Learning to Perform Moderation in Online Forums

学会在网上论坛中保持适度在线讨论论坛对于那些希望在互联网上查找信息、讨论想法和获得建议的人是一个宝贵的资源。论坛数量继续快速增长，涵盖政治、技术新闻和建议、医疗问题、产品评级和意见等主题。不幸的是，许多论坛的活跃度太高，导致信息过载。一些论坛实现了审核系统，作为处理此问题的一种方式，但由于稀疏性问题，它们通常是不够的。这个项目的目的是使目前完全依赖人类的节制过程自动化。通过在人类的适度中寻找模式，开发了一个学习执行机器适度的框架。解决了四个基本的研究挑战：(1)确定定义好坏评论的特征，并开发有效提取这些特征的方法；(2)开发可以被训练成高精度地调节任意评论的分类器；(3)使用在不同的、可能是非适度的论坛的主持论坛上训练中获得的知识；(4)开发一个有效结合人和机器审查的系统。数以百万计的人已经定期使用在线论坛。该项目将产生的技术将提高向在线论坛用户提供的服务质量，并通过大幅减少所需的人力节制来降低业务成本。该项目的更广泛影响包括培训马萨诸塞大学的研究生和本科生，涉及部署所产生的技术的传统和非传统传播努力，以及与INRIA，France.http://anytime.cs.umass.edu/shlomo/research/MODERATE.html的一个国际研究团队新成立的联盟

Learning to Perform Moderation in Online ForumsOnline discussion forums are a valuable resource for people looking to find information, discuss ideas, and get advice on the Internet. The number of forums continues to grow rapidly, covering such topics as politics, technical news and advice, medical issues, and product ratings and opinions. Unfortunately, many forums have too much activity, resulting in information overload. Moderation systems are implemented in some forums as a way to handle this problem, but due to sparsity issues, they are often not sufficient. This project is aimed at automating the moderation process, which currently relies entirely on humans. A framework for learning to perform machine moderation is developed by finding patterns in the moderations made by humans. Four fundamental research challenges are addressed: (1) Identify features that define a good or bad comment and develop methods to extract these features efficiently; (2) Develop classifiers that can be trained to moderate arbitrary comments with high accuracy; (3) Use the knowledge acquired in training on moderated forums in different, possibly unmoderated, forums; (4) Develop a system to combine human and machine moderation effectively. Millions of people already use online forums on a regular basis. This project will produce technology that will improve the quality of service provided to users of online forums and reduce the cost of operation by reducing substantially the amount of human moderation that is needed. The broader impact of the project includes training graduate and undergraduate students at the University of Massachusetts, traditional and non-traditional dissemination effort involving deployment of the resulting technology, and a newly formed alliance with an international research team at INRIA, France.http://anytime.cs.umass.edu/shlomo/research/MODERATE.html