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. 2009 Sep 30;4(9):e7252.
doi: 10.1371/journal.pone.0007252.

The cultural dynamics of copycat suicide

Alex Mesoudi  1
Affiliations

The cultural dynamics of copycat suicide

Alex Mesoudi. PLoS One. .

Abstract

The observation that suicides sometimes cluster in space and/or time has led to suggestions that these clusters are caused by the social learning of suicide-related behaviours, or "copycat suicides". Point clusters are clusters of suicides localised in both time and space, and have been attributed to direct social learning from nearby individuals. Mass clusters are clusters of suicides localised in time but not space, and have been attributed to the dissemination of information concerning celebrity suicides via the mass media. Here, agent-based simulations, in combination with scan statistic methods for detecting clusters of rare events, were used to clarify the social learning processes underlying point and mass clusters. It was found that social learning between neighbouring agents did generate point clusters as predicted, although this effect was partially mimicked by homophily (individuals preferentially assorting with similar others). The one-to-many transmission dynamics characterised by the mass media were shown to generate mass clusters, but only where social learning was weak, perhaps due to prestige bias (only copying prestigious celebrities) and similarity bias (only copying similar models) acting to reduce the subset of available models. These findings can help to clarify and formalise existing hypotheses and to guide future empirical work relating to real-life copycat suicides.

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Conflict of interest statement

Competing Interests: The author has declared that no competing interests exist.

Figures

Figure 1
Figure 1. Three time series indicating (A) baseline suicide occurrences with no clustering, (B) a spatiotemporal cluster resulting from social learning, and (C) a spatial cluster resulting from homophily.
Each square within the 10×10 grid indicates one 10-agent sub-group, with the colour of the square indicating the frequency of suicide from green (0%) to red (100%). In A, randomly distributed suicide events can be observed due to the non-copycat probability of suicide (p0 = 0.005). No clustering is detected under these conditions. In B, a spatiotemporal point cluster generated by social learning (s = 5) is marked with a red circle, and can be seen persisting over a period of three generations from t = 73 to t = 75 inclusive, thus showing localisation in both time and space. In C there is no social learning (s = 0), but homophily (h = 1) and large inter-group differences (q = 0.4) causes one sub-group, marked with a red circle, to be composed entirely of high suicide risk agents. This group repeatedly features suicides throughout the simulation run, forming a spatial (but not temporal) cluster despite the lack of social learning.
Figure 2
Figure 2. Two time series illustrating the effects of strong one-to-many transmission (r = 9).
In A, when the baseline suicide rate and the strength of social learning are relatively high (p0 = 0.005, s = 1), a pandemic causes the entire population to commit suicide at extremely high rates throughout the simulation run. Neither spatial nor temporal clusters are observed under these conditions, which are obviously highly unrealistic. In B, when the frequency of social learning is reduced by introducing prestige bias (p0 = 0.005, s = 0, cp = 0.01, cs = 5) such that only a small minority of agents have social influence, mass (temporal but not spatial) clusters emerge. Here, one of the four suicides that occur in generation t = 84 was a prestigious “celebrity”, resulting in a mass cluster in the following three generations. Suicide rates then drop back to baseline pre-cluster levels at generation t = 88.
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