EAGER: Investigating Diversity in Online Community Filtering

EAGER：调查在线社区过滤的多样性

• 批准号: 1048515
• 负责人: Rachel Greenstadt
• 金额: $ 9.58万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048515&HistoricalAwards=false
• 关键词: EAGER; Investigating; Diversity; Online; Community

A transition is occurring from a world in which gatekeepers and editors filter content before it is published to a world full of user-generated content in which information filtering is done after publication. Today's online communities have developed a variety of community-based filtering and rating mechanisms to help maintain quality and manageability. However, it is an open question whether these filtering mechanisms represent "the wisdom of crowds" or "the censoring mob."This project will apply statistical machine learning and ethnographic studies to understand the mechanisms by which online communities censor content from the bottom up. This understanding will provide insight into how values are and can be embedded into these large, socially intelligent systems. Ultimately, the goal is to design socially intelligent community filtering systems in which individuals, communities, and intelligent software agents collaborate, to explain the mechanisms behind social, bottom-up filtering, and expand the range of the possible in terms of the values these systems can reflect and the communities it can serve. This project will study the mechanisms through which the social construction of gender impacts community filtering systems. This will be done via an in-depth study of two online communities that have vigorous community policed comment filtering; one whose participants are predominantly male and another whose participants are predominantly female.Online communities are rapidly becoming the modern public square and community filtering has the potential to make the space vibrant and useful and/or degenerate into a form of censorship. The health of our civil society and its ability to address large challenges depends on the health of its public discourse. By creating systems for socially intelligent filtering that reflect the community we facilitate diversity, in that minority positions are protected and preserved, while at the same time majority positions have the opportunity to develop and refine cogent arguments necessary for a well reasoned debate.

从一个世界上进行过渡，在这个世界中，守门者和编辑器将其发布到一个充满用户生成内容的世界之前，在发布后进行了信息过滤。当今的在线社区已经开发了各种基于社区的过滤和评级机制，以帮助维持质量和可管理性。但是，这些过滤机制是否代表“人群的智慧”或“审查暴民”是一个悬而未决的问题。该项目将采用统计机器学习和人种学研究来了解在线社区从底部审查内容的机制。这种理解将提供有关价值观和可以嵌入这些大型社会智能系统的洞察力。最终，目标是设计具有社会智能的社区过滤系统，在该系统中，个人，社区和智能软件代理会协作，解释社交，自下而上的过滤背后的机制，并扩大了这些系统可以反映的价值观及其可以提供的社区的可能性范围。该项目将研究社会建构影响社区过滤系统的机制。这将通过对两个有力的社区监管评论过滤的在线社区进行深入研究来完成。一个人主要是男性，另一个是他的参与者主要是女性。在线社区正在迅速成为现代公共广场，社区过滤有可能使太空充满活力，有用和/或退化为审查形式。我们的公民社会的健康及其应对大型挑战的能力取决于其公共话语的健康。通过为反映社区的社会智能过滤制定系统，我们促进了多样性，在少数群体的立场得到保护和保留，而同时多数职位有机会发展和完善有理由辩论所必需的有力论据。