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Decoding the Social Media Genome. SORIN ADAM MATEI – COM David Braun - ENVISION Seungyoon Lee - COM Lorraine Kisselburgh - COM Brian Britt – COM Collaborators March Smith (Connected Action / Microsoft Research / NodeXL ) Horia Petrache , Physics, IUPUI WikiTrust , UC Santa Cruz.

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Decoding the social media genome

Decoding the Social Media Genome

SORIN ADAM MATEI– COM

David Braun - ENVISION

Seungyoon Lee - COM

Lorraine Kisselburgh - COM

Brian Britt – COM

Collaborators

March Smith (Connected Action / Microsoft Research / NodeXL)

HoriaPetrache, Physics, IUPUI

WikiTrust, UC Santa Cruz


A research opportunity for computational social science projects
A research opportunity for computational social science projects

  • Wikipedia dataset

    • 2001 – 2008 editorial histories

    • 17 mil articles

    • Over 280 mil edits

    • Over 20 milunique editors


Social network representation of the data
Social Network representation of the data projects

  • Nodes: editors (contributors)

  • Edges (links): co-editorial contributions

    • If two editors contributed to the same article, they have a linkage

    • Gravitational model – more words, stronger links, the longer the time between interventions the weaker the links

  • Expected size

    • Billions of edges

    • >1 TB of data


Why the social media genome
Why the “social media genome”? projects

  • Does the network have a number of “bases,” (ie, subnetwork structures), similar to DNA

  • Do these bases recombine to create “genes”, (ie, functional structures)?

  • Are genes organized into larger aggregates?

  • How can you tell where a “gene” starts and one ends?

  • How can we tell what functions specific “genes” have?


Research agenda
Research agenda projects

  • Can the complexity of a gigantic network be explained by a relatively simple “network alphabet”? (DNA-like “structure,” “chromosomes”)

  • What are the smallest structural units of this network? (bases, ATGC)

    • Are they limited to a limited array of topologies?

    • Are these topologies associated with specific roles?

  • Do the roles/topologies combine and recombine at various scales to create more complex structures? (genes)

  • In brief, what is the:

    • Vocabulary

    • Grammar

    • Syntax


Using entropy to measure complexity of the social media genome
Using entropy to measure complexity of the social media genome

  • As networks get more complex, do they decrease the entropy of the social system?

  • If yes, by how much?

  • What is the overtime evolution of complexity/entropy of the social network described by Wikipedia?


Wikipedia has become more top heavy
Wikipedia has become more top heavy genome

  • As Wikipedia has added more and more users (13 mil, at the last count), its core group (<100,000) accounts (percentage-wise) for most of the total work

  • Wikipedia is not an expression of wise crowds, but of a clear and well oiled ad-hoc bureaucracy – ad-hocracy


ln(x) genome

Intervention entropy

Intervention number (events)

As articles are edited more, the proportion of people who account for most of the work decreases

Orange line – entropy (uneveness) of actual contributions

Dotted line – wisdom of crowds ceiling, all contributions would be equal


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