Predicting tie strength with social media

Predicting tie strength with social media (2009)

by Eric Gilbert , Karrie Karahalios

Venue:	In Proceedings of the Conferece on Human Factors in Computing Systems (CHI’09
Citations:	50 - 1 self

Datum	Value	Source
TITLE	Predicting tie strength with social media	INFERENCE
AUTHOR NAME	Eric Gilbert	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Illinois at Urbana-Champaign	SVM HeaderParse 0.2
AUTHOR NAME	Karrie Karahalios	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Illinois at Urbana-Champaign	SVM HeaderParse 0.2
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,	SVM HeaderParse 0.2
YEAR	2009	INFERENCE
VENUE	In Proceedings of the Conferece on Human Factors in Computing Systems (CHI’09	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	220	INFERENCE
CITATIONS	38 found	ParsCit 1.0