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A 61-Million-Person Experiment in Social Influence and Political Mobilization

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Abstract

Human behaviour is thought to spread through face-to-face social networks, but it is difficult to identify social influence effects in observational studies, and it is unknown whether online social networks operate in the same way. Here we report results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users' friends, and friends of friends. The effect of social transmission on real-world voting was greater than the direct effect of the messages themselves, and nearly all the transmission occurred between 'close friends' who were more likely to have a face-to-face relationship. These results suggest that strong ties are instrumental for spreading both online and real-world behaviour in human social networks.
LETTER doi:10.1038/nature11421
A 61-million-person experiment in social influence
and political mobilization
Robert M. Bond
1
, Christopher J. Fariss
1
, Jason J. Jones
2
,AdamD.I.Kramer
3
, Cameron Marlow
3
, Jaime E. Settle
1
& James H. Fowler
1,4
Human behaviour is thought to spread through face-to-face social
networks, but it is difficult to identify social influence effects in
observational studies
9–13
, and it is unknown whether online social
networks operate in the same way
14–19
. Here we report results from
a randomized controlled trial of political mobilization messages
delivered to 61 million Facebook users during the 2010 US con-
gressional elections. The results show that the messages directly
influenced political self-expression, information seeking and real-
world voting behaviour of millions of people. Furthermore, the
messages not only influenced the users who received them but also
the users’ friends, and friends of friends. The effect of social trans-
mission on real-world voting was greater than the direct effect of
the messages themselves, and nearly all the transmission occurred
between ‘close friends’ who were more likely to have a face-to-face
relationship. These results suggest that strong ties are instrumental
for spreading both online and real-world behaviour in human
social networks.
Recent experimental studies
6,14–16
have attempted to measure the
causal effect of social influence online. At the same time, there is
increasing interest in the ability to use online social networks to study
and influence real-world behaviour
17–19
. However, online social networks
are also made up of many ‘weak-tie’ relationships
20
that may not
facilitate social influence
21
, and some studies suggest that online
communication may not be an effective medium for influence
22
.An
open question is whether online networks, which harness social
information from face-to-face networks, can be used effectively to
increase the likelihood of behaviour change and social contagion.
One behaviour that has been proposed to spread through networks
is the act of voting in national elections. Voter turnout is significantly
correlated among friends, family members and co-workers in obser-
vational studies
23,24
. Voter mobilization efforts are effective at increas-
ing turnout
25
, particularly those conducted face-to-face and those that
appeal to social pressure
26
and social identity
27
. There is also evidence
from one face-to-face field experiment that voting is ‘contagious’, in
the sense that mobilization can spread from person to person within
two-person households
28
. Although anecdotal accounts suggest that
online mobilization has made a big difference in recent elections
21
,a
meta-analysis of email experiments suggests that online appeals to vote
are ineffective
24
.
Voter mobilization experiments
26–28
have shown that most methods
of contacting potential voters have small effects (if any) on turnout
rates, ranging from 1% to 10%. However, the ability to reach large
populations online means that even small effects could yield behaviour
changes for millions of people. Furthermore, as many elections are
competitive, these changes could affect electoral outcomes. For
example, in the 2000 US presidential election, George Bush beat Al
Gore in Florida by 537 votes (less than 0.01% of votes cast in Florida).
Had Gore won Florida, he would have won the election.
To test the hypothesis that political behaviour can spread through
an online social network, we conducted a randomized controlled trial
with all users of at least 18 years of age in the United States who
accessed the Facebook website on 2 November 2010, the day of the
US congressional elections. Users were randomly assigned to a ‘social
message’ group, an ‘informational message’ group or a control group.
The social message group (n560,055,176) was shown a statement at
the top of their ‘News Feed’. This message encouraged the user to vote,
provided a link to find local polling places, showed a clickable button
reading ‘I Voted’, showed a counter indicating how many other
Facebook users had previously reported voting, and displayed up to
six small randomly selected ‘profile pictures’ of the user’s Facebook
friends who had already clicked the I Voted button (Fig. 1). The
informational message group (n5611,044) was shown the message,
poll information, counter and button, but they were not shown any
faces of friends. The control group (n5613,096) did not receive any
message at the top of their News Feed.
The design of the experiment allowed us to assess the impact that the
treatments had on three user actions; clicking the I Voted button,
clicking the polling-place link and voting in the election. Clicking
the I Voted button is similar to traditional measures of self-reported
voting, but here users reported their vote to their social community
rather than to a researcher. We therefore use this action to measure
political self-expression, as it is likely to be affected by the extent to
which a user desires to be seen as a voter by others. In contrast, social
desirability should not affect other user actions in the same way.
Clicking the polling-place link took users to a separate website that
helped them to find a polling location, and this action was not reported
to the user’s social community. We therefore use this action to measure
a user’s desire to seek information about the election. Finally, we used
a group-level process to study the validated voting behaviour of
6.3 million users matched to publicly available voter records (see
Supplementary Information).
We first analyse direct effects. We cannot compare the treatment
groups with the control group to assess the effect of the treatment on
self-expression and information seeking, because the control group did
not have the option to click an I Voted button or click on a polling-
place link. However, we can compare the proportion of users between
the two treatment groups to estimate the causal effect of seeing the
faces of friends who have identified themselves as voters (Fig. 1). Users
who received the social message were 2.08% (s.e.m., 0.05%; t-test,
P,0.01) more likely to click on the I Voted button than those who
received the informational message (20.04% in the social message
group versus 17.96% in the informational message group). Users
who received the social message were also 0.26% (s.e.m., 0.02%;
P,0.01) more likely to click the polling-place information link than
users who received the informational message (Fig. 1).
Although acts of political self-expression and information seeking
are important in their own right, they do not necessarily guarantee that
a particular user will actually vote. As such, we also measured the effect
that the experimental treatment had on validated voting, through
examination of public voting records. The results show that users
1
Political Science Department, University of California, San Diego, La Jolla, California 92093, USA.
2
Psychology Department, University of California, San Diego, La Jolla, California 92093, USA.
3
Data
Science, Facebook, Inc., Menlo Park, California 94025, USA.
4
Medical Genetics Division, University of California, San Diego, La Jolla, California 92093, USA.
13 SEPTEMBER 2012 | VOL 489 | NATURE | 295
Macmillan Publishers Limited. All rights reserved
©2012
who received the social message were 0.39% (s.e.m., 0.17%; t-test,
P50.02) more likely to vote than users who received no message at
all. Similarly, the difference in voting between those who received the
social message and those who received the informational messagewas
0.39% (s.e.m., 0.17%; t-test, P50.02), suggesting that seeing faces of
friends significantly contributed to the overall effect of the message on
real-world voting. In fact, turnout among those who received the
informational message was identical to turnout among those in the
control group (treatment effect 0.00%, s.e.m., 0.28%; P50.98), which
raises doubts about the effectiveness of information-only appeals to
vote in this context.
These results show that online political mobilization can have a
direct effect on political self-expression, information seeking and
real-world voting behaviour, and that messages including cues from
an individual’s social network are more effective than information-
only appeals. But what about indirect effects that spread from person
to person in the social network? Users in our sample had on average
149 Facebook friends, with whom they share social information,
although many of these relationships constitute ‘weak ties’. Past
research indicates that close friends have a stronger behavioural effect
on each other than do acquaintancesor strangers
9,11,13,21
. We therefore
expected mobilization to spread more effectively online through
‘strong ties’.
To distinguish users who are likely to have close relationships, we
used the degree to which Facebook friends interacted with each other on
the site (see Supplementary Information for more detail). Higher levels
of interaction indicate that friends are more likely to be physically
proximate and suggest a higher level of commitment to the friendship,
more positive affect between the friends, and a desire for the friendship
to be socially recognized
29
. We counted the number of interactions
between each pair of friends and categorized them by decile, ranking
them from the lowest to highest percentage of interactions. A validation
study (see SupplementaryInformation)shows that friends in the highest
decile are those most likely to be close friends in real life (Fig. 2a).
We then used these categories to estimate the effect of the mobil-
ization message on a user’s friends. Random assignment means that
any relationship between the message a user receives and a friend’s
behaviour is not due to shared attributes, as these attributes are not
correlated with the treatment (see Supplementary Information). To
measure a per-friend treatment effect, we compared behaviour in the
friends connected to a user who received the social message to beha-
viour in the friends connected to a user in the control group. To
account for dependencies in the network, we simulate the null distri-
bution using a network permutation method (see the Supplementary
Information). Monte Carlo simulations suggest that this method
minimizes the risk of false positives and recovers true causal effects
without bias (see Supplementary Information).
Figure 2 shows that the observed per-friend treatment effects increase
as tie-strength increases. All of the observed treatment effectsfall outside
the null distribution for expressed vote (Fig. 2b), suggesting that they are
significantly different from chance outcomes. For validated vote
(Fig. 2c), the observed treatment effect is near zero for weak ties, but
it spikes upwards and falls outside the null distribution for the top two
deciles. This suggests that strong ties are important for the spread of
real-world voting behaviour. Finally, the treatment effect for polling
place search gradually increases (Fig. 2d), with several of the effects
falling outside the 95% confidence interval of the null distribution.
To simplify the analysis and reporting of results, we arbitrarily
define ‘close friends’ as people who were in the eightieth percentile
or higher (decile 9) of frequency of interaction among all friendships in
the sample (see the Supplementary Information). ‘Friends’ are all other
Facebook friends who had less interaction. A total of 60,491,898 (98%)
users in our sample had at least 1 close friend, with the average user
having about 10 close friends (compared with an average of 139 friends
who were not close).
The results suggest that users were about 0.011% (95% confidence
interval (CI) of null distribution 20.009% to 0.010%) more likely to
engage in an act of political self-expression by clicking on the I Voted
button than they would have been had their friend seen no message.
Similarly, for each close friend who received the social message, an
individual was on average 0.099% (null 95% CI –0.042% to 0.048%)
more likely to express voting.
We also found an effect in the validated vote sample. For each close
friend who received thesocial message, a user was 0.224% (null 95% CI
–0.181% to 0.174%) more likely to vote than they would have been had
their close friend received no message. Similarly, for information-
seeking behaviour we found that for each close friend who received
the social message, a user was 0.012% (null 95% CI –0.012% to 0.012%)
more likely to click the link to find their polling place than they would
have been had their close friends received no message. In both cases
there was no evidence that other friends had an effect (see
Supplementary Information). Thus, ordinary Facebook friends may
affect online expressive behaviour, but they do not seem to affect
private or real-world political behaviours. In contrast, close friends
seem to have influenced all three.
The magnitude of these contagion effects are small per friend, but it
is important to remember that they resultfrom a single message, and in
many cases it was not possible to change the target’s behaviour. For
example, users may have already voted by absentee ballot before
Election Day, or they may have logged in to Facebook too late to vote
or to influence other users’ voting behaviour. In other words, all effects
measured here are intent-to-treat effects rather than treatment-on-
treated effects, which would be greater if we had better information
about who was eligible to receive the treatment.
ab
Informational message
Social message
friends have voted.
Today is Election Day What’s this?
People on Facebook Voted
Find your polling place on the U.S.
Politics Page and click the "I Voted"
button to tell your friends you voted.
close
VOTE l Voted
10 155376
Today is Election Day What’s this?
People on Facebook Voted
Find your polling place on the U.S.
Politics Page and click the "I Voted"
button to tell your friends you voted.
close
VOTE l Voted
10 155376
0
0.3
0.6
0.9
1.2
1.5
1.8
2.1
Direct effect of treatment
on own behaviour (%)
Self-
reported
voting
Search for
polling
place
Validated
voting
Validated
voting
Social
message
versus
control
Social
message
versus
informational
message
Jaime Settle, Jason Jones, and 18 other
Figure 1
|
The experiment and direct effects. a,b, Examples of the informational message and social message Facebook treatments (a) and their direct effect on
voting behaviour (b). Vertical lines indicate s.e.m. (they are too small to be seen for the first two bars).
RESEARCH LETTER
296 | NATURE | VOL 489 | 13 SEPTEMBER 2012
Macmillan Publishers Limited. All rights reserved
©2012
Moreover, the scale of the number of users, their friendship
connections and the potential voters in a given election is very large.
We estimated the per-user effect (the per-friend effect multiplied by
the average number of friends per user) and the total effect (the
per-user effect multiplied by the total number of users) on the
behaviour of everyone in the sample (see Supplementary Informa-
tion). The results suggest that friends generated an additional
886,000 expressed votes (11.4%, null 95% CI 21.1% to 1.1%),
and close friends generated a further 559,000 votes (10.9%, null
95% CI –0.3% to 0.3%). In the Supplementary Information we also
show that close friends of close friends (2 degrees of separation)
generated an additional 1 million expressed votes (11.7%, null 95%
CI –0.8% to 0.9%). Thus, the treatment clearly had a significant impact
on political self-expression and how it spread through the network,
and even weak ties seem to be relevant to its spread.
However, the effect of the social message on real-world validated
vote behaviour and polling-place search was more focused. The results
suggest that close friends generated an additional 282,000 validated
votes (11.8%, null 95% CI –1.3% to 1.2%) and an additional 74,000
polling-place searches (10.1%, null 95% CI –0.1% to 0.1%), but there
is no evidence that ordinary friends had any effect on either of these
two behaviours. In other words, close friendships accounted for all of
the significant contagion of these behaviours, in spite of the fact that
they make up only 7% of all friendships on Facebook.
To put these results in context, it is important to note that turnout
has been steadily increasing in recent US midterm elections, from
36.3% of the voting age population in 2002 to 37.2% in 2006, and to
37.8% in 2010. Our results suggest that the Facebook social message
increased turnout directly by about 60,000 voters and indirectly
through social contagion by another 280,000 voters, for a total of
340,000 additional votes. That represents about 0.14% of the voting
age population of about 236 million in 2010. However, this estimate
does not include the effect of the treatment on Facebook users who
were registered to vote but who we could not match because of
nicknames, typographical errors, and so on. It would be complex to
estimate the number of users on Facebook who are in the voter record
but unmatchable, and it is not clear whether treatment effects would be
of the same magnitude for these individuals, so we restrict our estimate
to the matched group that we were able to sample and observe. This
means it is possible that more of the 0.60% growth in turnout between
2006 and 2010 might have been caused by a single message on
Facebook.
The results of this study have many implications. First and foremost,
online political mobilization works. It induces political self-expression,
but it also induces information gathering and real, validated voter
turnout. Although previous research suggested that online messages
do not work
19
, it is possible that conventional sample sizes may not
be large enough to detect the modest effect sizes shown here. We
also show that social mobilization in online networks is significantly
more effective than informational mobilization alone. Showing
familiar faces to users can dramatically improve the effectiveness of
a mobilization message.
Beyond the direct effects of online mobilization, we show the
importance of social influence for effecting behaviour change. Our
0%
2%
4%
6%
8%
10%
Probability of being the closest friend
Decile of user–friend interactions
Decile of user–friend interactions Decile of user–friend interactions
Decile of user–friend interactions
24681013579
12345678910
–0.300
–0.200
–0.100
0
0.100
0.200
0.300
Increase in probability of vote (%)
Observed value
Simulated null
95% CI
12345678910
–0.100
–0.075
–0.050
–0.025
0
0.025
0.050
0.075
0.100
Increase in probability of expressed vote (%)
Observed value
Simulated null
95% CI
12345678910
–0.020
–0.010
0
0.010
0.020
Increase in probability of
polling-place search (%)
Observed value
Simulated null
95% CI
a b
c d
Figure 2
|
The effect of mobilization treatment that a friend received on a
user’s behaviour. ad, A validation study shows that at increasing levels of
interaction, Facebook friends are more likely to have a close real-world
relationship (a; see also the Supplementary Information). As the interaction
increases, so does the observed per-friend effect of friend’streatment on a user’s
expressed voting (b), validated voting (c) and polling-place search (d). Blue
diamonds indicatethe observed treatment effect. Horizontalgrey bars show the
null distribution derived from simulations of identical networks in which the
topology and incidence of the behaviour and treatment are the same but the
assignments of treatment are randomly reassigned.
LETTER RESEARCH
13 SEPTEMBER 2012 | VOL 489 | NATURE | 297
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©2012
validation study shows that close friends exerted about four times
more influence on the total numberof validated voters mobilized than
the message itself. These results are similar to those from a prior
network simulation study based on observational data that suggested
each act of voting on average generates an additional three votes as this
behaviour spreads through the network
30
. Thus, efforts to influence
behaviour should pay close attention not only to the effect a message
will have on those who receive it but also to the likelihood that the
message and the behaviour it spurs will spread from person to person
through the social network. And, in contrast to the results for close
friends, we find that Facebook friends have less effect. Online
mobilization works because it primarily spreads through strong-tie
networks that probably exist offline but have an online representa-
tion. In fact, it is plausible that unobserved face-to-face interactions
account for at least some of the social influencethat we observed in this
experiment.
More broadly, the results suggest that online messages might
influence a variety of offline behaviours, and this has implications
for our understanding of the role of online social media in society.
Experiments are expensive and have limited external validity, but the
growing availability of cheap and large-scale online social network
data
17
means that these experiments can be easily conducted in the
field. If we want to truly understand—and improve—our society,
wellbeing and the world around us, it will be important to use these
methods to identify which real world behaviours are amenable to
online interventions.
Received 17 April; accepted 18 July 2012.
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Supplementary Information is available in the online version of the paper.
Acknowledgements We are grateful to S. Aral, J. Berger, M. Cebrian, D. Centola,
N. Christakis, C. Dawes, L. Gee, D. Green, C. Kam, P. Loewen, P. Mucha, J. P. Onnela,
M. Porter, O. Smirnov and C. Volden for comments on early drafts. This work was
supported in part by the James S. McDonnell Foundation, and the University of Notre
Dame and the John Templeton Foundation as part of the Science of Generosity
Initiative.
Author Contributions All authors contributed to study design, data collection, analysis
and preparation of the manuscript. J.H.F. secured funding.
Author Information Reprints and permissions information is available at
www.nature.com/reprints. The authors declare no competing financial interests.
Readers are welcome to comment on theonline version of the paper. Correspondence
and requests for materials should be addressed to J.H.F. (jhfowler@ucsd.edu).
RESEARCH LETTER
298 | NATURE | VOL 489 | 13 SEPTEMBER 2012
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©2012
... Politics have already made intense use of social media for election campaigns. Such posts can influence real-life voting behavior [87]. Thus, political elections could be increasingly determined by social media or more concisely: P28. ...
... Bond et al. state that social media influence, through peer pressure, encourages other users to vote [87]. Their experiment points out that social media users, who viewed a photo and message of their friends voting, were more likely to vote themselves than users, who only received an informational message without a photo of their friends. ...
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Thesis
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Users are increasingly interacting with machine learning (ML)-based curation systems. YouTube and Facebook, two of the most visited websites worldwide, utilize such systems to curate content for billions of users. Contemporary challenges such as fake news, filter bubbles, and biased predictions make the understanding of ML-based curation systems an important and timely concern. Despite their political, social, and cultural importance, practitioners' framing of machine learning and users' understanding of ML-based curation systems have not been investigated systematically. This is problematic since machine learning - as a novel programming paradigm in which a mapping between input and output is inferred from data - poses a variety of open research questions regarding users' understanding. The first part of this thesis provides the first in-depth investigation of ML-based curation systems as socio-technical systems. The second part of the thesis contributes recommendations on how ML-based curation systems can and should be explained and audited. The first part analyses practitioners' framing of ML by examining how the term machine learning, ML applications, and ML algorithms are framed in tutorials. The thesis also investigates the beliefs that users have about YouTube and introduces a user belief framework of ML-based curation systems. Furthermore, it demonstrates how limited users' capabilities for providing input data for ML-based curation systems are. The second part evaluates different explanations of ML-based systems. This evaluation uncovered an explanatory gap between what is available to explain ML-based curation systems and what users need to understand such systems. Informed by this explanatory gap, the second part of this thesis demonstrates that audits of ML systems can be an important alternative to explanations. This demonstration of audits also uncovers a popularity bias enacted by YouTube's ML-based curation system. Based on these findings, the thesis recommends performing audits to ensure that ML-based systems act in the public's interest.
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