robin dunbar "has the internet changed our social world?"
DESCRIPTION
Presentation to 26th HBES Workshop Natal, 30 July 2014 - Internet Science official JRA6 workshop.TRANSCRIPT
Has the Internet Changed Has the Internet Changed Our Social World?Our Social World?
Robin Dunbar
The Global Village?
The Internet was based on the promise of enlarging your social world beyond the
limits of the local village
But has it actually worked?
Social Brain Hypothesis
• Predicted group size for humans is ~150
• “Dunbar’s Number”
Monkeys
Apes
Neocortex volume divided by rest of brain
The Natural Size of Human
Communities?
These all have mean sizes of 100-200
Neolithic villages 6500 BC 150-200 Modern armies (company) 180Hutterite communities 107‘Nebraska’ Amish parishes 113business organisation <200ideal church congregations <200Domesday Book villages [1087 AD] 150C18th English villages 160GoreTex Inc’s structure 150Research sub-disciplines 100-200
Small world experiments 134Hunter-Gatherer communities 148Xmas card networks 154
Maximum Network Size
350-374
325-349
300-324
275-299
250-274
225-249
200-224
175-199
150-174
125-149
100-124
75-99
50-74
25-49
0-24
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of
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ses
10
9
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7
6
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“Reverse”Small World Experiments
Hunter-Gatherer Societies
Xmas Card Networks
Individual Tribes
HumanSocial Groups
These all have mean sizes of These all have mean sizes of 100-200100-200
Neolithic villages 6500 BC 150-200 Modern armies (company) 180Hutterite communities 107‘Nebraska’ Amish parishes 113business organisation <200ideal church congregations <200Doomsday Book villages 150C18th English villages 160GoreTex Inc’s structure 150Research sub-disciplines 100-200
Small world experiments 134Hunter-Gatherer communities 148Xmas card networks 154
Maximum Network Size
350-374
325-349
300-324
275-299
250-274
225-249
200-224
175-199
150-174
125-149
100-124
75-99
50-74
25-49
0-24
Nu
mb
er
of
Ca
ses
10
9
8
7
6
5
4
3
2
1
0
1
10
100
1000
10000
0 10 20 30
“Reverse”Small World Experiments
Killworth et al (1984)
Hunter-Gatherer Societies
Dunbar (1993)
Xmas Card NetworksHill & Dunbar
(2003)
Individual Tribes
Her 152 friends recorded for posterity…..?
http://www.youtube.com/watch?v=ApOWWb7Mqdo
Luckily, it’s a hoax….It was an advertising stunt!
Is Your Online Network Bigger than ~150?
• Network size estimated from reciprocated exchanges
• # edges drops off after ~200
Gonzalez et al. (2011:PLoS-1) Haeter et al (2012: Phys. Rev. Letts)
Twitter Email
Has Facebook Really Widened Your Social World?• It seems not….
• Modal number of ‘friends’ on Facebook = 150-250
• You may list 100s of friends, but you only talk to a handful
N 1 million Facebook users
BUT….our friends are NOT all the same!
Our social world is less like this
…..and more like this
Intimacy, Frequency and Trust
• Relationship between frequency of contact and intimacy
• Trust and obligation seem to be important
Emotional Closeness
109876543210
Me
an
Tim
e S
ince
La
st C
on
tact
(M
on
ths)
8
6
4
2
0
LOW Emotional Closeness HIGH
The Fractal Periodicity of Human Group Sizes
Peak at=5.4
Peak at=5.2
Xmas Card Database
Social Groupings Database [N=60]
Scaling ratio = exp(2π/) = 3.2 and 3.3
Zhou, Sornette, Hill & Dunbar (2005)
Sizes of Hunter-Gatherer Groupings
Hamilton et al (2007)
The Expanding Circles
Our relationships form a hierarchically inclusive
series of circles of increasing size
but decreasing intensity [ie quality of relationship]
We know all these layers exist
…and the military maintain the sequence far
beyond [to ~50,000]
5
15
50
150
Intensity
EGO
500 1500
The Military Model
Modern Army Organisation USA Australia
[1994] [2010]
Section 10 12Platoon 30 45Company 126 168Battalion 650 775Brigade/Regiment 4000 3750Division 12,500 15,000
War of Spanish Succession [1701-1714]
The need to solve two conflicting requirements:
Maximising cohesion and the number of boots-on-the-ground
Network Structure on Facebook
• Facebook regional network in April 2008
• 3M nodes with 23M edges [useable dataset: 92,300 nodes]
• Density-based clustering: Optimal cluster structure is
4 layers
• Layer sizes correspond exactly to those found by Zhou et al. (2005) in F2F networks….with a scaling ratio of ~3….AND an added layer at 1.5
Cumulative size: 1.6 5.7 17.6 52.2Predicted size: (1.5) 5 15 50
Arnaboldi et al. (2012)
Optimal Cluster #
Support Sympathy Affinity ?? clique group group
Network Structure on Twitter
• 205,000 human Twitter followers, 200M tweets
• Reciprocated postings
• Optimal # clusters = 4• Layers have same scaling ratio
[~3) and sizes virtually identical to theoretical layers
Facebook: 1.6 5.7 17.6 52.2
Theoretical: 1.5 5 15 50
The Expanding Circles … as they really are
• It turns out, as predicted, that there really is an inner-inner layer at 1.5
• …perhaps because girls can have two intimate relationships (a best girlfriend PLUS a boyfriend)
….but boys can only manage one (a girlfriend or nothing)?
5
15
50
150
1.5
Social Bonding Primate-Style
Primate social bonds seem to involve two distinct components:
An emotionally intense component
[= grooming endorphins]
A cognitive component [=brain size + cognition]
• Best predictor of network size is orbitofrontal prefrontal cortex volume
• In a fine-grained VBM (voxel) analysis: best predictor of network size is ventromedial PFC
• 2 of 7 neuroimaging studies showing correlations between brain region volume and network size in humans and macaques
Friendship on the Brain?
Importance of Time
Kin
0 9 18months
Friends
Change in Emotional Closeness Daily contact rates per person
Friendships decline rapidly in the absence of contact
Time really is a Network Constraint
• Mobile phone dataset from 11 months [20M users and 9 billion calls]
• As network size [k] gets larger, o mean call rate asymptotes at ~200 o call diversity declines after a peak at
k≈15
Total calling is time is limited, and gets distributed more thinly
• There is a natural limit to network size, and it is set [in part] by how thinly social capital can be invested
Miritello et al. (2013)
Just how consistent are these patterns?
An 18-month longitudinal study of 30 18-year-olds transitioning to University
….for whom we have complete call + text records and detailed relationship
questionnaires (at start, mid and end)
Roberts et al (2009), Roberts & Dunbar (2010a,b)
Stability of Social Signatures
• Alters ranked by frequency of calls
• Ranking pattern remains similar across all three 6-mnth windowsDESPITE high turnover in in membership in successive 6-month windows [esp. in first interval as indicated by low Jaccard index – indexes similarity]
• 25% to top Alter 48% to top 3 Alters
Saramaki et al. (2014)
Stability of
Social Signatures
• Comparison between 3 intervals
• Individual signatures are significantly more similar over time [dself] than they are to other individuals’ signatures [dref]
• Picture is identical using Emotional Closeness, duration of calls and # texts
Saramaki et al. (2014)
Ego 1
Ego 2
Three Ways We Solved the
Bonding Problem
Millions Years BP
3.53.02.52.01.51.0.50.0-.5
Pre
dict
ed G
room
ing
Tim
e (%
)
50
40
30
20
10
Laughter a cross-cultural trait
shared with chimpanzees
Music and dance
Religion and its rituals
Australopiths
Modern humans
H. erectus
Archaic humans\
Something in the Way She Moves….?
• A study carried out in Brazil with very simple dance moves
Change in Pain Threshold
Self-in-Other Index
Conclusions
• Human social networks are constrained by (1) cognition and (2) time
• The internet has increased the distance over which we can contact network members….
• BUT it has not increased the size or structure of our networks
• The real limitations [now] are:
o Lack of face-to-face interactiono Absence of endorphin-based bonding mechanisms
Thanks ….!