The illusion of change: Correcting for biases in change inference for sparse, societal-scale data

Gabriel Cadamuro, Ramya Korlakai Vinayak, Joshua Blumenstock, Sham Kakade, Jacob N. Shapiro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Societal-scale data is playing an increasingly prominent role in social science research; examples from research on geopolitical events include questions on how emergency events impact the diffusion of information or how new policies change patterns of social interaction. Such research often draws critical inferences from observing how an exogenous event changes meaningful metrics like network degree or network entropy. However, as we show in this work, standard estimation methodologies make systematically incorrect inferences when the event also changes the sparsity of the data. To address this issue, we provide a general framework for inferring changes in social metrics when dealing with non-stationary sparsity. We propose a plug-in correction that can be applied to any estimator, including several recently proposed procedures. Using both simulated and real data, we demonstrate that the correction significantly improves the accuracy of the estimated change under a variety of plausible data generating processes. In particular, using a large dataset of calls from Afghanistan, we show that whereas traditional methods substantially overestimate the impact of a violent event on social diversity, the plug-in correction reveals the true response to be much more modest.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PublisherAssociation for Computing Machinery, Inc
Pages2608-2615
Number of pages8
ISBN (Electronic)9781450366748
DOIs
StatePublished - May 13 2019
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: May 13 2019May 17 2019

Publication series

NameThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019

Conference

Conference2019 World Wide Web Conference, WWW 2019
CountryUnited States
CitySan Francisco
Period5/13/195/17/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Keywords

  • Call Detail Records
  • Change Detection
  • Computational Social Science
  • Entropy Estimation

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  • Cite this

    Cadamuro, G., Vinayak, R. K., Blumenstock, J., Kakade, S., & Shapiro, J. N. (2019). The illusion of change: Correcting for biases in change inference for sparse, societal-scale data. In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 (pp. 2608-2615). (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3308558.3313722