Predicting tie strength with social media Export

@inproceedings{citeulike:4562241, 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.}, address = {New York, NY, USA}, author = {Gilbert, Eric and Karahalios, Karrie}, booktitle = {CHI '09: Proceedings of the 27th international conference on Human factors in computing systems}, citeulike-article-id = {4562241}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1518701.1518736}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1518701.1518736}, doi = {10.1145/1518701.1518736}, isbn = {978-1-60558-246-7}, keywords = {chi}, location = {Boston, MA, USA}, pages = {211--220}, posted-at = {2009-08-27 11:39:58}, priority = {2}, publisher = {ACM}, title = {Predicting tie strength with social media}, url = {http://dx.doi.org/10.1145/1518701.1518736}, year = {2009} }

TY - CONF ID - citeulike:4562241 L3 - citeulike-article-id:4562241 N2 - 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. CY - Boston, MA, USA SN - 978-1-60558-246-7 AD - New York, NY, USA EP - 220 TI - Predicting tie strength with social media PB - ACM T2 - CHI '09: Proceedings of the 27th international conference on Human factors in computing systems SP - 211 KW - chi AU - Gilbert, Eric AU - Karahalios, Karrie PY - 2009/// UR - http://dx.doi.org/10.1145/1518701.1518736 ER -