Understanding and Enabling Online Social Networks to Support Healthy Behaviors Noshir Contractor Jane S. & William J. White Professor of Behavioral Sciences Jane S. & William J. White Professor of Behavioral Sciences Professor of Ind Engg & Mgmt Sciences McCormick School of Engineering Professor of Ind. Engg & Mgmt Sciences, McCormick School of Engineering Professor of Communication Studies, School of Communication & Professor of Management & Organizations, Kellogg School of Management, Director, Science of Networks in Communities (SONIC) Research Laboratory nosh@northwestern.edu SONIC Supported by NSF IIS-0729505, Army Research Institute (W91WAW-08-C-0106), and Sony Online Entertainment Advancing the Science of Networks in Communities
Networks in Health • Networks of researchers, especially multi ‐ , inter ‐ , trans ‐ disciplinary • Networks to assemble teams of practitioners practitioners • Networks of patients N t k f ti t • Networks of “Science of Science” policy makers
Patientslikeme.com Patientslikeme.com
Healthy Lifestyle Network Healthy Lifestyle Network
MTML Social Drivers: Why do People Connect to Others? h d l h • Theories of self ‐ interest • Theories of contagion Theories of self interest • Theories of contagion • Theories of social and • Theories of balance resource exchange resource exchange • Theories of homophily • Theories of homophily • Theories of mutual • Theories of proximity interest and collective action Sources: Sources: Contractor, N. S., Wasserman, S. & Faust, K. (2006). Testing multi-theoretical multilevel hypotheses about organizational networks: An analytic framework and empirical example. Academy of Management Review. Monge, P. R. & Contractor, N. S. (2003). Theories of Communication Networks. New York: Oxford University Press.
“Structural Signatures” of MTML Structural Signatures of MTML A A B B F + F + - - C C E E D D Theories of Self interest Theories of Exchange Theories of Balance A A A B B F F - + B F + - + C E C E C D E D Novice G o v e rn m en t Expert D In d u stry Theories of Collective Action Theories of Collective Action Theories of Homophily Theories of Homophily Theories of Cognition Theories of Cognition
Statistical “MRI” for Structural Signatures Statistical MRI for Structural Signatures • p*/ERGM: Exponential Random Graph Models p /ERGM: Exponential Random Graph Models • Statistical “Macro ‐ scope” to detect structural motifs in observed networks motifs in observed networks • Move from exploratory to confirmatory network analysis to understand multi ‐ k l i d d l i theoretical multilevel motivations for why we create our social networks i l k
Multidimensional Networks Multiple types of Nodes and Multiple Types of Relationships
Its all about “Relational Metadata” • Technologies that “ capture ” communities’ relational meta ‐ data (Pingback and trackback in interblog networks blogrolls data (Pingback and trackback in interblog networks, blogrolls, data provenance) • Technologies to “ tag ” communities’ relational metadata (from Dublin Core taxonomies to folksonomies (‘wisdom of crowds’) like C i f lk i (‘ i d f d ’) lik – Tagging pictures (Flickr) – Social bookmarking (del.icio.us, LookupThis, BlinkList) – Social citations (CiteULike.org) Social citations (CiteULike org) – Social libraries (discogs.com, LibraryThing.com) – Social shopping (SwagRoll, Kaboodle, thethingsiwant.com) – Social networks (FOAF, SIOC, SocialGraph) Social networks (FOAF, SIOC, SocialGraph) • Technologies to “ manifest ” communities’ relational metadata (Tagclouds, Recommender systems, Rating/Reputation systems, ISI’s HistCite, Network Visualization systems) Hi tCit N t k Vi li ti t ) SONIC Advancing the Science of Networks in Communities
The Hubble telescope: $2.5 billion SONIC Source: David Lazer Advancing the Science of Networks in Communities
CERN particle accelerator: $ b ll $1 billion/year / SONIC Source: David Lazer Advancing the Science of Networks in Communities
The Web: priceless * * Apologies to MasterCard SONIC Source: David Lazer Advancing the Science of Networks in Communities
SONIC Advancing the Science of Networks in Communities
Using Digital Traces to Test MTML Using Digital Traces to Test MTML • Massively ‐ multiplayer online games (MMOGs) have Massively multiplayer online games (MMOGs) have over 45 million users worldwide and over $3 billion in revenue in 2008 • What does social behavior in online worlds tell us about the “real” world and vice versa? – Online games exhibit features that map onto real world processes: • Social networks economics groups communication conflict • Social networks, economics, groups, communication, conflict, expertise, leadership, crime, innovation, epidemics, etc. – Online games already capture the signatures of these b h behaviors in huge databases, just waiting to be analyzed. i i h d b j i i b l d
Rise of WoW SONIC Advancing the Science of Source: http://www.mmogchart.com/ Networks in Communities
Expertise/Information Retrieval Time One SONIC Advancing the Science of Networks in Communities
Expertise/Information Retrieval Time Two
Expertise/Information Retrieval Time Three
Unraveling the “Structural Signatures” “St t l Si t ” n Incentive for creating a WoW link with g someone = ‐ 1 55 (cost of creating a link) [Self ‐ interest] = 1.55 (cost of creating a link) [Self interest] + 0.55 (benefit of reciprocating) [Exchange] + 0.89 (benefit for being a friend of a friend) ( f f f f f ) [Balance] + 0.04 (benefit of connecting to an expert) [Cognition] [Cognition] SONIC All coefficients significant at 0.05 level Advancing the Science of Networks in Communities
Proximity & Homophily Proximity & Homophily • Long tradition of examining how physical proximity affects communication (Bossard, 1932; Stewart, 1942; Festinger, Schachter, & Back, 1950; Gullahorn, 1952) • Technology ‐ mediated proximity research began with the Technology mediated proximity research began with the availability of the telephone (Mayer, 1977) • With the computer, researchers began studying how offline distance affects online interaction (Cummings, ffli di t ff t li i t ti (C i Lee, & Kraut, 2006; Eveland & Bikson, 1986; Hampton & Wellman, 2001; Kraut, Egido, & Galegher, 1988) • New research suggests that online interactions organized more by the cost to communicate than by physical geography (Falk & Abler, 1980). g g p y ( )
Relevant MTML Theories Relevant MTML Theories • Theory of Proximity – People tend to form social relations with those who are geographically close to them. • Theory of Homophily Th f H hil – People tend to form social relations with those who share some prominent characteristics some prominent characteristics • Theory of Self ‐ interest – People are selective about making links online. People are selective about making links online. • Multi ‐ Theoretical Multilevel (MTML) Models – Contractor et al, 2006; Monge & Contractor, 2003 , ; g ,
Four Types of Relations in EQ2 Four Types of Relations in EQ2 • Partnership: two players play together in combat activities; p p y p y g ; • Instant messaging: two players exchange messages through Sony universal chat system • Player trade: players meet “face ‐ to ‐ face” in EQ2 and one gives items to another; • In ‐ game mail: one player sends a message and/or items to In game mail: one player sends a message and/or items to others by in ‐ game mail Synchronous Synchronous Asynchronous Asynchronous Interpersonal interaction Partnership, Instant messaging Transactional interaction Transactional interaction Player trade Player trade In ‐ game mail In game mail
Relation 1: Partnership Relation 1: Partnership
Relation 2: Instant Messaging Relation 2: Instant Messaging
Relation 3: Player Trade Relation 3: Player Trade
Relation 4: In ‐ game Mail Relation 4: In game Mail
Black: male Red: female Partnership Instant messaging Trade In-game mail
Hypotheses ‐ Proximity Hypotheses Proximity • H1: (Spatial proximity) Individuals who are proximate H1: (Spatial proximity) Individuals who are proximate in geographical distance are more likely to engage in online interaction than those who are not proximate. • H2: (Interaction types) Individuals who are proximate are more likely to engage in online interpersonal interactions (i.e. partnership and instant messaging) than transactional interactions (i.e. trade or mail).
Hypotheses ‐ Homophily Hypotheses Homophily • H3: (Gender) Individuals of the same gender are H3: (Gender) Individuals of the same gender are more likely to engage in interaction than those of opposite genders. • H4: (Age) Individuals who have smaller age differences are more likely to engage in interaction than those who have bigger differences. • H5: (Experience) Players who have similar years of game experience are more likely to engage in l k l interaction than those who have bigger differences.
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