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.

从一个看门人和编辑在内容发布前对内容进行过滤的世界，到一个充满用户生成内容的世界，在发布后进行信息过滤，这种转变正在发生。今天的在线社区已经开发了各种基于社区的过滤和评级机制，以帮助保持质量和可管理性。然而，这些过滤机制代表的是“大众智慧”还是“审查暴徒”，这是一个悬而未决的问题。该项目将应用统计机器学习和人种学研究来了解在线社区自下而上审查内容的机制。这种理解将有助于深入了解价值观是如何以及如何嵌入到这些大型的社会智能系统中。最终，目标是设计社会智能社区过滤系统，其中个人，社区和智能软件代理协作，解释社会，自下而上过滤背后的机制，并根据这些系统可以反映的价值和它可以服务的社区扩大可能的范围。本项目将研究性别的社会建构如何影响社区过滤系统的机制。这将通过对两个在线社区的深入研究来完成，这两个社区有强有力的社区监管评论过滤；一个参与者以男性为主，另一个参与者以女性为主。在线社区正迅速成为现代公共广场，社区过滤有可能使空间充满活力和有用，或者退化为一种审查形式。我们民间社会的健康状况及其应对重大挑战的能力取决于其公共话语的健康状况。通过创建反映社区的社会智能过滤系统，我们促进了多样性，在这种情况下，少数人的立场得到保护和保留，而与此同时，多数人的立场有机会发展和完善有说服力的论点，这是一场理性辩论所必需的。