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Information Flow and the Locus of

Influence in Online User Networks:

The Case of iOS Jailbreak *

Nitin Mayande & Charles Weber

Department of Engineering and Technology Management

Portland State University

12

th

OUI Conference, Boston, Mass., USA

July 28 – 30, 2014

*

This material is based upon work supported by the National Science Foundation under Grant No. 0822062 (Enabling Timely Revolutions in Organizational Performance).

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Outline

Influence in online user communities

Motivation for Study

Extant theory

Quantitative Empirical Study

(setting, measures, criterion)

Findings

Implications

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Understanding the Nature of Influence Online

Networks

Is becoming increasingly important

For the first time in history, Americans are expected to spend

more time online this year than watching television.

Many companies are reallocating their marketing resources

to specifically target social networking platforms

such as Facebook and Twitter.

For example, Proctor and Gamble spend between 25% and 35% of its

marketing budget on social media.

1

They are not just doing this to push their marketing messages;

they also want to entice the user communities within their

user networks to participate in the innovation process.

(Mayande and Jetter, 2010)

1

http://online.wsj.com/article/SB10001424127887323681904578641993173406444.html

accessed on 04/01/2014.

Mayande & Weber -- OUI 2014 3 7/25/2014

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Motivation for Study

Yet, the influence that individuals wield within

online user networks has yet to be quantified.

What factors determine influence within

online user networks?

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Extant Theory on Social Networks

(e.g., Granovetter, 1973; Coleman, 1988; Powell, 1990; Burt, 1992; Podolny, 1993; Putnam, 1995)

Not particularly useful in characterizing the loci of influence in

online networks.

Primarily based on connectivity,

Derived from phenomena that are subject to real-world

constraints.

We cannot assume that it can explain the behavior of online

networks,

which are highly dynamic open systems

to which traditional constraints such as physical distance

(Allen,

1977)

and geography do not apply.

Mayande & Weber -- OUI 2014 5 7/25/2014

Extant theory does not show how influence within a

network can depend upon control of information

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Quantitative Empirical Study

Investigates the nature of influence in an

online user innovation network

Characterizes how influence in this network

depends upon information flow.

Statistically analyzes the Twitter traffic

that transpired between the network’s members

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Setting:

Apple’s Jailbreak Community

(

http://en.wikipedia.org/wiki/Jailbreak_(iPhone_OS)

)

Members collaborate online

to remove restrictions on iOS,

Apple’s operating system for mobile devices.

Observed throughout September 2012.

Three significant events took place during that month:

1. a YouTube video about Jailbreak became available on Sept. 1;

2. a press release about a jailbreak of iOS6 occurred on Sept. 13,

3. Apple released the iPhone5, which featured iOS6, on Sept. 21.

Mayande & Weber -- OUI 2014 7 7/25/2014

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Measuring Information Flow

(See also Appendix A)

The interaction between members of an online

network represents a form of serial propagation.

(Mayande and Weber, 2012)

Number of paths

acts as a measure of the extent to which information

can spread virally within the network,

Number of geodesics

(shortest paths)

acts as a proxy for the speed at which information

spreads within the network.

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Criterion for Influence:

Eigenvector Centrality (EVC)

(Bonacich, 1972)

Chosen because,

in contrast to well-known centrality metrics from graph theory

such as

degree

,

betweenness

and

closeness

(Freeman 1977, 1979),

it measures the relative importance of particular nodes.

(Mayande, Weber & Jetter, 2011)

Assumes that,

ceteris paribus

,

connections to high-degree nodes are more important

than connections to low-degree nodes.

(For example, Google's PageRank is a variant of the

eigenvector centrality measure.)

Mayande & Weber -- OUI 2014 9 7/25/2014

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Key Finding 1

User networks on open platforms like Twitter

are activated by or in anticipation of specific

events.

Once the event passes the network dissipates.

People within the user network interact

even if they are not friends with or followers

of each other;

they simply share a common interest in a

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Trend Chart of Normalized Data of Twitter Traffic

for iOS Jailbreak User Network

People--Total number of people involved in conversation about Jailbreak (daily max. = 27788 people) Tweets--Total number of tweets that form the Jailbreak conversation (daily max. = 30362 tweets) Network Size--The biggest connected network of people within the Jailbreak conversation

(daily max. = 2334 nodes)

Network Tweets—Total number of tweets that form the biggest network (daily max. = 3116 people) Graph Diameter--The longest geodesic (shortest path) in the network (daily max. = 29 ties)

(Graph diameter: everybody in the network has access to everybody else in the network.)

Mayande & Weber -- OUI 2014 11 YouTube Video Release

about Jailbreak

Press Release about Jailbreak of iOS6

Apple releases iPhone5 with iOS6.

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Key Finding 2

Eigenvector centrality

correlates

well

with the number of paths,

but

not

with the number of shortest paths

(geodesics).

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EVC vs. Total # of Paths

Daily correlation between total number of paths and eigenvector centrality

(TPvsEVP) for September 2012.

Statistical significance is given by p1.

Mayande & Weber -- OUI 2014 13 0.00 0.20 0.40 0.60 0.80 1.00 1 -2 SE P 2 -3 SE P 3 -4 SE P 4 -5 SE P 5 -6 SE P 6 -7 SE P 7 -8 SE P 8 -9 SE P 9 -1 0 SEP 10 -1 1 S EP 11 -1 2 S EP 12 -1 3 S EP 13 -1 4 S EP 14 -1 5 S EP 15 -1 6 S EP 16 -1 7 S EP 17 -1 8 S EP 18 -1 9 S EP 19 -2 0 S EP 20 -2 1 S EP 21 -2 2 S EP 22 -2 3 S EP 23 -2 4 S EP 24 -2 5 S EP 25 -2 6 S EP 26 -2 7 S EP 27 -2 8 S EP 28 -2 9 S EP 29 -3 0 S EP TPvsEVC (Corr.) p1 (Sig.) 7/25/2014

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EVC vs. Total # of Geodesics

Daily correlation between total number of shortest paths (geodesics) and

eigenvector centrality (TSPvsEVP) for September 2012.

Statistical significance is given by p2.

-0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00 1-2 SE P 2-3 SE P 3-4 SE P 4-5 SE P 5-6 SE P 6-7 SE P 7-8 SE P 8-9 SE P 9-10 SEP 10 -1 1 S EP 11 -1 2 S EP 12 -1 3 S EP 13 -1 4 S EP 14 -1 5 S EP 15 -1 6 S EP 16 -1 7 S EP 17 -1 8 S EP 18 -1 9 S EP 19 -2 0 S EP 20 -2 1 S EP 21 -2 2 S EP 22 -2 3 S EP 23 -2 4 S EP 24 -2 5 S EP 25 -2 6 S EP 26 -2 7 S EP 27 -2 8 S EP 28 -2 9 S EP 29 -3 0 S EP TSPvsEVC (Corr.) p2 (Sig.)

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Implication of Findings

The extent to which information can spread

virally,

rather than the speed of information

propagation,

determines the loci of influence in online user

networks.

Alternatively, eigenvector centrality may not a

universal measure of influence in online user

networks

Mayande & Weber -- OUI 2014 15 7/25/2014

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Further Research

Better measures of influence in online user

networks to be found.

This study is a work in progress,

which will hopefully lead to a behavioral theory of

online user networks

that encompasses influence, networks structure

and information flow.

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About the Authors

Nitin Mayande has been an independent consultant in the area of virtual social network design since 2008. Currently, he is the Chief Scientist at Tellagence Corporation, a social media analytics firm. He received his training in communication engineering in India, and holds an MS in Technology Management from Portland State University, where he is pursuing his doctorate in the same field. • Charles Weber received (among other degrees) a B.S. degree in Engineering Physics from the University of Colorado, Boulder; an M.S. degree in Electrical Engineering from the University of

California, Davis; and a Ph.D. in Management from MIT's Sloan School of Management. He joined Hewlett-Packard Company as a process engineer in an IC manufacturing facility. He subsequently transferred to HP’s IC process development center, working in

electron beam lithography, parametric testing, microelectronic test structures, clean room layout, and yield management. From 1996 to 1998, Charles managed the defect detection project at SEMATECH as an HP assignee. In December 2002, he joined the faculty of Portland State University where he is an associate professor of engineering and technology management. In the winter semester of 2014, he held the Fulbright/Kathryn and Craig Hall Distinguished Chair for Entrepreneurship in Central Europe at the Vienna University of Economics and Business.

17 Mayande & Weber – OUI 2014

Mayande & Weber -- OUI 2014 7/25/2014

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References (1)

Allen, T. R. (1977). Managing the Flow of Technology. Cambridge, Mass., USA: MIT Press.

Bonacich, P. (1972). Factoring and weighting approaches to clique identification. Journal of Mathematical Sociology, 2, 113-120.

Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27, 55-71.

Burt, R. S. (1992). Structural Holes: The Social Structure of Competition, Cambridge, Mass, USA: Harvard University Press.

Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95-S120.

Franke, N. and Shah, S. (2003). How communities support innovative activities: An exploration. Research Policy, 32, 157–178.

Freeman, L.C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35-41. Freeman, L.C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-239. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360-1380.

Lakhani, K. R. and von Hippel, E. (2003). How open source software works: “Free” user-to-user assistance. Research Policy, 32, 923–943.

Mayande, N. and Jetter, A. (2010). Business models, governance structures and network structures in virtual communities. 8th Workshop on Open and User Innovation, Boston, MA, USA, Aug. 1-2, 2010.

Mayande, N., Weber, C. M. and Jetter, A., “A novel approach to analyzing online user innovation networks” Mass Customization and Personalization Conference, San Francisco, California, USA, November 19, 2011.

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References (2)

Mayande, N., and Weber, C. M. “Structure, information flow and synchronized innovation in online open innovation communities,” 10th Workshop on Open and User Innovation, Boston, MA, USA,

July 29-August 1, 2012.

Podolny, J. M. (1993). A status-based model of market competition. American Journal of Sociology, 98, 829–872.

Powell, W.W. (1990). Neither market nor hierarchy: Network forms of organization.In Barry M. Staw and L.L Cummings (eds.), Research in Organization Behaviour, 12, 295-336.

Putnam, R. D. 1995. Bowling alone: America's declining social capital. Journal of Democracy, 6(1), 65-78.

Von Hippel, E. (2001). Innovation by user communities: Learning from open-source software. MIT Sloan Management Review, 42(4), 82-86.

Von Hippel, Eric (2002). Open source projects as horizontal innovation networks - by and for users, Working Paper 4366-02, MIT Sloan School of Management, June 2002.

Wasserman, S. and Faust, K., (1994). Social Network Analysis: Methods and Applications, New York, NY, USA, Cambridge University Press.

Wellman, B., Boase, J. and Chen, W. (2002). The networked nature of community: On and off the Internet. IT and Society, 1(1), 151-165 (http://www.ITandSociety.org).

West, J. and Lakhani, K. R. (2008). Getting clear about communities in open innovation, Industry and Innovation, 15(2), 223-231.

Mayande & Weber -- OUI 2014 19 7/25/2014

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Online User Communities

Featured prominently in the OUI literature

(e.g., von Hippel, 2001; Lakhani and von Hippel, 2003; Franke and

Shah, 2003; West and Lakhani, 2008).

“… interpersonal ties that provide sociability, support,

information, a sense of belonging, and social identity.”

(Wellman, 2002, p. 154)

Special case of

online user networks

,

which have been defined as “user nodes interconnected by

information transfer links”

(von Hippel, 2002, p. 1)

.

that do not necessarily exhibit all of the abovementioned

attributes of online user communities.

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Extant Theory (continued.)

Extant theory does not show how influence within a

network can depend upon control of information flow.

In principle, every person within a social network can

decide whether or not information should be passed on

and who should receive it.

Yet, existing methods for social network analysis do not

take these preferential attachments into consideration.

They may consequently ascribe a degree of influence to a

manager that may not reflect his/her actual influence,

potentially causing managers to choose incorrect channels

to get things done.”

(Mayande, Weber & Jetter, 2011, p. 1)

Mayande & Weber -- OUI 2014 21 7/25/2014

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MEASURING INFLUENCE AND

INFORMATION FLOW

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Various Types of Information Flow

Distinguished by two properties:

the routes through which the traffic flows

and the method by which the flows are propagated.

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Serial Duplication

Path

Any possible way you can get from one node to another

Paths emphasize the impact information spread and exposure from

multiple sources.

Geodesic

Shortest possible path

Geodesics emphasize speed at which information spreads.

Serial duplication Geodesics Information Spread

Paths Information Spread and

References

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