This paper presents a general detection framework, and develops a variety of content and structure features to find high quality threads. The feature selection algorithm, which is a combination of genetic algorithm, Tabu search and a machine learning algorithm, is designed to attain a better assessment of key features. In this paper, an experiment is done that focuses on the Tencent Message Boards. The experimental results, obtained from a large scale evaluation of over thousands of real web forum threads and user ratings, demonstrate the feasibility of modeling and detecting high quality threads. The proposed feature extraction methods, feature selection algorithms, and detection framework can be useful for a variety of domains such as Blogs and social network platforms.%提出了一种通用的高质量主题发现框架.在该框架下,利用特征抽取技术提取内容特征,利用结构特征去发现高质量主题.提出了一种基于遗传算法、禁忌搜索与机器学习的特征选择算法,周来评价被抽取特征的重要性.在腾讯论坛数据集上进行了大量的实验.实验结果表明,该框架能够很好地发现高质量主题.提出的特征抽取算法、特征选择算法以及高质量主题发现框架能够在很多Web2.0领域得到应用,例如,博客、社会网络平台等.
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