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Social Dynamics in the Age of the Web

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  1. Social Dynamics in the Age of the Web Bernardo A. Huberman HP Labs

  2. social dynamics Bruegel, Peter the Younger. Village Feast traditional methods accurate but laborious

  3. facebook • a massive social network • used by millions of students and enterprise employees

  4. our analysis • 4.2 million users from 500 schools • 284 million messages, 79 million “pokes” • 26 months of activity who do users communicate with, and when? S. Golder, D. Wilkinson and B. A. Huberman, 3rd Int’l Conference on Communities and Technologies. June 28-30, 2007.

  5. facebook Empirical analysis of real-world examples lead to the discovery of social dynamics on a massive scale, including consensus formation, collective categorization, and temporal patterns. (Facebook analysis, 362 million emails among 4.2 million users-26 months) Robust global patterns are plain when viewed in aggregate, but no individual could observe this phenomenon.

  6. temporal rhythms

  7. attention attention

  8. the economics of attention in the information age, the one scarce resource is attention very valuable, hard to obtain and rather ephemeral --- thus the intense messaging, from spam to news to advertising two big problems for content providers, news and marketing people • 1. how to present the most salient items while taking into account the visual real estate available on a given device (especially the small ones) • 2. if a user can only attend to a finite number of items in a given time interval, what should be presented up front?

  9. no problem information poor environments

  10. in information-rich environments, the scarce resource is attention very valuable, ephemeral, and hard to obtain big problem

  11. getting the attention of a group broadcasting

  12. getting the attention of a groupvirally

  13. viral marketing – the network dimension a way of drawing attention to specific products/news without broadcasting an example of information flows inside a large social network a study of15 million recommendations from

  14. does receiving more recommendationsincreases the likelihood of buying? DVDs BOOKS J. Lefkovec, L. Adamic and B. A. Huberman, ACMTransactions on the Web, Vol. 1, 5 (2007)

  15. attention - the temporal dimension • we share with others what captures our attention • as novelty fades we pay less attention and search for more question: how does novelty interact with collective attention? answer: in a highly nonlinear but predictable way

  16. user generated news

  17. we studied 1 million users of the allocation of attention among items is universal distribution of final digg numbers of 29684 stories lognormal, as predicted since attention depends on the order in which links are presented, we can dynamically reconfigure a site so as to maximize the number of hits it receives

  18. the decay of novelty: story “half life”: 69 minutes F. Wu and B. Huberman, “Novelty and collective attention,” Proc. Natl. Acad. USA, Vol.105, 17599 (2007)

  19. what follows? knowledge of the determinants of attention can be used to dynamically configure websites so as to maximize the number of hits or decide what to present so as to maximize the user’s utility moreover: an interesting tension between popularity and novelty as drivers of attention example: should most popular have higher ranking than most novel?

  20. opinions attention

  21. public opinions: an expression of the collective intelligence opinions about new products and institutions, political candidates, companies people, movies, ideas question? how do opinions form and evolve?

  22. a paradox similar to another paradox: why do people vote? • Downs (1957) Riker and Ordeshook (1968) • there is a cost to voting, and an expressive utility from doing so (Schuessler 2000) why do people bother to post opinions? (millions do) by contributing her own opinion to an existing opinion pool, a person affects the average by a marginal amoung that diminishes with the size of the pool (Wu and Huberman)

  23. 1. no history average voting, EXn, as a function of the number of votes, n.

  24. 2.history available - costless to vote sample average fraction of positive and negative votes in as a function of the number of votes n. evident polarization

  25. measuring influence consider n people having rated an item with values if the (n+1) person rates the item, the average will move to: so that the absolute change in rating will be: in large groups influence means making the numerator large

  26. 3. costly to post reviews extreme reviews  moderate reviews  average rating of 16,454 books on Amazon with more than 20 reviews. softening of views over time.

  27. making a difference average deviation of Amazon ratings. 16,454 reviews of books with more than 20 reviews. people who disagree with current opinion tend to be the ones expressing their views

  28. to learn more