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The dynamics of audience applause.

Mann RP, Faria J, Sumpter DJ, Krause J - J R Soc Interface (2013)

Bottom Line: Individuals' probability of starting clapping increased in proportion to the number of other audience members already 'infected' by this social contagion, regardless of their spatial proximity.The cessation of applause is similarly socially mediated, but is to a lesser degree controlled by the reluctance of individuals to clap too many times.We also found consistent differences between individuals in their willingness to start and stop clapping.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Uppsala University, Uppsala 75106, Sweden. rmann@math.uu.se

ABSTRACT
The study of social identity and crowd psychology looks at how and why individual people change their behaviour in response to others. Within a group, a new behaviour can emerge first in a few individuals before it spreads rapidly to all other members. A number of mathematical models have been hypothesized to describe these social contagion phenomena, but these models remain largely untested against empirical data. We used Bayesian model selection to test between various hypotheses about the spread of a simple social behaviour, applause after an academic presentation. Individuals' probability of starting clapping increased in proportion to the number of other audience members already 'infected' by this social contagion, regardless of their spatial proximity. The cessation of applause is similarly socially mediated, but is to a lesser degree controlled by the reluctance of individuals to clap too many times. We also found consistent differences between individuals in their willingness to start and stop clapping. The social contagion model arising from our analysis predicts that the time the audience spends clapping can vary considerably, even in the absence of any differences in the quality of the presentations they have heard.

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Model schematic. Individuals progress from an initially ‘susceptible’ state (S), before they have started clapping, to an ‘infected’ state (I) while clapping and eventually to a ‘recovered’ state (R) once they stop clapping. The probability of moving from S to I is given by the starting probability per second, Pstart. Once the individuals have started clapping, they either stop or continue after each successive clap, stopping with probability Pstop or continuing with probability 1 − Pstart. These probabilities are determined by the proportion of individuals and direct neighbours who have started and stopped clapping and the number of claps each individual has already performed according to the models described in the text.
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RSIF20130466F2: Model schematic. Individuals progress from an initially ‘susceptible’ state (S), before they have started clapping, to an ‘infected’ state (I) while clapping and eventually to a ‘recovered’ state (R) once they stop clapping. The probability of moving from S to I is given by the starting probability per second, Pstart. Once the individuals have started clapping, they either stop or continue after each successive clap, stopping with probability Pstop or continuing with probability 1 − Pstart. These probabilities are determined by the proportion of individuals and direct neighbours who have started and stopped clapping and the number of claps each individual has already performed according to the models described in the text.

Mentions: Both the onset and the cessation of clapping follow a sigmoidal curve, with an initially slow uptake of the new behaviour followed by a phase of rapid change and eventual saturation (figure 1). Such sigmoidal growth and decay resemble the pattern of infection typically seen in the spread of diseases, both empirically and in epidemiology models, supporting the possibility of social contagion in clapping. We used a Bayesian methodology to test models for starting and stopping clapping [13–15]. We construct models that specify the probability that an individual will start or stop clapping (figure 2). These probabilities are conditioned on the state of the group (see the listed group characteristics below). By iterating over all observed events (starting/not starting; stopping/not stopping) and multiplying the probabilities of those events specified by a given model, we determine the likelihood of the data conditioned on any specific values of the model's adjustable parameters. Further summing over a range of possible parameter values by integration, we fairly assess the relative probabilities of the models (see §4).Figure 2.


The dynamics of audience applause.

Mann RP, Faria J, Sumpter DJ, Krause J - J R Soc Interface (2013)

Model schematic. Individuals progress from an initially ‘susceptible’ state (S), before they have started clapping, to an ‘infected’ state (I) while clapping and eventually to a ‘recovered’ state (R) once they stop clapping. The probability of moving from S to I is given by the starting probability per second, Pstart. Once the individuals have started clapping, they either stop or continue after each successive clap, stopping with probability Pstop or continuing with probability 1 − Pstart. These probabilities are determined by the proportion of individuals and direct neighbours who have started and stopped clapping and the number of claps each individual has already performed according to the models described in the text.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4043176&req=5

RSIF20130466F2: Model schematic. Individuals progress from an initially ‘susceptible’ state (S), before they have started clapping, to an ‘infected’ state (I) while clapping and eventually to a ‘recovered’ state (R) once they stop clapping. The probability of moving from S to I is given by the starting probability per second, Pstart. Once the individuals have started clapping, they either stop or continue after each successive clap, stopping with probability Pstop or continuing with probability 1 − Pstart. These probabilities are determined by the proportion of individuals and direct neighbours who have started and stopped clapping and the number of claps each individual has already performed according to the models described in the text.
Mentions: Both the onset and the cessation of clapping follow a sigmoidal curve, with an initially slow uptake of the new behaviour followed by a phase of rapid change and eventual saturation (figure 1). Such sigmoidal growth and decay resemble the pattern of infection typically seen in the spread of diseases, both empirically and in epidemiology models, supporting the possibility of social contagion in clapping. We used a Bayesian methodology to test models for starting and stopping clapping [13–15]. We construct models that specify the probability that an individual will start or stop clapping (figure 2). These probabilities are conditioned on the state of the group (see the listed group characteristics below). By iterating over all observed events (starting/not starting; stopping/not stopping) and multiplying the probabilities of those events specified by a given model, we determine the likelihood of the data conditioned on any specific values of the model's adjustable parameters. Further summing over a range of possible parameter values by integration, we fairly assess the relative probabilities of the models (see §4).Figure 2.

Bottom Line: Individuals' probability of starting clapping increased in proportion to the number of other audience members already 'infected' by this social contagion, regardless of their spatial proximity.The cessation of applause is similarly socially mediated, but is to a lesser degree controlled by the reluctance of individuals to clap too many times.We also found consistent differences between individuals in their willingness to start and stop clapping.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Uppsala University, Uppsala 75106, Sweden. rmann@math.uu.se

ABSTRACT
The study of social identity and crowd psychology looks at how and why individual people change their behaviour in response to others. Within a group, a new behaviour can emerge first in a few individuals before it spreads rapidly to all other members. A number of mathematical models have been hypothesized to describe these social contagion phenomena, but these models remain largely untested against empirical data. We used Bayesian model selection to test between various hypotheses about the spread of a simple social behaviour, applause after an academic presentation. Individuals' probability of starting clapping increased in proportion to the number of other audience members already 'infected' by this social contagion, regardless of their spatial proximity. The cessation of applause is similarly socially mediated, but is to a lesser degree controlled by the reluctance of individuals to clap too many times. We also found consistent differences between individuals in their willingness to start and stop clapping. The social contagion model arising from our analysis predicts that the time the audience spends clapping can vary considerably, even in the absence of any differences in the quality of the presentations they have heard.

Show MeSH
Related in: MedlinePlus