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

To appear in Giovanni Luca Ciampaglia, Afra J. Mashhadi, and Taha Yasseri (Editors), Social Informatics: Proceedings of the 9th International Conference, SocInfo 2017 (Oxford, UK, September 13-15), Springer LNCS, 2017

19 Pages Posted: 13 Aug 2017  

Chi Ling Chan

Symbolic Systems Program

Justin Lai

Symbolic Systems Program

Brian Hooi

Carnegie Mellon University - Department of Machine Learning

Todd Davies

Stanford University - Symbolic Systems Program; Center for the Study of Language and Information

Date Written: July 25, 2017

Abstract

Goel et al. (2016) 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.

Keywords: petitions, virality, broadcast, diffusion

JEL Classification: C55, C63, Y80, Z13, Z18

Suggested Citation

Chan, Chi Ling and Lai, Justin and Hooi, Brian and Davies, Todd, The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data (July 25, 2017). To appear in Giovanni Luca Ciampaglia, Afra J. Mashhadi, and Taha Yasseri (Editors), Social Informatics: Proceedings of the 9th International Conference, SocInfo 2017 (Oxford, UK, September 13-15), Springer LNCS, 2017. Available at SSRN: https://ssrn.com/abstract=3017040

Chi Ling Chan

Symbolic Systems Program ( email )

Stanford, CA 94305
United States

Justin Lai

Symbolic Systems Program ( email )

Stanford, CA 94305
United States

Brian Hooi

Carnegie Mellon University - Department of Machine Learning ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213
United States

HOME PAGE: http://https://www.andrew.cmu.edu/user/bhooi/

Todd R. Davies (Contact Author)

Stanford University - Symbolic Systems Program ( email )

Stanford, CA 94305-2150
United States

HOME PAGE: http://www.stanford.edu/~davies

Center for the Study of Language and Information ( email )

Stanford, CA 94305-4115
United States

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