INFORMATION EPIDEMICS AND VIRAL CONTAGION

Dmitry Paranyushkin / www.noduslabs.com

SOCIAL NETWORKThe nodes are the people, the connections are interactions

between them (visualization by Gephi - www.gephi.org)

Watch the video onvimeo.com/2035117

TYPES OF NETWORKScale-free - degrees distributed following power-law

(a few, but significant # of well-connected and disconnected)

TYPES OF NETWORKSmall world - special case of scale free – tightly-knit loosely

connected communities with short distance between the nodes

TYPES OF NETWORKRandom - degrees distributed “normally” across the nodes

(most have an average number of connections)

EPIDEMIC MODELSS: Susceptible, I: Infected, R: Removed/Recovered

(Ball 1997; Newman 2002; Newman et al 2006; Watts 2002)

S I R

S I S

S I R S

CONTAGION DYNAMICSMessage = Virus

Watch the video on vimeo.com/36958670

HOW DOES IT HAPPEN?Ideology, Trends, Collective action, Protest, Meme...

“healthy” “infected”

1. INFORMATION CASCADESHerd-like behavior, influenced by the others. Only when

“conversion threshold“ is exceeded (Watts 2002; Hui et al 2010; Young 2002)

most “friends” adopted a

trend, so the blue node does the same finally

2. GIANT COMPONENTMost nodes must belong to the same component

for the global epidemics to occur (Watts 2002; Newman et al 2006)

no connections between the nodes = cascades not possible

many connections between the nodes= cascades can occur

3. START WITH A GROUPRapid spread of disease within tightly connected communities can lead to an epidemic outbreak even if the links are loose

WHY?Because once the contagion is spread within the group, it will

spread across super-network to the other groups (Ball 1997).

STRATEGIES OF RESISTANCELeave the number of susceptibles the same in each group, thus

preventing the virus from spreading within and throughout.

Better than random nodes, but still not perfect - immunize random groups

Optimal - leave the same number of susceptibles in each group

99%? 10% IS ENOUGH.Committed 10% can change the opinion of the majority as long

as they persistently broadcast their message (Xie et al 2011)

4. BUILD SHORTCUTSScale-free networks with shortcuts are better in propagating, dense networks are better for cascades. (Kuperman 2001; Yan et al 2008)

* not too many!

THE ETHICS OF PROMISCUITYOr how to make random connections,

without driving your network crazy.

1. Amplitude of contagion increases with the higher number of random shortcuts (Cummings 2005; Kuperman 2001)

2. Small-world wirings (links between different communities) enhance network synchronization (Barahora & Pecora 2002).

3. Synchronization (simultaneous information cascades) are boosted if the links are made between the nodes of varying degree (Boccaletti 2006)

4. Assortative networks (well-connected nodes attract each other) are good in percolating (spreading the message further and maintaining the endemic contagion for a longer term period). Disassortative networks (nodes with varying degree connect together) are better in sync, but the contagion is periodic and short lived (Bragard 2007; Newman 2002)

5. FOCUS ON BROKERSThe nodes that connect different communities, are the best one

to target when spreading a message. (Stonedahl 2010; Freeman 1997)

6. MESSAGE = VIRUSThe message should have the capacity to replicate itself

across the network.

Image: CC Laura Billings @ FlickR

START WITH A QUESTION@someone “Is it true what BILD wrote that Angela Merkel

disappeared?” #weird #politics #germany #shithappens

Rumours started on Twitter during the London riots were much more long-lived when started with a query, which in turn produced statements in support and opposition of the original statement.

Watch the video on vimeo.com/33742762

RECONTEXTUALIZEAcknowledge the mindset of the target group,

but bring in some novelty.

THE SAME PURPOSEThe message should reiterate the purpose that brings

the target network together.

Against Putin Facebook group The viral message should imply “against Putin”,

not “protect animal rights”

PRACTICAL APPLICATIONSFacebook Promotion

PRACTICAL APPLICATIONSEvent Organization

SUMMARYInformation Epidemics and Viral Contagion

1. Information Cascades (people should be talking to each other)

2. Giant Component (most of the people should be connected to each other, bring the “loners” in)

3. Focus on Groups (better the more densely connected ones, 10% can be enough)

4. Make Random Shortcuts (communication outside of one’s community, diversity of links)

5. Information Brokers (people who connect different communities together)

6. Message = Virus (the message should have the capacity to replicate itself)

REFERENCES

Ball, F. (1997). Epidemics with two levels of mixing. The Annals of Applied Probability, 7(1), 46–89. Institute of Mathematical Statistics. Retrieved from http://projecteuclid.org/euclid.aoap/1034625252

Ball, F., Neal, P., & Lyne, O. (2010). Epidemics with two levels of mixing. MOdelling Complex Systems, University of Manchester. Institute of Mathematical Statistics. Retrieved from http://projecteuclid.org/euclid.aoap/1034625252

Bastian, M.; Heymann, S.; Jacomy, M.; (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. Association for the Advancement of Artificial Intelligence

Freeman, L. (1977). A Set of Measures of Centrality Based on Betweenness. SociometryVol. 40, No. 1 (Mar., 1977): 35-41

Hui, C., Goldberg, M., Magdon-Ismail, M., & Wallace, W. A. (2010). Simulating the diffusion of information: An agent-based modeling approach. International Journal of Agent Technologies and Systems (IJATS), 2(3), 31–46. IGI Global.

Kuperman, M., & Abramson, G. (2001). Small World Effect in an Epidemiological Model. Physical Review Letters, 86(13), 2909-2912.

Newman, M. E. J. (2002a). The spread of epidemic disease on networks.

Newman, M. E. J. (2002b). Assortative mixing in networks. Physical Review Letters, 89(20), 5. American Physical Society. Retrieved from http://arxiv.org/abs/cond-mat/0205405

Newman, M. E. J., Barabasi, A.-L., & Watts, D. J. (2006). The structure and dynamics of networks. Princeton University Press. doi:10.1073/pnas.0912671107

Stonedahl, F., Rand, W., & Wilensky, U. (2010). Evolving Viral Marketing Strategies. Learning.

Watts, D. (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of, 99(9), 5766-71.

Xie, J., Sreenivasan, S., Korniss, G., Zhang, W., Lim, C., & Szymanski, B. K. (2011). Social consensus through the influence of committed minorities. Physical Review E, 84(1), 1-9.

Yan, G., Fu, Z.-qian, Ren, J., & Wang, W.-xu. (2008). Collective Synchronization Induced by Epidemic Dynamics on Complex Networks with Communities. Science And Technology, 0, 3-7.

Young, H. P. (2002). The Diffusion of Innovations in Social Networks. Economy as an evolving complex system 3, 3(1966), 1-19. Oxford University Press, USA.

INFORMATION EPIDEMICS AND VIRAL CONTAGION

We used Gephi for network analysis and visualization – download it on www.gephi.org

We used NetVizz app by Bernhard Rieder to get Facebook data.

More on www.noduslabs.com - Contact: Dmitry Paranyushkin | dmitry@noduslabs.com

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