In case you have any additions, comments or links that would make my notes more complete / more useful, please leave a comment and fill in the blanks.
Lada starts by telling a story about the different social networks at MIT vs. Stanford, where at MIT fraternaties are well established and play an important role in defining social communities, while at Stanford they are discouraged – each year you have to enter a room lottery that determines with whom you gonna live with in the coming year. This difference can be observed in the social networks among students. But analyzing the relationships between people, and the actions they perform is challenging because of the difficulty of correlation vs. causation. Do two friends buy the same item because they have a social relationship (causation) or do they happen to buy the same item independent of their relation (correlation)?
The Social Hyperlink (how intent spreads through Second Life):
That’s why Lada got interested in Second Life, as in SL it is possible to trace how information (e.g. dance moves, items) spreads along social ties. In many cases, SL maintains information about previous item owners, allowing us to study how items propagate through networks of SL users. The example Lada talked about was gesture transfer among users of second life. Lada presented results from a study analyzing 12.6 mio transfers (where 23% have accurate previous owner info). What you can do with this data is investigate patterns of information spread through the social network.
- 48% of transfers happen between friends.
- Cascades among friends are deeper / items are passed along social ties more often (higher precentage of non-leaf nodes)
- But: adoption over time is weaker in social networks. Lada speculates that a reason for that is that information spread among friends is “niche” information (only relevant to a small group of homogeneous friends)
The next question Lada deals with is whether targeting hubs/early adopters would be a promising strategy to spread information in networks, by dividing the network into early adopters and laggards:
- early adopters (or Mavens in Gladwell’s terms) were less social (fewer friends than the average)
- they were also not active in distributing assests, that means that they are not influencers
- social networks influences adoption
- niche items get a bigger boost (from social relations)
- some individuals have more influence than others
User Intent and Social Networks: What I find interesting about this work, particularly the Second Life Case, is that it allows us to study the propagation of intent in social networks. This kind of data enables us to examine how social relations influence what people want. I find this to be an important research question, because intent is generally assumed to be an attribute of individuals rather than a characteristic of social networks as a whole. I think that people tend to prefer believing that their goals are individual and intrinsic, rather than determined(?) by their social network. Studies such as the SL study have the potential to explore this question empirically.
But network analysis can be employed for other aspects of links as well, Lada gives two more examples:
The Knowledge-Exchange Hyperlink:
One of the questions Lada talked about in this context was: What motivates users to answer questions?
From Interviews from Naver: altruism, learning, hobby, business, points
From crawls: filling in the blanks, correcting others
The Trust Hyperlink:
Lada got interested in Couchsurfing as a way to study trust in social networks. (The rationale being that trust is required to let somebody stay in your home.)
The study included 600.000 users, 156.000 surfed or hosted. 55.000 in largest, strongly connected component
Observerations: Overtime, people tend to engage in both surfing and hosting.
Results: direct reciprocity only accounts for 12-18% (surf the couch of the person you have hosted). Generalized reciprocity is at place.People are willing to vouch for people they only knew via couch-surfing. They tend to vouch for fewer couch-surfing friends than best friends, but overall there are more couch-surfing friends.
I really enjoyed Lada’s keynote, I think the keynote did a great job in motivating and illustrating the potential of network analysis to explore different aspects of linked information on the web. I came across her work many times before in my own research and I’m happy to have had the chance to hear her talk in person.
Next up are my students Christian and Mark who are pitching their posters on “Understanding the Motivation behind Tagging” (Christian Körner) and “Towards Automatically Annotating Textual Resources with Human Intent” (Mark Kröll). Good luck!
Update (Jul 4 2009): Lada’s slides of the talk are available online!