法。[结果]我们观察到，与分布遵循总体分层的样本相比，采用由帖子度中心性指标的均匀分布获得的样本，可保持相同的精确率水平，但召回率显著提高（+30%）。我们发现，用相似样本训练的分类器在部署到整个论坛时，对犯罪活动分类的不一致率最高达33%。
英文摘要：
[Context] Researchers analyze underground forums to study abuse and cybercrime activities. Due to the size of the forums and the domain expertise required to identify criminal discussions, most approaches employ supervised machine learning techniques to automatically classify the posts of interest. [Goal] Human annotation is costly. How to select samples to annotate that account for the structure of the forum? [Method] We present a methodology to generate stratified samples based on information about the centrality properties of the population and evaluate classifier performance. [Result] We observe that by employing a sample obtained from a uniform distribution of the post degree centrality metric, we maintain the same level of precision but significantly increase the recall (+30%) compared to a sample whose distribution is respecting the population stratification. We find that classifiers trained with similar samples disagree on the classification of criminal activities up to 33% of the time when deployed on the entire forum.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
