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Predicting tie strength with social media (2009)
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| Venue: | In Proceedings of the Conferece on Human Factors in Computing Systems (CHI’09 |
| Citations: | 218 - 6 self |
BibTeX
@INPROCEEDINGS{Gilbert09predictingtie,
author = {Eric Gilbert and Karrie Karahalios},
title = {Predicting tie strength with social media},
booktitle = {In Proceedings of the Conferece on Human Factors in Computing Systems (CHI’09},
year = {2009},
pages = {220}
}
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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. Author Keywords Social media, social networks, relationship modeling, ties,
Keyphrases
tie strength social medium social medium data relationship modeling privacy control author keywords social medium total stranger message routing social network information prioritization predictive model modeling tie strength social medium treat topic social science social medium tie weak tie social medium design element quantitative finding
