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Information flow reveals prediction limits in online social activity

Nature Human Behaviour volume 3, pages 122–128 (2019)Cite this article

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A Publisher Correction to this article was published on 04 February 2019

This article has been updated

Abstract

Modern society depends on the flow of information over online social networks, and users of popular platforms generate substantial behavioural data about themselves and their social ties1,2,3,4,5. However, it remains unclear what fundamental limits exist when using these data to predict the activities and interests of individuals, and to what accuracy such predictions can be made using an individual’s social ties. Here, we show that 95% of the potential predictive accuracy for an individual is achievable using their social ties only, without requiring that individual’s data. We used information theoretic tools to estimate the predictive information in the writings of Twitter users, providing an upper bound on the available predictive information that holds for any predictive or machine learning methods. As few as 8–9 of an individual’s contacts are sufficient to obtain predictability compared with that of the individual alone. Distinct temporal and social effects are visible by measuring information flow along social ties, allowing us to better study the dynamics of online activity. Our results have distinct privacy implications: information is so strongly embedded in a social network that, in principle, one can profile an individual from their available social ties even when the individual forgoes the platform completely.

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Fig. 1: Information and predictability in online social activity.
Fig. 2: Recency of information.
Fig. 3: Social interactions are visible in information flow.
Fig. 4: An ‘information homophily’ between egos and alters.

Code availability

The code used to generate the results of this paper is available from the corresponding authors upon request.

Data availability

The data that support the findings of this study are available at Figshare.

Change history

  • 04 February 2019

    The original and corrected figures are shown in the accompanying Publisher Correction.

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Acknowledgements

We gratefully acknowledge the resources provided by the Vermont Advanced Computing Core. This material is based on work supported by the National Science Foundation under grant no. IIS-1447634 (J.P.B.). L.M. acknowledges support from the Data To Decisions Cooperative Research Centre (D2D CRC) and the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Mathematics & Statistics, University of Vermont, Burlington, VT, USA

    James P. Bagrow, Xipei Liu & Lewis Mitchell

  2. Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA

    James P. Bagrow, Xipei Liu & Lewis Mitchell

  3. School of Mathematical Sciences, North Terrace Campus, The University of Adelaide, Adelaide, South Australia, Australia

    Lewis Mitchell

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  1. James P. Bagrow
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  2. Xipei Liu
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Contributions

J.P.B. and L.M. designed the research. L.M. oversaw data collection and processing. X.L. collected and analysed human rater data. J.P.B. and L.M. analysed the data and wrote the manuscript.

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Correspondence to James P. Bagrow or Lewis Mitchell.

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Supplementary Notes 1–9, Supplementary Figures 1–13, Supplementary Tables 1–4

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Bagrow, J.P., Liu, X. & Mitchell, L. Information flow reveals prediction limits in online social activity. Nat Hum Behav 3, 122–128 (2019). https://doi.org/10.1038/s41562-018-0510-5

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