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

社会技术系统提供了获取越来越多的在线信息的途径。为了帮助人们应对信息过载，这些系统实现了“算法管理”：自动选择应该向用户显示哪些内容，应该隐藏哪些内容，以及应该如何呈现。实际上，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种程度上遇到过算法管理，可能甚至没有意识到这一点。在一个社会技术系统中，用户的贡献、社会关系和行为以及技术的特征是相互依赖的，它们决定了系统的用途、使用方式以及随着时间的推移如何演变。这个研究项目的目标是调查社会行为和算法管理之间的关系，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。本项目采用一种多方法方法来确定社会和技术机制如何影响个人用户的信息生产和消费，从而形成用户群体和贡献主体的系统级属性。实验室实验研究了不同类型的算法管理如何影响社会过程，例如遵守社会规范和改变目标受众的沟通方式。实地研究增强了实验室实验，使用技术干预来展示这些变化如何随着时间的推移和算法的变化在现实世界中发挥作用。在系统层面，基于代理的模型将个人层面的过程与算法管理的系统层面效果联系起来，大规模的数据收集在真实系统中寻找这些效果的迹象。该项目推进了目前对在日益互联和自动化的环境中塑造信息访问和使用的力量的理解。结果将以设计模式的形式为创建和操作算法的系统设计师提供指导，这些设计模式将支持系统的、可推广的方式来规划不同规模的算法管理的效果。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，并有效地在跨学科合作项目中工作。

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.