Computational Perspective on Social Phenomena in On-Line Networks
Web is metaphors of library and Crowd
Core Question Combining content and structure
- What features of a message help predict its level of penetration
- How can network structure help untderstanding contents?
Quoted Phrases The 2008 election news cycle
- many phrases
Why do certain quotes stand out?
Movie Quotes as Viral Text
Algorithm Recognition of Memorability
- Less probable in their word choice
- Compare to a base language model trained on newswire
- Not just individual word, but consecutive 2- and 3-word sequence
- E.g. "You had me at hello"
- But more probable in their part-of-speech composition
-" You had me at hello" has same part-of-speech sequence as "I met him in boston".
- A memorable quote : a sequence of unusual words built on a scaffolding of common part-of-speech paterns
Connective Media and Social Feedback Effect
IN aggregate memorable quotes are more general
- suggest a certain probability to the quote
mutation of textual mems as thery trabel from source to source
genetic analogy for mems beginning of a formalization
- fitness function
- mutation mechanism and functional element
- population structure
Decisions in SocialMedia
interface with the network plays a role in your decisions
- User-defined groups
participate in online collaborative project
decision to use Twitter Hashtag
Click on a product ad endorsed
Diffusion CurveLong standing framework
proability of adopting a behavior depends on number of network neighbors already adopting
Key issue; qualitative shape of the curves
Diminishing returns? Critical Mass?
Predicgtion and Potential Influence
Likly to do something when more friends are doing it, Why?
homophily / selection vs influence
Dependence on number of friends :
a first step toward general prediction
- Given the full pattern of connections among your friends, estimate probability of adopting a new behavior
- using mot-if
Appilcation to cnoversational curation
Many sites organized around evolvind discussion threads
Basic problem : Algorithmic curation
Key sub-problem : Length prediction
Final Reflections: Toward a model of You
Not only massive population, data of indiviidually
Software that understands your behavior better then you do
- Ex:How rapidly do you reply to email?
The web is powered by feedback loop between people and information