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International Conference on Social Informatics

SocInfo 2017: Social Informatics pp 499–517Cite as

The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10539))

Abstract

Goel et al. [14] examined diffusion data from Twitter to conclude that online petitions are shared more virally than other types of content. Their definition of structural virality, which measures the extent to which diffusion follows a broadcast model or is spread person to person (virally), depends on knowing the topology of the diffusion cascade. But often the diffusion structure cannot be observed directly. We examined time-stamped signature data from the Obama White House’s We the People petition platform. We developed measures based on temporal dynamics that, we argue, can be used to infer diffusion structure as well as the more intrinsic notion of virality sometimes known as infectiousness. These measures indicate that successful petitions are likely to be higher in both intrinsic and structural virality than unsuccessful petitions are. We also investigate threshold effects on petition signing that challenge simple contagion models, and report simulations for a theoretical model that are consistent with our data.

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Notes

  1. 1.

    We the people petitions from the Obama years are archived at https://petitions.obamawhitehouse.archives.gov.

  2. 2.

    Our data are available at https://github.com/justinlai/petitiondata.

  3. 3.

    For an alternative perspective on e-petition “success,” see Wright [47].

  4. 4.

    Indeed, for some other types of content such as the spread of memes, initial infectiousness appears not to be a good predictor of later success [39, 45].

  5. 5.

    In footnote 10 on p. 187, Goel et al. clarify that this statement applies to normalized and not just to absolute size [14].

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Acknowledgements

We wish to thank Marek Hlavac for technical assistance, and Lee Ross and Howard Rheingold for timely and valuable feedback on an earlier version of this work (which was submitted by the first author as her masters thesis [7]), as well as three anonymous reviewers for their helpful comments.

Author information

Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Chi Ling Chan, Justin Lai & Todd Davies

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Bryan Hooi

Authors
  1. Chi Ling Chan
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  2. Justin Lai
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  3. Bryan Hooi
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  4. Todd Davies
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Corresponding author

Correspondence to Todd Davies .

Editor information

Editors and Affiliations

  1. Indiana University, Bloomington, Indiana, USA

    Giovanni Luca Ciampaglia

  2. University of Washington, Seattle, Washington, USA

    Afra Mashhadi

  3. University of Oxford, Oxford, United Kingdom

    Taha Yasseri

Appendices

Appendix A: Signature Graphs for Individual Petitions

Fig. 3.
figure 3

Temporal distribution of 5 randomly chosen petitions which did not reach the 100,000 signature mark.

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Fig. 4.
figure 4

Temporal distribution of 5 randomly chosen petitions which succeeded in reaching the 100,000 signature mark

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Appendix B: Aggregated Temporal Signature Graphs

Fig. 5.
figure 5

Adoption curves capturing daily accumulation of signatures in 3682 petitions across a 60-day period. A clear spike indicating a surge in support for a large number of petitions right before the 30-day deadline.

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Fig. 6.
figure 6

Adoption curves capturing daily accumulation of signatures in 3682 petitions across a 60-day period. A clear spike indicating a surge in support for a large number of petitions right before the 30-day deadline.

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Chan, C.L., Lai, J., Hooi, B., Davies, T. (2017). The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10539. Springer, Cham. https://doi.org/10.1007/978-3-319-67217-5_30

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  • DOI: https://doi.org/10.1007/978-3-319-67217-5_30

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