%0 Conference Paper %1 Leskovec2010Predicting %A Leskovec, Jure %A Huttenlocher, Daniel %A Kleinberg, Jon %B Proceedings of the 19th International Conference on World Wide Web %C New York, NY, USA %D 2010 %I ACM %K opinions, wikipedia social-networks %P 641--650 %R 10.1145/1772690.1772756 %T Predicting Positive and Negative Links in Online Social Networks %U http://dx.doi.org/10.1145/1772690.1772756 %X We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network. %@ 978-1-60558-799-8

@inproceedings{Leskovec2010Predicting, abstract = {{We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.}}, added-at = {2019-06-10T14:53:09.000+0200}, address = {New York, NY, USA}, author = {Leskovec, Jure and Huttenlocher, Daniel and Kleinberg, Jon}, biburl = {https://www.bibsonomy.org/bibtex/2f5fd680abfb2b26477fb82c0e56d8fc2/nonancourt}, booktitle = {Proceedings of the 19th International Conference on World Wide Web}, citeulike-article-id = {7097816}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1772690.1772756}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1772690.1772756}, doi = {10.1145/1772690.1772756}, interhash = {24158224b6b45342017e1157f98f5c65}, intrahash = {f5fd680abfb2b26477fb82c0e56d8fc2}, isbn = {978-1-60558-799-8}, keywords = {opinions, wikipedia social-networks}, location = {Raleigh, North Carolina, USA}, pages = {641--650}, posted-at = {2015-07-15 15:38:48}, priority = {2}, publisher = {ACM}, series = {WWW '10}, timestamp = {2019-07-31T12:32:03.000+0200}, title = {{Predicting Positive and Negative Links in Online Social Networks}}, url = {http://dx.doi.org/10.1145/1772690.1772756}, year = 2010 }