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Studying Systems of Communities

Studying Systems of Communities

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Studying Systems of Communities

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  1. Studying Systems of Communities Brian Butler University of Pittsburgh Xiaoqing Wang University of Pittsburgh

  2. Motivation • Benefits & costs • Commitment • Conversation Individuals Microsoft.public.xml Group • Structure • Culture & ideology • Type & identity Existing Research on Online Community Sustainability • Existing online community research typically consists one individual or one community at a time.

  3. Microsoft.public.xml Online Communities are not Isolated Source: http://netscan.research.microsoft.com

  4. Strategies for Examining Systems of Communities • Visualization • Cases • Impact of communities on one another • Aggregate impact measures • Dyadic impacts and structure • Impact of communities on individual behavior • Aggregate impact measures • Dual mode person/community networks • System structure models • “Market share” models

  5. Visualizations Source: http://netscan.research.microsoft.com

  6. Cases • Focus on one system of communities • Descriptive and exploratory analysis • Useful for: • Clarifying how you will define the system or systems of groups • Selecting a strategy for larger scale analyses • Determining what the available data actually allows you to describe (and how hard it really is to construct the measures) • Example: USENET newsgroups related to breast cancer

  7. Defining the System(s) of Interest • Formal Names • Peers in the USENET hierarchy • Managed (sort of) • Membership Niches • Other places that the people go/are • Emergent • Content Niches • Other places that the messages go/are • Emergent

  8. Formal Names • Names act as boundary objects • Boundary Object: “[A]rtifacts, documents, terms, concepts…around which a [group] can organize their interactions” (Thompson, 2005, quoting Wenger, 1998, p. 105). • They also serve to define the neighborhood likely to be visible to individuals seeking content or people related to a topic • Operational question: What is the (a?) relevant neighborhood for alt.support.cancer.breast?

  9. %cancer% Neighborhood • 26 groups (alt.support.cancer.breast is not included) • Post Characteristics • Total Messages: 139812 Posts • Total Replies: 105768 Replies • Total Starts: 34044 (18271 Successful) • Thread Start Ratio: 53.6% • Average Posts/Month/Group: 220 • Maximum Posts/Month/Group: 2014 • Minimum Posts/Month/Group: 1 • People Characteristics • Total Unique Posters: 17960 (+ one record for “0 author”– ?) • Average Authors/Month/Group: 61 • Average 1 Time Posters/Month/Group: 35 • Average Returnees/Month/Group: 16

  10. %cancer% System – Posting Activity Correlation: -0.484 (p = 0.225, N.S.)

  11. %cancer% System – Author Dynamics %cancer% System alt.support.cancer.breast Pearson Correlations Avg Authors/Month : 0.738 (p = 0.037) Avg 1 Timers/Month: 0.806 (p = 0.016) Avg Returnee/Month: 0.562 (p = 0.181)

  12. %cancer% Neighborhood – Author Dynamics (II) Correlations: First Seen – 0.506 (p = 0.201); Last Seen – 0.216 (p=0.607); 1st Long – 0.507 (p = 0.200)

  13. Descriptive Analysis of the System Raises Questions… • Is it competition between one group and others? • Posting activity hints that it might be… • Or common external influence • 9/11/2001 might have suppressed activity in non-support groups and increased it in alt.support.cancer.breast --------------------------------------------------------------- • Is it a case of “there goes the neighborhood? • Positive significant correlations among average group participation measures and general downward trend suggest that it might be…. • Or increased fragmentation • More active groups would potentially lower the average number of participants in each one.

  14. Membership-Based Systems • Comembership ‘links’ • Two newsgroups have a comembership link when they share a common poster • This treated as a dyadic network even though it may not be dyadic (could also be mixed mode graph). • Non-directed ties; directed ties can represent movement • Co-memberships ego network vs. comembership network centered a target

  15. alt.support.cancer.breast (ASCB) Membership System • Full membership network • Any newsgroup that shares a member with alt.support.cancer.breast any time during the study period • 143,710 newsgroups (+ the group itself) • 39,275 newsgroups share a single member with ASCB • 70,307 (49%) have 5 or fewer members in common with ASCB • The most extensive membership tie is alt.support.cancer with with 1502 participants in common (this is 37% of the participants in ASCB)

  16. alt.support.cancer.breast (ASCB) Shared Membership Definitions • Filter 1: Participant level of activity • Logic: Groups that share active members are have a stronger link than those that share peripheral or minimal member • Measurement: • Active span (First Seen – Last Seen) • Number of posts • Active span > 1 day is used (In ASCB 1680 (41%) are meet this criteria) • Implementation: Individuals must be active in both newsgroups • Results: • 90,958 newsgroups (+ the group itself) • 39,275 newsgroups share a single member with ASCB • 59,701 (66%) have 5 or fewer members in common with ASCB • The most extensive comembership tie is alt.support.cancer with with 433 participants in common (this is 27% of the active participants in ASCB)

  17. alt.support.cancer.breast (ASCB) Shared Membership Definitions • Filter 2: Number of Shared Participants • Logic: The more active members that two group share, the stronger the connection between them • Measurement: • Number of unique authors shared by two newsgroups across the entire study period • Implementation: A cutoff 55 or more shared active participants (~3% of ASCB’s active participants) was used because it results in a comembership neighborhood that was the same size (# of groups) as the %cancer% neighborhood. • Results: • 26 newsgroups (+ the group itself) • Maximum shared participants (433 – alt.support.cancer) • Minimum shared participants (55) • Average shared participants (103)

  18. Membership System Description • 26 groups (alt.support.cancer.breast is not included) • Post Characteristics • Total Messages: 7,582,551 Posts • Total Replies: 6,456,678 Replies • Total Starts: 1,125,873 (674,344 Successful) • Thread Start Ratio: 60% • Average Posts/Month/Group: 5152 • Maximum Posts/Month/Group: 95,862 • Minimum Posts/Month/Group: 1 • Poster Characteristics • Total Unique Posters: 304,089 (+ one record for “0 author”– ?) • Average Authors/Month/Group: 535 • Average 1 Time Posters/Month/Group: 263 • Average Returnees/Month/Group: 164

  19. Membership System – Posting Activity Correlation: 0.014 (p = 0.975)

  20. Membership System – Author Dynamics ASBC Member System alt.support.cancer.breast Pearson Correlations Avg Authors/Month : -0.765 (p = 0.027) Avg 1 Timers/Month: -0.476 (p = 0.233) Avg Returnee/Month: -0.638 (p = 0.089)

  21. ASBC Member Links Over Time(Shared Authors for Each of the 27 Groups)

  22. Content System • Crossposting network -- which we couldn’t assess with this data set. Source: http://netscan.research.microsoft.com

  23. Formal and Membership System Overlap • 26 groups in Formal Name System • 26 groups in ASBC Member System • Only 5 groups in both alt.support.cancer.testicular sci.med.prostate.cancer alt.support.cancer.prostate alt.support.cancer sci.med.diseases.cancer

  24. Larger Sample Research Approaches • Impact of communities on one another • Aggregate impact measures • Dyadic impacts and structure • Impact of communities on individual behavior • Aggregate impact measures • Dual mode person/community networks • System structure models • “Market share” models

  25. System Characteristics • Size of the system • #Crossposting groups • #Comember groups • Activity in the system: • AvgPostCount in the CoMember system • AvgAuthorCount in the CoMember system • OverlapRate = AvgSharedAuthorPerNG/ActiveAuthors • Question: How does the size of the system and the activity in it affect a focal group?

  26. Communities Affects (Aggregate)- Findings • Content-based and relationship-based competition both reduce the ability to retain members. • High bond-based communities are more affected by characteristics of the member system • Low bond-based communities are more affected by the characteristics of the content–based system. • Communities with high levels of contribution are more affected by the characteristics of the content–based system

  27. Community System Characteristics Effects on Individual Behavior • Size of the content-based system is negatively associated with an individual’s likelihood of ongoing participation in a given community • The larger the content-based community system, the lower the impact of an individual’s recent contribution activity on their likelihood of ongoing participation in a given community • Possible explanation: Competition interacting with individual investment in their relationship with a community

  28. Other Approaches to Studying Systems of Communities • Impact of communities on one another • Dyadic impacts and structure • Impact of communities on individual behavior • Dual mode person/community networks • System structure models • “Market share” models

  29. Various Research and Practical Questions • Competition vs. Complementary • Life cycle issues • System life cycle • Community life cycle • Member life cycle • Role of communities within systems --------------------------------------------------------------------- • To link or not to link? • How many communities should we have (and does it matter)?