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Virtual Neighborhoods Architecture of Online Communities . Reuven Aviv Zippy Erlich Gilad Ravid Agenda. Introduction Design, Mechanisms, Architecture Method Results. Design of network. mechanisms. Social Interdependence theory.

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Virtual Neighborhoods Architecture of Online Communities

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Virtual neighborhoods architecture of online communities l.jpg

Virtual Neighborhoods Architecture of Online Communities

Reuven Aviv

Zippy Erlich

Gilad Ravid

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  • Introduction

  • Design, Mechanisms, Architecture

  • Method

  • Results

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Design of network


Social Interdependence theory

Matching the predictions of network emergence theories


Content Analysis

Network statistical analysis of Markov models

Collaborative Knowledge construction

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Global SNA



Equivalence (role groups)

Power of actors

Range of influence


Local SNA



Dyads and triads

SNA viewpoints

Aviv, R., Erlich, Z., Ravid, G., & Geva, A. (2003). Network Analysis of Knowledge Construction in Asynchronous Learning Networks. Journal of Asynchronous Learning Networks

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The Social Capital Mechanism

  • Hunt for Knowledge (social capital)

    • Using efficient interactions

    • E.g. bridging others

  • Community works with broadcast medium:

    • Most efficient connection: No interactions

      • Passive members (Lurkers)

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Cognitive balance

Emergence of transitive triads



Creation of knowledge support

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  • The architecture of a network can be describes in terms of three components

    • One or more relations are the fundamental glue between the actors

    • A partition of the actors and the relations into 2 level hierarchy of groups of actors

    • A set of mechanisms shaping the relations to the creation of the neighborhood

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In star

Mutual dyad

Mix star

Out star

Virtual Neighborhoods

Transitive triad

Cyclic triad

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Method of Analysis

  • Reveal Architectural Components

  • Identify Relevant Theories

  • Identify Mechanisms

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  • Analyze the recorded responsiveness data of two online forums of learners with different design

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Example: Two Communities

  • 16 weeks each; 19, 18 participants

  • Parts of Open U “Business Ethics” Course

  • Team community

    • Designed for Knowledge Construction

    • Tested positively by Content Analysis

  • Forum Community

    • Designed for support by Q & A

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Social Capital & Transaction Costs

  • Burt 1992, 2002

  • Bridge over Holes with minimal cost

  • Few single links

  • link<0

  • Supported for both networks

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Collective action

  • Coleman, 1973, 1986; Marwell & Oliver 1993; Fulk et al. 1996

  • Inducements to contribute under peer pressure

  • Respond to several others

  • If large density & centralization & size then out star > 0

  • Supported for team network.

  • Not supported for forum network because condition in not fulfilled

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  • Willer & Skvoretz, 1997; Hommans, 1958

  • Exchange resources directly, depending on partner & network status

  • Tendency to reciprocate to resource promising partners

  • mutuality > 0

  • Not supported for team network because there are no a-priory resource promising actors

  • supported for Forum network because Tutor is a-priory resource promising actors

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Generalized exchange

  • Bearman, 1997

  • Exchange resources via mediators, depending on partner & network states

  • Tendency to respond cyclically to resource promising partner

  • cyclicity > 0

  • Not supported in both networks. Probably because no need for information exchange via mediators

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Contagion Exposure

  • Burt 1980; Contractor et al., 1990

  • Leading to social influence & limitation in attitudes, knowledge & behavior

  • Respond to same as other equivalent actors

  • Out star > 0; in star > 0; mixed star >0; transitivity >0

  • Not supported in both networks. Probably because contagion process could not develop in the short lifetime of networks

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Cognitive Consistency

  • Heider 1958; Festinger, 1957; Cartwright et al., 1956

  • Drive for balance in cognitions

  • Respond via several paths

  • transitivity > 0

  • Supported in team networks. Not supported in the forum network. In both networks this is due to their designs

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Uncertainty reduction

  • Berger 1987

  • Reduce uncertainty by gaining

  • Attract responses from several others

  • In star > 0

  • Not supported in both networks. In the forum network the tutor clarified all uncertainties

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Exogenous factors

  • Residual personal tendencies o respond or trigger only to actors with pre assigned roles

  • For students (1)resp=0; (2)trigg=0; For tutor (3)resp>0; (4)trigg>0

  • 1,2 supported for both networks; 3 un supported for team network, supported for forum network; 4 un supported for both networks

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Thank You

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