BITlab: Behavior Information Technology

BITlab
404 Wilson Rd. Room 251
Communication Arts & Sciences
Michigan State University
East Lansing, MI 48824

Effects of automated information selection and presentation in online information systems

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.

Summer 2014 REU Poster
Poster Presented at 2014 MID-Sure

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.

This material is based upon work supported by the National Science Foundation under Grant No. IIS-1217212. Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

Funded by NSF Award IIS-1217212

PI: Emilee Rader

Nicholas GilreathNicholas Gilreath
Janghee ChoJanghee Cho

Mailing list: curation@bitlab.cas.msu.edu

Publications

  • Emilee Rader. “Examining User Surprise as a Symptom of Algorithmic FilteringInternational Journal of Human-Computer Studies. Vol. 98 pp. 72-88. 2017. ( Link )

  • Emilee Rader and Rebecca Gray. “Understanding User Beliefs About Algorithmic Curation in the Facebook News FeedProceedings of the ACM Conference on Human Factors in Computing (SIGCHI). Seoul, Korea. April 2015. ( Link )

  • Emilee Rader, Alcides Velasquez, Kayla Hales, and Helen Kwok. “The Gap Between Producer Intentions and Consumer Behavior in Social MediaProceedings of the ACM Conference on Supporting Group Work (GROUP). Sanibel Island, FL. November 2012. ( Abstract, PDF, ACM DL )

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