Predicting tie strength with social media

Eric Gilbert, Karrie Karahalios

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.

Original languageEnglish (US)
Title of host publicationCHI 2009
Subtitle of host publicationDigital Life New World - Proceedings of the 27th International Conference on Human Factors in Computing Systems
Pages211-220
Number of pages10
DOIs
StatePublished - Dec 1 2009
Event27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009 - Boston, MA, United States
Duration: Apr 4 2009Apr 9 2009

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Other

Other27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009
CountryUnited States
CityBoston, MA
Period4/4/094/9/09

Keywords

  • Relationship modeling
  • Sns
  • Social media
  • Social networks
  • Tie strength
  • Ties

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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