New Metrics for New Media Bay Area CIO IT Executives Meetup

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Information about New Metrics for New Media Bay Area CIO IT Executives Meetup
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Published on August 5, 2009

Author: tkanzaveli

Source: slideshare.net

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Presentation done by Marc Smith, Chief Social Scientist, Telligent at the Bay Area CIO/IT Executives meetup http://www.meetup.com/CIO-IT-Executives/ run by Tatyana Kanzaveli.

Telligent Social AnalyticsNew Metrics for New MediaResearch & Tools Marc A. SmithChief Social ScientistTelligent Systems

E-mail (and more) is from people to people 2

Patterns are left behind 3

Social NetworkTheory Central tenet Social structure emerges from the aggregate of relationships (ties) among members of a population Phenomena of interest Emergence of cliques and clusters from patterns of relationships Centrality (core), periphery (isolates), betweenness Methods Surveys, interviews, observations, log file analysis, computational analysis of matrices (Hampton &Wellman, 1999; Paolillo,2001; Wellman, 2001) Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7-16

Social media platforms are a source of multiple Social network data sets:“Friends”“Replies”“Follows”“Comments”“Reads”“Co-edits”“Co-mentions”“Hybrids”

Hardin, Garrett. 1968/1977. “The tragedy of the commons.” Science 162: 1243-48. Pp. 16-30 in Managing the Commons, edited by G. Hardin and J. Baden. San Francisco: Freeman. Wellman, Barry. 1997. “An electronic group is virtually a social network.” In S. Kiesler (Ed.), The Culture of the Internet. Hillsdale, NJ: Lawrence Erlbaum. 6

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AnswerPersonSignatures Discussion People 11

Discussion Starter Spammer Reply orientedDiscussion Flame Warrior 12

The Ties that Blind? 13

Reply-To Network Network at distance 2 for the most prolific author of the microsoft.public.internetexplorer.general newsgroup The Ties that Blind?

15 Darwin Bell

The Ties that Blind? Pajek without modification can sometimes reveal structures of great interest.

Mapping Newsgroup Social Ties Microsoft.public.windowsxp.server.general 17 Two “answer people” with an emerging 3rd.

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Distinguishing attributes: Answer person Outward ties to local isolates Relative absence of triangles Few intense ties Reply Magnet Ties from local isolates Often inward only Sparse, few triangles Few intense ties Reply Magnet Ties from local isolates often inward only Sparse, few triangles Few intense ties Journal of Social Structure: “Visualizing the Signatures of Social Roles in Online Discussion Groups” http://www.cmu.edu/joss/content/articles/volume8/Welser/

Distinguishing attributes: Answer person Outward ties to local isolates Relative absence of triangles Few intense ties Discussion person Ties from local isolates often inward only Dense, many triangles Numerous intense ties Reply Magnet Ties from local isolates often inward only Sparse, few triangles Few intense ties Journal of Social Structure: “Visualizing the Signatures of Social Roles in Online Discussion Groups” http://www.cmu.edu/joss/content/articles/volume8/Welser/

Recent publications "Visualizing the Signatures of Social Roles in Online Discussion Groups”The Journal of Social Structure.  8(2) “Picturing Usenet” The Journal of Computer Mediated Communication “You are who you talk to” HICSS 2007

Leading research: Adamic et al. 2008 Knowledge Sharing and Yahoo Answers: Everyone Knows Something,Adamic, Lada A., Zhang Jun, BakshyEytan, and Ackerman Mark S. , WWW2008, (2008)

Use social network analysis measurements in reporting on social media data.Analytics calculates network metrics for all content authors.In-degreeOut-degreeEigenvector centralityClustering coefficientIngredients of User Type Scores

Social media usage generatesSocial Networks

Display community members sorted by network attributes using Excel Data|Sort



User type reports in Telligent Analytics Include social network metrics to define different kinds of contributors: Answerer: users who reply to many questions from many people. Influencer: users who are connected to other well connected users. Asker: users who raise questions that get answered by answer people. Connector: highly connected users who are replied to or linked to by many other community users. Originator: initiates new content in the site that is often linked to by others. Commenter: replies or links to content created by others. Spectator: reads but tends not to create content. Overseer: moderates content created by others.

NodeXL: Network Overview, Discovery and Exploration for Excel Leverage spreadsheet for storage of edge and vertex data http://www.codeplex.com/nodexl

The NodeXL Team

The NodeXL project is Available via the CodePlexOpen Source Project Hosting Site: Site:http://www.codeplex.com/nodexl



NodeXL:Display nodes with subgraph images sorted by network attributes using Excel Data|Sort

Resources to supportEducational Use ofNodeXLFree Tutorial/Manual Data SetsAvailable





NodeXL: Filtered clusters

NodeXL: Import social networks from email

NodeXL: Import social networks from email



Social Network Analysis Engine Development: NodeXL Extend and apply social network analysis engine: Improve layouts and visualizations Additional metrics and measures Technical architecture shift to the web and cloud Scale and performance Clustering and time series analysis

NodeXL Partnerships and community University of Maryland Ohio University Stanford University University of Pennsylvania YOU? 10,000 + downloads on Codeplex

Telligent Analytics Provides a source of network edge lists and integrates social network metrics in User Type Scores Further possible social network analysis applications Recommendations: my friend’s edit what documents? Search optimization: show documents from “answer people” Role discovery: who are the topic starters? The answer people?

Telligent Social AnalyticsResearch & Tools Marc A. SmithChief Social ScientistTelligent Systems Marc.Smith@Telligent.com http://www.telligent.com http://www.connectedaction.net

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