Detecting emotional contagion in massive social networks

PloS one, Volume 9, Issue 3, 2014, Pages e90315-e90315.

Cited by: 239|Bibtex|Views92|DOI:https://doi.org/10.1371/journal.pone.0090315
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Experiments have demonstrated that people can ‘‘catch’’ emotional states they observe in others over time frames ranging from seconds to months, and the possibility of emotional contagion between strangers, even those in ephemeral contact, has been documented by the effects of ‘‘...

Abstract:

Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emoti...More

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Introduction
  • Happiness and other emotions have recently been an important focus of attention in a wide range of disciplines, including psychology, economics, and neuroscience [1,2,3,4].
  • It is difficult to ascertain whether correlations in observational studies result from influencing the emotions of social contacts or from choosing social contacts with similar emotions [16]
  • This problem has been addressed by using experimental methods to estimate network effects [17,18,19,20,21,22], but these methods are either limited in scale and external validity, or they require very close collaboration with private companies, which means there are limited opportunities to conduct such experiments.
  • They may wish to provide a uniform online experience to all users, which reduces their willingness to create experimental treatment groups of sufficient size to take advantage of their massive scale
Highlights
  • Happiness and other emotions have recently been an important focus of attention in a wide range of disciplines, including psychology, economics, and neuroscience [1,2,3,4]
  • Experiments have demonstrated that people can ‘‘catch’’ emotional states they observe in others over time frames ranging from seconds to months [6,7], and the possibility of emotional contagion between strangers, even those in ephemeral contact, has been documented by the effects of ‘‘service with a smile’’ on customer satisfaction and tipping [8]
  • This problem has been addressed by using experimental methods to estimate network effects [17,18,19,20,21,22], but these methods are either limited in scale and external validity, or they require very close collaboration with private companies, which means there are limited opportunities to conduct such experiments
  • We propose an alternative method for detecting emotional contagion in massive social networks that is based on instrumental variables regression, a technique pioneered in economics [23]
  • Our estimates of the social contagion of emotional expression suggest that there may be large-scale spillovers in online networks
  • We estimate that a rainy day in New York City directly yields an additional 1500 (95% CI 1100 to 2100) negative posts by users in New York City and about 700 (95% CI 600 to 800) negative posts by their friends elsewhere
  • It is plausible that these effects might be even stronger when subpopulations are geographically defined, since many studies suggest that physical proximity increases social influence between connected individuals [16]. Another limitation is that instruments are not always readily available, and in some cases it may be unclear whether they are causally and directly related to the outcome variable of interest. When such instruments are available, this approach may prove to be a useful alternative to costly largescale experiments with limited external validity since they require neither experimental control nor alteration of the user environment
Results
  • Consistent with recently-published research on Twitter posts [28], Fig. 1 shows temporal patterns of variation in positive and negative emotions on Facebook that correspond to greater happiness on weekends and holidays.
  • The authors find that an average rainy day decreases the number of positive posts by 1.19% (95% CI: 0.78% to 1.60%) and increases the number of negative posts by 1.16% (0.78% to 1.55%)
  • While these effects are small, it is their statistical significance – not size – that matters, since the goal is to use them as instruments to study the effect of exogenous variation in friends’ emotional expression on one’s own expression.
  • Both models generate test statistics that suggest the rainfall instruments are strong enough to provide adequate power and that they are appropriately identified
Conclusion
  • The authors' estimates of the social contagion of emotional expression suggest that there may be large-scale spillovers in online networks.
  • It is plausible that these effects might be even stronger when subpopulations are geographically defined, since many studies suggest that physical proximity increases social influence between connected individuals [16].
  • Another limitation is that instruments are not always readily available, and in some cases it may be unclear whether they are causally and directly related to the outcome variable of interest.
  • When such instruments are available, this approach may prove to be a useful alternative to costly largescale experiments with limited external validity since they require neither experimental control nor alteration of the user environment
Summary
  • Introduction:

    Happiness and other emotions have recently been an important focus of attention in a wide range of disciplines, including psychology, economics, and neuroscience [1,2,3,4].
  • It is difficult to ascertain whether correlations in observational studies result from influencing the emotions of social contacts or from choosing social contacts with similar emotions [16]
  • This problem has been addressed by using experimental methods to estimate network effects [17,18,19,20,21,22], but these methods are either limited in scale and external validity, or they require very close collaboration with private companies, which means there are limited opportunities to conduct such experiments.
  • They may wish to provide a uniform online experience to all users, which reduces their willingness to create experimental treatment groups of sufficient size to take advantage of their massive scale
  • Results:

    Consistent with recently-published research on Twitter posts [28], Fig. 1 shows temporal patterns of variation in positive and negative emotions on Facebook that correspond to greater happiness on weekends and holidays.
  • The authors find that an average rainy day decreases the number of positive posts by 1.19% (95% CI: 0.78% to 1.60%) and increases the number of negative posts by 1.16% (0.78% to 1.55%)
  • While these effects are small, it is their statistical significance – not size – that matters, since the goal is to use them as instruments to study the effect of exogenous variation in friends’ emotional expression on one’s own expression.
  • Both models generate test statistics that suggest the rainfall instruments are strong enough to provide adequate power and that they are appropriately identified
  • Conclusion:

    The authors' estimates of the social contagion of emotional expression suggest that there may be large-scale spillovers in online networks.
  • It is plausible that these effects might be even stronger when subpopulations are geographically defined, since many studies suggest that physical proximity increases social influence between connected individuals [16].
  • Another limitation is that instruments are not always readily available, and in some cases it may be unclear whether they are causally and directly related to the outcome variable of interest.
  • When such instruments are available, this approach may prove to be a useful alternative to costly largescale experiments with limited external validity since they require neither experimental control nor alteration of the user environment
Funding
  • Funding: This work was partially supported by Army Research Office Grant W911NF-11-1-0363, and a grant from the James S
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