Learning to Perform Moderation in Online Forums

学习在在线论坛中进行审核

• 批准号: 0328601
• 负责人: Shlomo Zilberstein
• 金额: $ 25.08万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0328601&HistoricalAwards=false
• 关键词: Learning; Perform; Moderation; Online; Forums

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

学习在网上论坛中进行审核网上论坛是人们在互联网上寻找信息、讨论想法和获得建议的宝贵资源。 论坛的数量继续快速增长，涵盖政治、技术新闻和建议、医疗问题以及产品评级和意见等主题。 不幸的是，许多论坛有太多的活动，导致信息过载。 在一些论坛中实现了适度系统作为处理这个问题的一种方式，但由于稀疏性问题，它们通常是不够的。 该项目旨在使目前完全依赖人类的审核过程自动化。 通过在人类进行的调节中寻找模式，开发了一个用于学习执行机器调节的框架。 解决了四个基本的研究挑战：（1）识别定义好的或坏的评论的特征，并开发有效提取这些特征的方法;（2）开发可以训练以高准确度缓和任意评论的分类器;（3）使用在不同的（可能是无主持的）主持论坛上训练获得的知识;（4）开发一个系统，将联合收割机人机适度有效地结合起来。 数百万人已经定期使用在线论坛。 该项目将开发技术，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工审核来降低运营成本。 该项目的更广泛影响包括在马萨诸塞州大学培训研究生和本科生，涉及部署所产生技术的传统和非传统传播工作，以及与INRIA的一个国际研究小组新成立的联盟，France.http://anytime.cs.umass.edu/shlomo/research/MODERATE.html