Signed Networks in Social Media

arXiv:1003.2424v1 [physics.soc-ph] 11 Mar 2010 Signed Networks in Social Media Jure Leskovec Stanford University jure@cs.stanford.edu Daniel Huttenlocher Cornell University dph@cs.cornell.edu Jon Kleinberg Cornell University kleinber@cs.cornell.edu ABSTRACT Relations between users on social media sites often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. In contrast to the bulk of research on social net- works that has focused almost exclusively on positive inter- pretations of links between people, we study how the inter- play between positive and negative relationships affects the structure of on-line social networks. We connect our anal- yses to theories of signed networks from social psychology. We find that the classical theory of structural balance tends to capture certain common patterns of interaction, but that it is also at odds with some of the fundamental phenomena we observe — particularly related to the evolving, directed na- ture of these on-line networks. We then develop an alternate theory of status that better explains the observed edge signs and provides insights into the underlying social mechanisms. Our work provides one of the first large-scale evaluations of theories of signed networks using on-line datasets, as well as providing a perspective for reasoning about social media sites. Author Keywords signed networks, structural balance, status theory, positive edges, negative edges, trust, distrust. ACM Classification Keywords H.5.3 Information Systems: Group and Organization Inter- faces—Web-based interaction. General Terms Human Factors, Measurement, Design. INTRODUCTION Social network analysis provides a useful perspective on a range of social computing applications. The structure of net- works arising in such applications offers insights into pat- terns of interactions, and reveals global phenomena at scales that may be hard to identify when looking at a finer-grained resolution. At the same time, there is an ongoing challenge in adapting such network approaches to the study of social computing: users develop rich relationships with one an- other in these settings, while network analyses generally re- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. CHI 2010, April 10 – 15, 2010, Atlanta, Georgia, USA Copyright 2010 ACM 978-1-60558-929-9/10/04...$10.00. duce these complex relationship to the existence of simple pairwise links. It is a fundamental research problem to bridge the gap between the richness of the existing relationships and the stylized nature of network representations of these rela- tionships. The main focus of our work here is to examine the inter- play between positive and negative links in social media — a dimension of on-line social network analysis that has been largely unexplored. With relatively few exceptions (e.g., [1, 15, 16]), research in on-line social networks has focused on contexts in which the interactions have largely only positive interpretations — that is, connecting people to their friends, fans, followers, and collaborators. But in many settings it is important to also explicitly take negative relations into con- sideration, especially when studying interactions in social media: discussion lists are filled with controversy and dis- agreement, and social-networking sites harbor antagonism alongside amity. The richness of a social network in such cases generally consists of a mixture of both positive and negative interactions, co-existing in a single structure. We aim to develop a better understanding of the role that net- work structure plays when some links between people are positive while others are negative. For instance, in on-line rating sites such as Epinions, people can give both positive and negative ratings not only to items but also to other raters. In on-line discussion sites such as Slashdot, users can tag other users as “friends” and “foes”. Our approach here is to adapt and extend theories from social psychology to an- alyze these types of signed networks as they arise in social computing applications. These theories enable us to char- acterize the differences between the observed and predicted configurations of positive and negative links in on-line so- cial networks. We also use contrasts between the theories to draw inferences about how links are being used in particular social computing applications. In addition to insights into the applications themselves, our studies provide, to the best of our knowledge, some of the first large-scale evaluations of these social-psychological theories via on-line datasets. Positive and negative links in on-line data

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