TY - GEN
T1 - The illusion of change
T2 - 2019 World Wide Web Conference, WWW 2019
AU - Cadamuro, Gabriel
AU - Vinayak, Ramya Korlakai
AU - Blumenstock, Joshua
AU - Kakade, Sham
AU - Shapiro, Jacob N.
N1 - Publisher Copyright:
© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
PY - 2019/5/13
Y1 - 2019/5/13
N2 - 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.
AB - 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.
KW - Call Detail Records
KW - Change Detection
KW - Computational Social Science
KW - Entropy Estimation
UR - http://www.scopus.com/inward/record.url?scp=85066917431&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066917431&partnerID=8YFLogxK
U2 - 10.1145/3308558.3313722
DO - 10.1145/3308558.3313722
M3 - Conference contribution
AN - SCOPUS:85066917431
T3 - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
SP - 2608
EP - 2615
BT - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PB - Association for Computing Machinery, Inc
Y2 - 13 May 2019 through 17 May 2019
ER -