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Making sense of the networked audience: The case of Facebook - Dr. Bernie Hogan is a Research Fellow at the Oxford Internet Institute Social media sites are excellent at gathering friends, but not so great at making sense of them. This leads to social information overload: too many ties, too much information and too much tedium. There is a great deal of information latent in these friendships that can be used to make sense of our networks, both spatially and relationally. Particularly through the use of social network analysis (SNA), we can discover hidden influencers and coherent clusters. This talk will give an overview of some concepts of social network analysis and demonstrate how these can be applied to online social media sites. Bernie will use as case study his ongoing fieldwork on Facebook with Microsoft Research that demonstrates mismatches between the way individuals organize their online friendships and the way that order emerges from the friendships naturally. These findings will be distilled into some general principles that can be applied to social network sites generally.
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Making sense of the networked audience: The case of Facebook
Bernie Hogan, PhDResearch Fellow, Oxford Internet Institute
University of Oxford
Local Social Summit, Latitude 51.50015, Longitude -0.12624
November 3, 2009
A preamble: Why Local?
Scale matters.
Email and IM contact by distance.
Source: Does Distance Still Matter in the Age of the Internet?
Diana Mok, Juan-Antonio Carrasco and Barry Wellman
Rural connect to Rural
Source: Network in the GardenEric Gilbert, Karrie Karahalios and Christian Sandvig
Nope.
Wellman’s Networked Individualism
Door-to-door
Place-to-place
Person-to-person
People
Mail and landlines
Email and profiles
The particular case of a peculiar age
Person-to-person networking does not undermine distance. But it makes distance secondary to specific
social relationships. Each individual has their own unique relationships. Like a thumbprint.
We live in an age of access. To be local is to be accessible.
The paradox of convenienceEveryone is somewhere, no one is everywhere
network literacy: a visual primer
CORE CONCEPTS
types
centrality
communities
network layouts
There are many types
Source: Carter Butts
Networkscan show
prominence
Generally referred to in centrality. There are
many types:
Degree - links in & outBetweenness - shortest pathsPower - high degree friendsCloseness - easily reachable
Networks clusterLiberal and Conservative Blogs
Source: Divided they BlogLada Adamic and Natalie Glance
Source: Jim Moody
Where does sociology fit?
Because you can never have too many irrelevant friends
Reasons for friends on SNS1. Actual friends 2. Acquaintances, family members, colleagues 3. It would be socially inappropriate to say no because you know them 4. Having lots of Friends makes you look popular 5. It’s a way of indicating that you are a fan (of that person, band, etc.) 6. Your list of Friends reveals who you are 7. Their Profile is cool so being Friends makes you look cool 8. Collecting Friends lets you see more people (Friendster) 9. It’s the only way to see a private Profile (MySpace) 10. Being Friends lets you see someone’s bulletins and their Friends-only blog
posts (MySpace) 11. You want them to see your bulletins, private Profile, private blog (MySpace) 12. You can use your Friends list to find someone later 13. It’s easier to say yes than no.
19
Source: HolyTaco.com
Sometimes, this is what it feels like to be on the site.
But again, scale matters.
Men with 500 friends only have mutual conversations with 10 of them. Its up to 16 for women.
That’s less than 4% of friends.
Source: Economist (via Overstated.net)
So why bother?Person-to-person networks need access controls.
Social network sites fill this niche...poorly.
Nevertheless, there’s gold in them thar’ hills.
Trace data can tell a lot
Source: Predicting tie strength with social media. Eric Gilbert, Karrie Karahalios and Christian Sandvig.
And networks can help simplify
Friends of friends are not randomly scattered, but clustered in coherent locales.
Community detection can isolate these groups.
Friend listsTedious and incoherent
Greedy CommunityLarge swaths of Sense
Eigenvector CommunityWell partitioned, but
overwhelming
Current tools to leverage networks on Facebook
• Touchgraph
• Nexus
• Friendwheel
• Mysocnet (Mine)
• http://apps.facebook.com/mynet_phaseone/
So what?
• Lurking within any social network site profile is a host of clustered peers. Discovering these groups through community detection is an effective way to bring coherence to a profile, and help it scale.
• Consider: planning a party, recommending a concert, sending out important news.
Nearness is now a social property as
much as a spatial one.This is not the same thing as collaborative
filtering. Networks do not signify similarity, they signify community. These are the people that do things together,
disclose information to each other.
Looking forwardParticular relationships create networks.
Norms of access create overload.
Thinking local is one solution, but it is partial.
We need to create contexts, so users don’t have to.
Thank YouBernie Hogan
Research Fellow, OII@blurky
bernie.hogan@oii.ox.ac.uk
;)
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