Conference paper
Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, David M. Blei, James Moffet, Donald P. Green
Proceedings of the Web Conference (WWW), 2021
Proceedings of the Web Conference (WWW), 2021
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APA
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Schein, A., Vafa, K., Sridhar, D., Veitch, V., Quinn, J., Blei, D. M., … Green, D. P. (2021). Assessing the Effects of Friend-to-Friend Texting on Turnout in the 2018 U.S. Midterm Elections. In Proceedings of the Web Conference (WWW).
Chicago/Turabian
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Schein, Aaron, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, David M. Blei, James Moffet, and Donald P. Green. “Assessing the Effects of Friend-to-Friend Texting on Turnout in the 2018 U.S. Midterm Elections.” In Proceedings of the Web Conference (WWW), 2021.
MLA
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Schein, Aaron, et al. “Assessing the Effects of Friend-to-Friend Texting on Turnout in the 2018 U.S. Midterm Elections.” Proceedings of the Web Conference (WWW), 2021.
BibTeX Click to copy
@inproceedings{aaron2021a,
title = {Assessing the Effects of Friend-to-Friend Texting on Turnout in the 2018 U.S. Midterm Elections},
year = {2021},
author = {Schein, Aaron and Vafa, Keyon and Sridhar, Dhanya and Veitch, Victor and Quinn, Jeffrey and Blei, David M. and Moffet, James and Green, Donald P.},
booktitle = {Proceedings of the Web Conference (WWW)}
}
Other materials: [Code] [see below for video]
Abstract: Recent mobile app technology lets people systematize the process of messaging their friends to urge them to vote. Prior to the most recent US midterm elections in 2018, the mobile app Outvote randomized an aspect of their system, hoping to unobtrusively assess the causal effect of their users’ messages on voter turnout. However, properly assessing this causal effect is hindered by multiple statistical challenges, including attenuation bias due to mismeasurement of subjects’ outcomes and low precision due to two-sided non-compliance with subjects’ assignments. We address these challenges, which are likely to impinge upon any study that seeks to randomize authentic friend-to-friend interactions, by tailoring the statistical analysis to make use of additional data about both users and subjects. Using meta-data of users’ in-app behavior, we reconstruct subjects’ positions in users’ queues. We use this information to refine the study population to more compliant subjects who were higher in the queues, and we do so in a systematic way which optimizes a proxy for the study’s power. To mitigate attenuation bias, we then use ancillary data of subjects’ matches to the voter rolls that lets us refine the study population to one with low rates of outcome mismeasurement. Our analysis reveals statistically significant treatment effects from friend-to-friend mobilization efforts (8.3, CI = (1.2, 15.3)) that are among the largest reported in the get-out-the-vote (GOTV) literature. While social pressure from friends has long been conjectured to play a role in effective GOTV treatments, the present study is among the first to assess these effects experimentally.
IC2S2 2020
An earlier version of this paper was presented at the 2020 International Conference on Computational Social Science (IC2S2) where it won the Best Presentation award!
In the press
This research was covered by NBC News and the New Yorker, and I wrote about it in a Financial Times op-ed.
