Social Dynamics in the Age
of the Web
Bernardo A. Huberman
HP Labs
© 2007 Hewlett-Packard Development Company, L.P.
The information contained herein is subject to change without notice
social dynamics
Bruegel, Peter the Younger. Village Feast
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traditional methods accurate but laborious
facebook
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•
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a massive social
network
used by millions of
students and
enterprise employees
our analysis
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4.2 million users from 500 schools
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284 million messages, 79 million “pokes”
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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.
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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.
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temporal rhythms
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attention
attention
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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
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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)
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2. if a user can only attend to a finite number of items in a given time interval,
what should be presented up front?
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information poor environments
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no problem
in information-rich environments, the scarce
resource is attention
very valuable, ephemeral, and hard to obtain
big problem
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getting the attention of a group
broadcasting
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getting the attention of a group
virally
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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 amazon.com
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does receiving more recommendations
increases the likelihood of buying?
DVDs
BOOKS
0.08
0.06
Probability of Buying
Probability of Buying
0.05
0.04
0.03
0.02
0.06
0.04
0.02
0.01
0
2
4
6
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Incoming Recommendations
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0
10
20
30
40
50
Incoming Recommendations
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J. Lefkovec, L. Adamic and B. A. Huberman,
ACMTransactions on the Web, Vol. 1, 5 (2007)
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attention - the temporal dimension
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we share with others what captures our attention
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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
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digg.com: user generated news
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we studied 1 million users of digg.com
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
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the decay of novelty:
Nt  (1  r (t )t ) Nt 1
r (t ) ~ exp[ 0.4t 0.4 ]
story “half life”: 69 minutes
F. Wu and B. Huberman, “Novelty and collective attention,” Proc. Natl. Acad.
USA, Vol.105, 17599 (2007)
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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?
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opinions
attention
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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?
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a paradox
why do people bother to post opinions? (millions do)
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)
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)
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1. no
history
average essembly.com voting, EXn, as a function of the
number of votes, n.
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2.
history available - costless to vote
sample average fraction of positive and negative votes in Jyte.com as a
function of the number of votes n. evident polarization
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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
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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.
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making a difference
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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
to learn more
http://www.hpl.hp.com/research/idl
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