III: HCC: Small: Effects of Automated Information Selection and Presentation in Online Information Systems

III：HCC：小：在线信息系统中自动信息选择和呈现的影响

• 批准号: 1217212
• 负责人: Emilee Rader
• 金额: $ 48.61万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217212&HistoricalAwards=false
• 关键词: III; HCC; Small; Effects; Automated

Socio-technical systems provide access to ever-increasing quantities of information online. To help people cope with information overload, these systems implement "algorithmic curation": automated selection of what content should be displayed to users, what should be hidden, and how it should be presented. Virtually every Internet user who reads online news, visits social media sites, or uses a search engine has encountered algorithmic curation at some point, probably without even realizing it. In a socio-technical system, user contributions, social relationships and behavior, and features of the technology are interdependent, and determine what the system is used for, how it is used, and how it evolves over time. The goal of this research project is to investigate the relationship between social behavior and algorithmic curation, in order to better predict the effects of this pervasive practice on what we read, contribute, and communicate about online.This project uses a multi-method approach to identify ways in which social and technical mechanisms influence individual users' information production and consumption, and thereby shape system-level properties of the user population and the corpus of contributions. Lab experiments investigate how social processes, such as obeying social norms and altering communications for an intended audience, are affected by different types of algorithmic curation. Field studies augment the lab experiments, using technology interventions to demonstrate how these changes play out for people in the real world over time, and as algorithms change. At the system level, agent-based models connect individual-level processes with system-level effects of algorithmic curation, and large-scale data collection looks for signs of those effects on real systems.This project advances the current understanding of forces that shape information access and use in an increasingly connected and automated environment. Results will be used to provide guidance to system designers who create and manipulate algorithms, in the form of design patterns that will support a systematic, generalizable way of planning for effects of algorithmic curation at different scales. The project Web site (http://bitlab.cas.msu.edu/curation) provides information. Undergraduate and graduate students involved in the project will become better problem solvers, and work effectively on collaborative interdisciplinary projects.

社会技术系统提供了对日益增多的在线信息的访问。为了帮助人们科普信息过载，这些系统实现了“算法策展”：自动选择应该向用户显示什么内容，应该隐藏什么内容，以及应该如何呈现。事实上，每一个阅读在线新闻、访问社交媒体网站或使用搜索引擎的互联网用户都在某个时候遇到过算法策展，甚至可能没有意识到这一点。在社会技术系统中，用户贡献、社会关系和行为以及技术特征是相互依赖的，并决定了系统的用途、使用方式以及随着时间的推移如何演变。该研究项目的目标是调查社会行为和算法策展之间的关系，以便更好地预测这种普遍做法对我们在线阅读，贡献和交流的影响。该项目使用多方法方法来确定社会和技术机制影响个人用户信息生产和消费的方式，从而形成用户群体和贡献语料库的系统级属性。实验室实验研究了不同类型的算法策展如何影响社会过程，例如遵守社会规范和改变目标受众的沟通。实地研究补充了实验室实验，使用技术干预来展示这些变化如何随着时间的推移以及算法的变化而在真实的世界中对人们产生影响。在系统层面，基于代理的模型将个人层面的过程与算法策展的系统层面效应联系起来，大规模数据收集将寻找这些效应在真实的系统上的迹象。该项目推进了当前对在日益连接和自动化的环境中塑造信息访问和使用的力量的理解。结果将用于为创建和操作算法的系统设计人员提供指导，以设计模式的形式，支持以系统的，可推广的方式规划不同规模的算法策展效果。项目网站（http：//bitlab.cas.msu.edu/curation）提供了相关信息。参与该项目的本科生和研究生将成为更好的问题解决者，并有效地合作跨学科项目。