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Do online social media cut through the constraints that limit the size of offline social networks?

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The social brain hypothesis has suggested that natural social network sizes may have a characteristic size in humans. This is determined in part by cognitive constraints and in part by the time costs of servicing relationships. Online social networking offers the potential to break through the glass ceiling imposed by at least the second of these, potentially enabling us to maintain much larger social networks. This is tested using two separate UK surveys, each randomly stratified by age, gender and regional population size. The data show that the size and range of online egocentric social networks, indexed as the number of Facebook friends, is similar to that of offline face-to-face networks. For one sample, respondents also specified the number of individuals in the inner layers of their network (formally identified as support clique and sympathy group), and these were also similar in size to those observed in offline networks. This suggests that, as originally proposed by the social brain hypothesis, there is a cognitive constraint on the size of social networks that even the communication advantages of online media are unable to overcome. In practical terms, it may reflect the fact that real (as opposed to casual) relationships require at least occasional face-to-face interaction to maintain them.
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Research
Cite this article: Dunbar RIM. 2016 Do online
social media cut through the constraints that
limit the size of oine social networks? R. Soc.
open sci. 3: 150292.
http://dx.doi.org/10.1098/rsos.150292
Received: 25 June 2015
Accepted: 9 December 2015
Subject Category:
Psychology and cognitive neuroscience
Subject Areas:
psychology
Keywords:
network size, social networking sites (SNSs),
social brain hypothesis, egocentric networks
Author for correspondence:
R. I. M. Dunbar
e-mail: robin.dunbar@psy.ox.ac.uk
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rsos.150292 or via
http://rsos.royalsocietypublishing.org.
Do online social media cut
through the constraints that
limit the size of oine
social networks?
R. I. M. Dunbar
Department of Experimental Psychology, University of Oxford,S outh Parks Road,
Oxford OX1 3UD, UK
The social brain hypothesis has suggested that natural social
network sizes may have a characteristic size in humans. This
is determined in part by cognitive constraints and in part
by the time costs of servicing relationships. Online social
networking offers the potential to break through the glass
ceiling imposed by at least the second of these, potentially
enabling us to maintain much larger social networks. This is
tested using two separate UK surveys, each randomly stratified
by age, gender and regional population size. The data show
that the size and range of online egocentric social networks,
indexed as the number of Facebook friends, is similar to that of
offline face-to-face networks. For one sample, respondents also
specified the number of individuals in the inner layers of their
network (formally identified as support clique and sympathy
group), and these were also similar in size to those observed
in offline networks. This suggests that, as originally proposed
by the social brain hypothesis, there is a cognitive constraint
on the size of social networks that even the communication
advantages of online media are unable to overcome. In practical
terms, it may reflect the fact that real (as opposed to casual)
relationships require at least occasional face-to-face interaction
to maintain them.
1. Introduction
Thanks to the Internet, the past decade has witnessed a dramatic
revolution in our social world. By providing novel channels
that allow us to communicate with individuals that we would
otherwise have difficulty meeting face to face, the Internet has
made it possible to service existing relationships as well as meet
new individuals more efficiently and on a wider geographical
scale. Given the extensive use of social media, the question as
to whether Internet-based social networking sites (SNSs) have a
positive or negative impact on social relationships has been much
debated [1,2]. Cyberpessimists (e.g. [37]) have argued that the
2016 The Authors. Published by the Royal Society under the terms of the Creative Commons
Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted
use, provided the original author and source are credited.
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Internet has detrimental effects on our social life. In contrast, cyberoptimists (e.g. [8,9]) have insisted that
the effects have been beneficial in many different ways (for reviews, see [10,11]).
There is some evidence to suggest that one of the motivations for using social media among teenagers
is to extend their range of social contacts [12]. Indeed, it has specifically been claimed that those who
are more socially competent use social media to expand their network of friendships, thereby increasing
their social capital [1316]. However, young children, and to some extent teenagers, are relatively poor
at judging relationship quality: very young children, for example, commonly mistake a desire to form
friendships on their part with the assumption that such friendships are reciprocated [17]. Adults tend
to be more attuned to the nuances in different types of relationship and are less prone to signing up to
‘friending’ requests without considering the nature of the relationship involved. They may thus provide
a better test of the hypothesis than the teenagers who have been the focus of most social media research.
One specific respect in which the Internet has been held to change our social world is the size of
our social networks. On the basis of a projection from an equation relating social community size to
neocortex volume in apes, it had previously been suggested that there is a natural group size for humans
[18], and this has been validated against hunter–gatherer community sizes [19,20]aswellasthesizesof
offline personal social networks (egocentric networks) in two European populations [21,22]. This limit
is thought to arise from a combination of a cognitive constraint (the product of the relationship with
neocortex size known as the social brain hypothesis (SBH) [18,23]) and a time constraint associated with
the costs of servicing relationships [24,25]. Implicit evidence for a potential cognitive constraint has been
provided by a number of neuroimaging studies which show that individual variation in adult social
network size correlates with the volume of core areas in the neocortex (notably those regions of the
prefrontal and temporal lobes) that are associated with the ‘theory of mind’ network in humans [2629],
and this also seems to be true of monkeys [30].
An important feature of natural social networks in both humans [19,20,25] and non-human primates
[31] is that they are structured into a distinctive series of hierarchically inclusive layers that have a
natural scaling ratio of approximately 3. These layers reflect both interaction frequencies and, at least
in humans, emotional closeness [22,25,31]. In humans, these layers have values that approximate 5, 15,
50 and 150, and extend beyond this in at least two further layers to 500 and 1500 [32]. The first three layers
have been identified in several online datasets [33] and, at least in humans, appear to be a consequence
of a constraint on available social time [34] combined with a relationship between time invested in a
relationship and its quality (as rated in terms of emotional closeness) [25,35,36]. The two outermost layers
(at 500 and 1500) correspond, respectively, to acquaintances (people we would not consider as personal
friends or family, but know well enough to have a conversation with) and to the number of faces we can
put names to.
It has been suggested that, even if this limit on personal network size exists in the face-to-face world,
the rise of online SNSs has circumvented at least some of these constraints and has thus allowed us to
increase dramatically the number of people we can have as friends [9,3739]. Because there are significant
limits on the number of people we can talk to at any one time in the offline world [4042]aswellason
the amount of time we have available for social interaction [25,43], there is inevitably a limit on the size
of our egocentric social networks when relationships require time investment. In contrast, there are no
limits to the number of people who can read our posts, and SNSs might thus allow us to cut through this
constraint imposed by face-to-face interaction. Being able to interact with many individuals at the same
time could in principle allow us to increase social network size dramatically.
Although many recent studies have undertaken comparisons between online and offline networks,
almost all have focused on the small number of strong ties [44] that individuals have [4550]. The
definitions used in sampling networks in these studies have often been so restrictive that the typical
network size they reveal has been in the order of five individuals (and never more than 20), suggesting
that they are sampling just the two innermost core layers of personal egocentric networks (typically
five and 12–15 individuals, respectively [25,34]). These sampling strategies thus ignore the fact that an
individual’s social network (defined as all meaningful relationships) is considerably larger than this, and
typically in the order of 100–200 individuals.
In addition, these studies have, with very few exceptions, sampled students (and, in many cases,
teenagers), heavy SNS users or members of other specialized communities [2,51], and so cannot be
considered representative of the wider population. The few studies that have undertaken large-scale
randomized samples representative of the population at large [2,48] have focused only on the innermost
layers of the network and have had quite modest sample sizes (N1000 Internet users) by the standards
of sociological sampling. Two attempts to examine the size of extended online social networks with
seriously large samples have considered communities formed among twitterati (i.e. followers of a
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particular twitter account) [52] and scientific email communities [53]. Both claimed evidence for a
natural community size between 100 and 200, but neither of these can really be considered conventional
everyday social communities in any meaningful sense.
This study tests the claim that online social environments allow us to significantly increase the size of
our social networks using two large structured random samples of the UK population and the number
of friends listed on Facebook as the test metric. These data constitute the first attempt to determine the
natural limit on network size using unbiased, randomized, stratified sampling of a national population.
As such, this study is the first real attempt to test whether online social media do allow us to increase the
size of our social networks.
2. Material and methods
The data derive from two samples commissioned from the panel provider OnePoll by the WildCard
agency on behalf of the Thomas J. Fudge’s company, sampled from OnePoll’s large in-house panel. The
samples were carried out in the first week of April 2015 and the third week of May 2015, respectively.
Each sample was a nationally structured random sample of adults aged 18–65 years distributed
proportionally to age, sex and regional population across the UK.
Sample 1 included 2000 adults (mean age 39 years; 45.2% male), focused on people’s use of, and
satisfaction with, online social media, with adults who ‘made regular use of social media’ as the sampling
criterion. A total of 85.4% of respondents declared that they checked social media every day; 51%
declared that they had never deleted their social media profile. Only 12.3% of the entire sample declared
that, when they had deleted a profile, they had lasted more than two weeks before signing back in, with
4% declaring that they had lasted more than a year. Sample 2 included 1375 adults (mean age =37.4
years; 39.1% male) and sampled professional adults who worked full time at 9.00 to 17.00 weekday jobs
and had attended business meetings on behalf of their employer. In this sample, respondents were not
necessarily social media users, and Sample 2 thus might be seen as being more representative of the
general population. Each sample included approximately 30 questions related to use of online media
(Sample 1) and social behaviour in relation to management meetings (Sample 2), with network size
questions included as part of these.
In both cases, subjects were asked to state, on a 14-point (Sample 1) or 16-point (Sample 2) categorical
scale ranging from 0 to 1000+, how many friends they had on Facebook (electronic supplementary
material, tables S1 and S8). Categorical answers were used rather than asking for actual numbers of
Facebook friends because, in large-scale sampling, it is important to maintain respondent interest and
focus, and not to distract them by requiring them to break off and open new windows. Respondents in
Sample 1 were also asked to state how many of these individuals they considered to be close friends
(on a 12-point categorical scale ranging from 0 to 100+: electronic supplementary material, table S4) and
how many individuals they would ‘consider going to for advice or sympathy in times of great emotional
or other distress’ (on a 7-point categorical scale ranging between 0 and 16+: electronic supplementary
material, table S5). These correspond to the two innermost circles of the egocentric social network, the
sympathy group (normatively approx. 15 individuals) and the support clique (normatively approx. 5
individuals) [25,54].
3. Results
Figure 1 plots the distribution of total number of friends for the two samples. The mean number of friends
is 155.2 and 182.8 in the two samples, respectively. Neither of these differs significantly from the value
of 150 (95% CI =100–200) predicted by the SBH [18](z=0.20, p=0.842; z=1.29, p=0.198, respectively).
(If we exclude those who responded 0 in Sample 2, the mean increases to 186.5, but the conclusion
is unchanged.) As has been noted in all previous studies reporting network size, both distributions
have long tails to the right, but have marked modes around 150. Although the two distributions differ
significantly from each other (χ2=191.6, d.f. =9, p<0.0001), this is in fact mainly due to the high
frequency of 0 scores in Sample 2. Given the difference in sampling criteria (regular social media users
versus business professionals), this is perhaps hardly surprising. Discounting the 0 category, the two
distributions in fact correlate closely across size categories (r=0.909, N=11, p<0.001; the slope does
not differ significantly from β=1, t9=0.293, p=0.776).
In both cases, women list significantly more friends than men (Sample 1: 165.5 versus 145.0;
Sample 2: 196.2 versus 156.6; χ2=33.3 and χ2=56.1, respectively; d.f. =8, p<0.0001). Network size
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0
0
0
20
40
60
80
100
120
140
160
180
200
50
100
150
200
250
300
350
400
450
25 50 75 100 200 300
frequencyfrequency
400 500 600 700 800 900 1000
0 25 50 75 100 150 200
no. friends
250 300 350 400 450 500 600 700 800
(a)
(b)
Figure 1. Distribution of network size for (a) Sample 1 (social network users: N=2000) and (b) Sample 2 (business employees:
N=1375).
also varies significantly by decadal age class: in each sample, mean network size is negatively related
to age class, with younger age classes having larger online networks than older age classes (electronic
supplementary material, tables S3 and S9; r=−0.972, p=0.006, and r=−0.973, p=0.005; N=5 in both
cases). Both distributions differ significantly across age classes (χ2=436.7, d.f. =28, and χ2=352.1,
d.f. =24; p<0.0001). Partitioning χ2indicates that, in both samples, each age class differs significantly
from the others (χ254.2 and χ221.5, respectively, p<0.005). Note that the mean values for each age
class are almost identical in the two samples (electronic supplementary material, figure S1: Pearson’s
r=0.993, standardized β=0.993, N=5, p=0.001).
On average, respondents in Sample 1 considered that only 27.6% of their Facebook friends
could be considered ‘genuine’ (i.e. close) friends, with a strong modal value between 0 and 10%
(electronic supplementary material, table S2 and figure S2). These respondents were also asked more
explicit questions about the number of close friends they had. Figure 2 plots the distribution of
the support clique and the number of close friends (sympathy group) for Sample 1. The mean
values are 4.1 and 13.6, respectively, for the support clique (friends on whom you would depend
for emotional/social support in times of crisis) and sympathy group (close friends). Neither mean
is significantly different from the values of 3.8 ±2.29 and 11.3 ±6.19 given for these two grouping
layers by Hill & Dunbar [21] based on the literature (standardized deviates: z=0.13, p=0.897, and
z=0.37, p=0.711, respectively) or from the generic values of 5 and 15 identified by Zhou et al.[19]
(z=0.39, p=0.694, and z=0.23, p=0.818, respectively). It is noteworthy that the mean size of the
support clique and sympathy group hardly vary at all with age (electronic supplementary material,
tables S5 and S7) (r=−0.837, p=0.077, and r=−0.042, p=0.947, respectively; N=5). The distribution of
support clique values does not differ across decadal age classes (χ2=9.79, d.f. =20, p=0.972). Although
the distributions do differ significantly across age classes for the sympathy group data (χ2=45.76,
d.f. =24, p=0.0047), most of the deviations of observed from expected are in fact modest and rather
inconsistently distributed.
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0
0 1–2 3–4 5–7
support clique
s
y
mpath
y
g
roup
frequency
8–10 11–15 16+
5
10
15
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35
0
0
1–2
3–4
5–10
11–15
16–20
21–30
31–40
41–50
51–75
76–100
101+
frequency
5
10
15
20
25
30
(a)
(b)
Figure 2. Distribution of (a) support clique size and (b) sympathy group size for Sample 1 (N=2000).
4. Discussion
These data allow us to draw two conclusions. First, they confirm, using two separate, large, nationally
stratified random samples, that the typical egocentric network size for adult humans is similar to
that predicted by the SBH. The selection criteria for the two samples were quite different and could
have yielded very different outcomes. In fact, both estimates of network size were well within the
95% confidence limits around the predicted value from the SBH (roughly 100–200 [18]). As with
more conventional offline networks, there is considerable variance around the mean values. In offline
networks, this variance is due to personality differences (extroverts have larger networks than introverts
[55]), gender (women have larger networks than men [56]) and, as we show here, age (electronic
supplementary material, tables S3 and S9).
Sample 1 also confirms the existence and size of the two innermost layers of the social network, now
conventionally identified as the support clique and the sympathy group [25]. Numerically, the innermost
layer (five individuals) has been found in almost every online and offline study that has sampled network
sizes, where estimates have varied from 4.0 to 9.0 in online samples [2,46,48,57] and 2.1 to 7.4 in offline
studies [46,48,54,56,5865]. The 15-layer has likewise been widely identified, with online values varying
between 11.2 and 20.9 [2,45,48] and offline between 9.0 and 20.0 [28,48,54,56,6668]. In the present sample,
the mean values for these two layers (4.1 and 13.6) fit comfortably within these ranges.
The second main finding is that the two samples provide a direct test of whether online SNSs allow
individuals to have larger social networks than is possible offline because SNSs allow one or more of the
constraints that limit offline social network size to be circumvented. The results clearly suggest that they
do not. This result concurs with previous findings for a much smaller sample (N=117) which suggested
that heavy users of online social media do not have larger offline social networks than casual users, even
though more of these may appear online for heavy users [69]. Previous studies that have looked at this
question have typically focused exclusively on the innermost (5) network layer, and this study is the first
to test the full social network.
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We might argue that the long tails to the right provide evidence that, even if most do not, some people
can maintain larger networks online (i.e. a small number of people really do have large numbers of listed
friends). If so, it would seem that this does not apply universally: only 14.2% of respondents in Sample 1
listed more than 300 friends (i.e. significantly more than the average offline network) and only 13.4% of
those in Sample 2 did so. Since respondents were not asked to specify the quality of their relationships
with individual alters, we cannot say whether the extra members in these larger networks really are
additional high-quality relationships or simply individuals that would normally be included in the circle
of acquaintances that forms a further layer stretching out to 500 individuals in the offline world [19,25]. In
most online platforms, these are all formally subsumed under the single category ‘friends’, whereas in the
offline world we would naturally distinguish between friends and acquaintances of different emotional
quality (and may even make that distinction informally for ourselves online).
In fact, analyses of traffic in online environments such as Facebook and Twitter reproduce rather
faithfully both the nested structure of the inner layers of offline networks and their typical interaction
frequencies [33]. Taken together with the fact that, in this study, the sizes of the two inner friendship
circles did not differ from those previously identified in offline samples (figure 2), this suggests that it is
most likely failure to differentiate relationships of different quality in the outermost layers that leads to
the impression of large numbers of online ‘friends’ (see also [2]). Respondents who had unusually large
networks did not increase the numbers of close friendships they had, but rather added more loosely
defined acquaintances into their friendship circle simply because most social media sites do not allow
one to differentiate between these layers (see also below).
The data in the two samples confirm previous findings of a small but consistent difference in network
size between the sexes (with females generally having larger networks at any given layer than males)
[21,28,54,56]. In both samples in this study, women had significantly larger networks than men, though
the differences remain within the natural range of variation in egocentric social network size. This is in
line with previous studies, which have suggested that women have more friends than men at least in the
innermost layers (though, again, the absolute differences are numerically modest, and sometimes not
significant) [28,56,70]. This may be related to women’s greater social skills, as reflected in their higher
scores on the kinds of cognitive tasks (e.g. mentalizing) that are thought to underpin social relationships
[56,71].
The data also highlight a strong age effect in complete network size: younger respondents (18–24 year
olds) had significantly larger networks than older respondents (55+year olds) (electronic supplementary
material, tables S3 and S9). Rosen et al. [57] report a mean network size of 249 for a sample with a mean
age of 19.5 years, which is close to the mean of 282 found in the 18–24-year-old age group in this study
(electronic supplementary material, table S3). It is noteworthy that this is also close to the ‘optimal’
number of online friends that maximizes social attractiveness: in a study of students (mean age 20.2
years), Tong et al. [72] found that ratings for attractiveness of fictitious profiles peaked at profiles which
listed 302 friends (in a range covering 102–902 in steps of 200). This may reflect well-known differences
in how teenagers (in particular) and older adults use social media, with younger individuals using it in
a more exploratory way to meet new people [12].
Note, however, that there is no equivalent age effect for the inner two layers of the network
(electronic supplementary material, tables S5 and S7), suggesting that the age effect applies only in
the outermost layer(s) of more casual friendships. Even so, this age effect contrasts with findings from
offline networks, where younger respondents tend to have significantly smaller social networks than
older adults [21,24,35]. A likely explanation for this difference probably lies in the fact that SNSs typically
encourage promiscuous ‘friending’ of individuals who often have very tenuous links to ego (X is a friend
[or friend-of-friend-of-a-friend] of Y, so would you like to befriend them?). Given that children are less
discriminating than adults in defining friendships [17], this may cause younger people in general to
respond more positively to these invitations. In addition, teenagers and young adults are in a period of
their lives when searching for new friendship (and especially romantic) opportunities is a particularly
important part of the natural life cycle; this may encourage individuals to establish many weak links
with alters as a means of testing out the opportunities available to them.
It is perhaps worth noting that there has been a notable tendency for teenagers to move away from
using Facebook as a social environment and to make use of media like Snapchat, WeChat, Vine, Flickr and
Instagram instead [73], with Facebook being reserved mainly for managing social arrangements. It is not
yet entirely clear what has driven this, but the fact that Facebook is too open to view by others seems to
have been especially important [74,75]. Teenagers have much smaller offline social networks than adults
[24,76], and forcing them to enlarge their network with large numbers of anonymous ‘friends-of-friends’
may place significant strain on their ability to manage their networks. Thus, this trend towards more
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private social media may actually confirm the claim being made here—that open-ended social media do
not in fact allow us to increase the sizes of our social circles beyond that imposed by the SBH and the
constraints of everyday offline interaction.
The fact that social networks remain about the same size despite the communication opportunities
provided by social media suggests that the constraints that limit face-to-face networks are not fully
circumvented by online environments. Instead, it seems that online social networks remain subject to
the same cognitive demands of maintaining relationships that limit offline friendships. These constraints
come in two principal forms: a cognitive constraint derivative of the SBH and a temporal constraint
associated with the time that needs to be invested in a relationship to maintain it at a requisite level
of emotional intensity [25]. We can only interact coherently with a very small number of other people
(about three, in fact) at any one time [40,41]. It seems that even in an online environment, the focus of
our attention is still limited in this way.
This conclusion is reinforced by analyses of the frequencies with which individuals communicate
with members of their network in the inner network layers in online environments (Facebook and
Twitter): these yield interaction rates that are virtually identical to those observed in the offline world
[33]. Data from both face-to-face contacts [25,35] as well as mobile phone databases [34,77] suggest that
there are natural limits to both the amount of time we can devote to social interactions with network
members and how we distribute this time among them. Indeed, it seems that each of us distributes our
social capital, whether this is indexed by frequency of calling or by self-rated emotional closeness, in a
uniquely characteristic way rather like a social signature, and that this signature remains stable over time
despite significant churn in network membership [77]. This appears to be immune to the opportunities
for multiple interactions offered by the Internet.
Respondents were not asked to specify details about the individual alters in their networks (e.g. age,
gender, emotional closeness or spatial proximity). Consequently, we cannot say whether participants’
decisions about whom to include as a Facebook friend reflect ease of access to individuals (e.g. how far
away they lived, and so how easy it might be to meet up with them) or the distinction between family
and friends (an important feature of face-to-face networks [22]). It may well be that the individuals in the
sample with unusually small numbers of Facebook friends were using social media to maintain links only
with distant family and friends. If so, this is only likely to affect the variance in the data by increasing the
size of the left-hand tail (as figure 1 perhaps suggests). However, there is evidence to suggest that people
do not use communication media only to contact geographically distant alters: to the contrary, mobile
phone data show rather clearly that people in fact phone most frequently the people they live closest
to [78].
The fact that people do not seem to use social media to increase the size of their social circles
suggests that social media may function mainly to prevent friendships decaying over time in the
absence of opportunities for face-to-face contact [76,79]. Given that people generally find interactions via
digital media (including the phone as well as instant messaging and other text-based social media) less
satisfying than face-to-face interactions [80], it may be that face-to-face meetings are required from time
to time to prevent friendships, in particular, sliding down through the network layers and eventually
slipping over the edge of the 150 layer into the category of acquaintances (the 500 layer) beyond.
Friendships, in particular, have a natural decay rate in the absence of contact, and social media may well
function to slow down the rate of decay. However, that alone may not be sufficient to prevent friendships
eventually dying naturally if they are not occasionally reinforced by face-to-face interaction.
Ethics. The study was considered to be exempt from the need for ethics approval by the University of Oxford research
ethics board (CUREC).
Competing interests. I declare I have no competing interests.
Funding. R.D.’s research is funded by a European Research Council Advanced grant. The surveys were funded by the
Thomas J. Fudge’s company.
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Supplementary resource (1)

... The classification of social media platforms as small (vs. medium, and large) was determined through Dunbar and co.'s research on the average group size for humans (i.e., Dunbar's number; Dunbar, 2016;Konnikova, 2014;Zhou et al., 2005; see also Dunbar, 1993). It has been consistently reported that the average person has a social group/network of about 150 (what would be considered intimate friends and family, even extending to casual friends). ...
... It has been consistently reported that the average person has a social group/network of about 150 (what would be considered intimate friends and family, even extending to casual friends). More recent research has expanded on this (especially through a social media lens) and demonstrated that the size of one's social network can expand incrementally up to 1500, with around 200-300 still being considered within the scope of "casual friends", while around 500 included acquaintances, and over 900 up to 1500 is considered the maximum social network capacity that people can remember (Dunbar, 2016;Zhou et al., 2005). ...
Thesis
Does the social status of victims in emergencies play a role in bystanders’ compassionate orientations towards them? In this thesis, I examine the hitherto unexplored proposition that bystanders may be more inclined toward expressing compassion in favor of victims who signal high (rather than low) social status. I tested this novel thesis in six experiments that systematically varied the social status of victims of fabricated emergencies and afterward measured their compassion to investigate whether the expression of this emotion was stronger for higher (relative to lower) status victims. In doing so, I considered a variety of situational and individual difference factors that could enable (or constrain) a compassion bias favoring victims from high-status backgrounds. In the first empirical chapter (3), I showed that participants (N = 436) reported higher compassionate intentions toward victims of a terror attack that were described as coming from a high-status (vs. low-status) background, while also providing indirect evidence that cost calculations may play a role in this process. In the second empirical chapter (4), I directly investigated the cost-calculus caveat and explored the role of ideological persuasions. The initial experiment in Chapter 4 (N = 273) showed that even participants with an egalitarian ideology do sometimes succumb to the high-status compassion bias, but this occurs when the cost of doing so is trivial for them: a trend that was largely replicated in a subsequent experiment in that chapter (N = 288). The final empirical chapter (5) explored the role that threat appraisals might play in the process, testing the idea of whether high-status victims will continue to benefit from a compassion bias even when they seem threatening to bystanders. In the three experiments (N = 1,373) reported in Chapter 5, I showed that threat appraisals undermine a compassion bias favoring victims from both high and low-status backgrounds. Hence, overall, the preponderance of the evidence across Chapters 3-5 affirms the existence of compassion bias favoring victims from high-status backgrounds, although they also do outline important situational and individual difference factors that can sometimes eliminate or even reverse this trend. This is an important contribution in terms of not only theoretical advancements (i.e., helping to show that status plays a role in compassion during emergencies) but also practice (e.g., it could be useful in the training of frontline emergency responders).
... This allows one to follow groups with specific expertise, such as the Surgical Infection Society (SIS @SurgInfxSoc), IDSA (@IDSAInfo), SIDP (@SIDPharm), and SCCM (@SCCM), and use the targeted Twitter strategy we describe to influence practice and policy around antibiotic use among surgeons. Healthcare professionals, like all humans, are subject to the inherent limitations of their primate brain when trying to exchange information during social interactions [21]. There are barriers to efficient communication, such as time, geographical distance, resources, and language, that can be partially overcome with specific tools. ...
... There are barriers to efficient communication, such as time, geographical distance, resources, and language, that can be partially overcome with specific tools. Twitter has the advantage that it does not restrict social network size in the same way in which it may be limited by a surgeon's cognition [21]. In fact, Twitter can be used to create and coordinate global surgical communities and ecosystems, avoiding time, distance, and language restrictions, a clear advantage considering all the restrictions imposed by the COVID-19 pandemic. ...
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Many infectious diseases (ID) clinicians join Twitter to follow other ID colleagues or "like" people. While there is great value in engaging with people who have similar interests, there is equal value in engaging with "unlike" or non-ID people. Here, we describe how Twitter connected an ID pharmacist with a pediatric surgeon, a vice chair of surgery, a surgeon chief medical officer from Spain, and a surgical intensive care unit pharmacist. This Twitter collaboration resulted in several scholarly activities related to antibiotic resistance and antibiotic stewardship and served as a conduit for global collaboration.
... In stable social relationships, people know other people who have a relationship with them and know their relations with other people (Wikipedia, Dunbar number). Although it is believed that online social media develop the size of social networks and the set of related people, Goncalves et al. (2011) and Dunbar (2016) showed that the number of users' friends in online social media are close to the real social networks (Goncalves et al., 2011 andDunbar 2016). However, users rarely share their attention. ...
... In stable social relationships, people know other people who have a relationship with them and know their relations with other people (Wikipedia, Dunbar number). Although it is believed that online social media develop the size of social networks and the set of related people, Goncalves et al. (2011) and Dunbar (2016) showed that the number of users' friends in online social media are close to the real social networks (Goncalves et al., 2011 andDunbar 2016). However, users rarely share their attention. ...
... These parameters were explicitly chosen to achieve clustering coefficients and in-degrees that were realistic for real-life community interactions, namely, a consistent clustering coefficient of approximately 0.3 as well as an average in-degree between 7 and 9 (for populations that begin with a strong consensus belief, we expect marginally higher average degree). Though a person might interact with a larger number of individuals through their online social networks, or a smaller number of individuals through in-person interactions, surveys have shown that people report feeling genuinely close to between 5 and 10 individuals in their social circle, broadly construed [27]. The clustering coefficient was chosen to be consistent with values for average clustering coefficients on directed graphs using random walks on social networks [28]. ...
... Fig 5 depicts examples of the initialized networks for each of the four scenarios we analyzed. For each of the scenarios the average-in degree was between 6 and 9, which corresponds to realistic social networks [27,28]. Our simulation results are presented in Section 5. ...
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Covid-19 vaccines are widely available in the United States, yet our Covid-19 vaccination rates have remained far below 100%. Not only that, but CDC data shows that even in places where vaccine acceptance was proportionally high at the outset of the Covid-19 vaccination effort, that willingness has not necessarily translated into high rates of vaccination over the subsequent months. We model how such a shift could have arisen, using parameters in agreement with data from the state of Alabama. The simulations suggest that in Alabama, local interactions would have favored the emergence of tight consensus around the initial majority view, which was to accept the Covid-19 vaccine. Yet this is not what happened. We therefore add to our model the impact of mega-influencers such as mass media, the governor of the state, etc. Our simulations show that a single vaccine-hesitant mega-influencer, reaching a large fraction of the population, can indeed cause the consensus to shift radically, from acceptance to hesitancy. Surprisingly this is true even when the mega-influencer only reaches individuals who are already somewhat inclined to agree with them, and under the conservative assumption that individuals give no more weight to the mega-influencer than they would give to a single one of their friends or neighbors. Our simulations also suggest that a competing mega-influencer with the opposite view can shift the mean population opinion back, but cannot restore the tightness of consensus around that view. Our code and data are distributed in the ODyN (Opinion Dynamic Networks) library available at https://github.com/annahaensch/ODyN.
... We define the following dimensions of the online image: possible threats [50], privacy [51], resource security [52], user security and simplicity [53,54,55]. Earlier empirical results show that customer satisfaction increases when they perceive the company and its image as appropriate [56,57], indicating that image in social media is associated with customer trust and ultimately with the company's reputation [58]. User safety is perceived as the ability to navigate on social media in accordance with the law, with negligible risk, while ensuring that the activities carried out by enterprises are reliable, clear and professional [59,60,61,62,63]. ...
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... Empirical studies have validated this and put the number of people with whom we can meaningfully interact at approximately 150. Recent research showed that this is true of both off-line and on-line networks (Dunbar 2016). If that is the case, why then are our online social networks on average five to ten times larger? ...
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