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Series:   Elements in Campaigns and Elections

Securing American Elections

How Data-Driven Election Monitoring Can Improve Our Democracy

Published online by Cambridge University Press:  05 November 2020

R. Michael Alvarez ,
Nicholas Adams-Cohen ,
Seo-young Silvia Kim  and
Yimeng Li
R. Michael Alvarez
Affiliation:
California Institute of Technology
Nicholas Adams-Cohen
Affiliation:
Stanford University, California
Seo-young Silvia Kim
Affiliation:
California Institute of Technology
Yimeng Li
Affiliation:
California Institute of Technology

Summary

The integrity of democratic elections, both in the United States and abroad, is an important problem. In this Element, we present a data-driven approach that evaluates the performance of the administration of a democratic election, before, during, and after Election Day. We show that this data-driven method can help to improve confidence in the integrity of American elections.
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Keywords

election securityelection auditselection fraudvoter registrationsocial mediasurvey methodologyvoter confidence
Type
Element
Information
Series: Elements in Campaigns and Elections
Online ISBN: 9781108887359
Publisher: Cambridge University Press
Print publication: 26 November 2020

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References

Adams-Cohen, N. J., Hao, C., Jia, C., Matschke, N., & Alvarez, R. M. (2017). Election monitoring using Twitter (Working Paper No. 129). California Institute of Technology. Retrieved from http://vote.caltech.edu/working-papers/129Google Scholar
Alvarez, R. M., Atkeson, L. R., & Hall, T. E. (2012a). Confirming elections: Creating confidence and integrity through election auditing. Palgrave Macmillan.Google Scholar
Alvarez, R. M., Atkeson, L. R., & Hall, T. E. (2012b). Evaluating elections: A handbook of methods and standards. Cambridge University Press. doi: http://dx.doi.org/10.1017/CBO9781139226547Google Scholar
Alvarez, R. M., Atkeson, L. R., Levin, I., & Li, Y. (2019). Paying attention to inattentive survey respondents. Political Analysis, 27(2), 145–162. doi: 10.1017/pan.2018.57CrossRefGoogle Scholar
Alvarez, R. M., Cao, J., and Li, Y. (2020). Voter experiences, perceptions of fraud, and voter confidence (Working Paper No. 139). Caltech/MIT Voting Technology Project. Retrieved from http://vote.caltech.edu/working-papers/139.Google Scholar
Alvarez, R. M., & Hall, T. E. (2006). Controlling democracy: The principal– agent problems in election administration. Policy Studies Journal, 34(4), 491–510. doi: 10.1111/j.1541-0072.2006.00188.xGoogle Scholar
Alvarez, R. M., Hall, T. E., and Llewellyn, M. H. (2008). Are Americans confident their ballots are counted? The Journal of Politics 70(3), 754–766.CrossRefGoogle Scholar
Alvarez, R. M., Katz, G., & Pomares, J. (2011). The impact of new technologies on voter confidence in Latin America: Evidence from e-voting experiments in Argentina and Colombia. Journal of Information Technology & Politics, 8(2), 199–217.Google Scholar
Alvarez, R. M., & Katz, J. N. (2008). The case of the 2002 general election. In Alvarez, R. M., Hall, T. E., & Hyde, S. D. (Eds.), Election fraud: Detecting and deterring electoral manipulation (pp. 149–162). Brookings Institution Press.Google Scholar
Alvarez, R. M., Katz, J. N., Hill, S. A., & Hartman, E. K. (2012). Machines versus humans: The counting and recounting of prescored punchcard ballots. In Alvarez, R. M., Atkeson, L. R., & Hall, T. E. (Eds.), Confirming elections: Creating confidence and integrity through election auditing (pp. 73–88). Palgrave Macmillan.Google Scholar
Alvarez, R. M., & Schousen, M. M. (1993). Policy moderation or conflicting expectations: Testing the international models of split-ticket voting. American Politics Quarterly, 21(4), 410–438.Google Scholar
Ansolabehere, S., & Hersh, E. (2010). The quality of voter registration records: A state-by-state analysis. Report, Caltech/MIT Voting Technology Project. http://www.vote.caltech.edu/reports/6Google Scholar
Ansolabehere, S., & Hersh, E. (2014). Voter registration: The process and quality of lists. In Burden, Barry C. and Stewart, Charles III (Eds.), The measure of American elections (pp. 61–90). Cambridge University Press. https://books.google.com/books?hl=en&lr=&id=mTADBAAAQBAJ&oi=fnd&pg=PR13&dq=the+measure+of+american+elections&ots=Fo-4IxJi5-&sig=mr-EoL99n3vESByjQCzsRo9rPv8#v=onepage&q=the%20measure%20of%20american%20elections&f=falseGoogle Scholar
Ansolabehere, S., & Hersh, E. (2017). ADGN: An algorithm for record linkage using address, date of birth, gender, and name. Statistics and Public Policy, 4(1), 1–10.Google Scholar
Atkeson, L. R., Alvarez, R. M., & Hall, T. E. (2015). Voter confidence: How to measure it and how it differs from government support. Election Law Journal: Rules, Politics, and Policy, 14(3), 207–219. doi: 10.1089/elj.2014.0293Google Scholar
Atkeson, L. R., & Saunders, K. L. (2007). The effect of election administration on voter confidence: A local matter? PS: Political Science and Politics, 40(4), 655–660. Retrieved from www.jstor.org/stable/20452045.Google Scholar
Barberá, P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Political Analysis, 23(1), 76–91.Google Scholar
Beauchamp, N. (2017). Predicting and interpolating state-level polls using Twitter textual data. American Journal of Political Science, 61(2), 490–503.Google Scholar
Beber, B., & Scacco, A. (2012). What the numbers say: A digit-based test for election fraud. Political Analysis, 20(2), 211–234.Google Scholar
Benoit, K., & Nulty, P. (2016). quanteda: Quantitative analysis of textual data [Computer software manual]. Retrieved from http://github.com/kbenoit/quanteda (R package version 0.9.1–11)Google Scholar
Berinsky, A. J., Margolis, M. F., & Sances, M. W. (2014). Separating the shirkers from the workers? Making sure respondents pay attention on self-administered surveys. American Journal of Political Science, 58(3), 739–753. doi: 10.1111/ajps.12081Google Scholar
Bessi, A., & Ferrara, E. (2016). Social bots distort the 2016 US presidential election online discussion. First Monday, 21(11).Google Scholar
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan.), 993–1022.Google Scholar
Breuninger, K. (2018, November). Missing power cords, foreclosures: Here’s where voters are running into problems at the polls. CNBC. Retrieved from www.cnbc.com/2018/11/06/heres-where-voters-are-running-into-problems-at-the-polls.htmlGoogle Scholar
Burden, B. C., & Kimball, D. C. (2002). Why Americans split their tickets: Campaigns, competition, and divided government. University of Michigan Press.Google Scholar
Burnap, P., Gibson, R., Sloan, L., Southern, R., & Williams, M. (2016). 140 characters to victory? Using Twitter to predict the UK 2015 general election. Electoral Studies, 41, 230–233.CrossRefGoogle Scholar
Campbell, A., Converse, P., Miller, W., & Stokes, D. (1980). The American Voter. University of Chicago Press.Google Scholar
Cantú, F., & Saiegh, S. M. (2011). Fraudulent democracy? An analysis of Argentina’s infamous decade using supervised machine learning. Political Analysis, 19(4), 409–433.Google Scholar
Ceron, A., Curini, L., & Iacus, S. M. (2015). Using sentiment analysis to monitor electoral campaigns: Method matters – evidence from the United States and Italy. Social Science Computer Review, 33(1), 3–20.CrossRefGoogle Scholar
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 15.Google Scholar
Christen, P. (2012). Data matching: Concepts and techniques for record linkage, entity resolution, and duplicate detection. Springer Science & Business Media.Google Scholar
Christen, P. (2014). Preparation of a real temporal voter data set for record linkage and duplicate detection research (Tech. Rep.). The Australian National University. Retrieved from http://users.cecs.anu.edu.au/~Peter.Christen/publications/ncvoter-report-29june2014.pdfGoogle Scholar
Cohen, W., Ravikumar, P., & Fienberg, S. (2003). A comparison of string metrics for matching names and records. In Proceedings of the KDD-2003 Workshop on Data Cleaning, Record Linkage, and Object Consolidation Washington, DC, August, 2003 (Vol. 3, pp. 73–78).Google Scholar
Conover, M. D., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F., & Flammini, A. (2011). Political polarization on Twitter. In Proceedings of the 5th international conference on weblogs and social media. Barcelona, Spain: AAAI.Google Scholar
Deville, J.-C., & Sarndal, C.-E. (1992). Calibration estimators in survey sampling. Journal of the American Statistical Association, 87(418), 376–382. Retrieved from www.jstor.org/stable/2290268 doi: 10.2307/2290268Google Scholar
Dreyfuss, E. (2018, November). Georgia voting machine issues heighten scrutiny on Brian Kemp. Wired.Google Scholar
Enamorado, T., Fifield, B., & Imai, K. (2018). Using a probabilistic model to assist merging of large-scale administrative records. American Political Science Review, 1–19.Google Scholar
Epstein, R. J. (2018, August 22). Republican Troy Balderson declared winner in tight Ohio special election. The Wall Street Journal.Google Scholar
Fellegi, I. P., & Sunter, A. B. (1969). A theory for record linkage. Journal of the American Statistical Association, 64(328), 1183–1210.Google Scholar
Ferrara, E. (2017). Disinformation and social bot operations in the run up to the 2017 French presidential election. CoRR, abs/1707.00086. Retrieved from http://arxiv.org/abs/1707.00086Google Scholar
Gerken, H. (2009). The democracy index: Why our election system is failing and how to fix it. Princeton University Press.CrossRefGoogle Scholar
Golbeck, J., Grimes, J. M., & Rogers, A. (2010). Twitter use by the US Congress. Journal of the American Society for Information Science and Technology, 61(8), 1612–1621.Google Scholar
Graham, T., Jackson, D., & Broersma, M. (2016). New platform, old habits? Candidates’ use of Twitter during the 2010 British and Dutch general election campaigns. New Media & Society, 18(5), 765–783.Google Scholar
Groves, R. M., Presser, S., & Dipko, S. (2004). The role of topic interest in survey participation decisions. The Public Opinion Quarterly, 68(1), 2–31.CrossRefGoogle Scholar
Hall, J. L. (2008a). Improving the security, transparency, and efficiency of California’s 1% manual tally procedures. 2008 USENIX/ACCURATE Electronic Voting Technology Workshop. Retrieved from www.usenix.org/legacy/events/evt08/tech/full_papers/hall/hall.pdfGoogle Scholar
Hall, J. L. (2008b). Procedures for California’s 1% manual tally. Retrieved from https://josephhall.org/procedures/ca_tally_procedures-2008.pdf.Google Scholar
Hecht, B., Hong, L., Suh, B., & Chi, E. H. (2011). Tweets from Justin Bieber’s heart: The dynamics of the location field in user profiles. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 237–246). New York, NY, USA: ACM.Google Scholar
Hernández, M. A., & Stolfo, S. J. (1995). The merge/purge problem for large databases. ACM SIGMOD Record, 24, pp. 127–138.Google Scholar
Herzog, T. N., Scheuren, F., & Winkler, W. E. (2010). Record linkage. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5), 535–543.Google Scholar
Herzog, T. N., Scheuren, F. J., & Winkler, W. E. (2007). Data quality and record linkage techniques. Springer Science & Business Media.Google Scholar
Howard, P. N., Kollanyi, B., Bolsover, G., Bradshaw, S., & Neudert, L. -M. (2017). Junk news and bots during the US election: What were Michigan voters sharing over Twitter? COMPROP Data Memo, 3.Google Scholar
Ikawa, Y., Enoki, M., & Tatsubori, M. (2012). Location inference using microblog messages. In Proceedings of the 21st international conference on world wide web (pp. 687–690). New York, NY, USA: ACM.Google Scholar
Jaro, M. A. (1989). Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. Journal of the American Statistical Association, 84(406), 414–420.Google Scholar
Key, V. O. (1984). Southern politics in state and nation: A new edition. The University of Tennessee Press.Google Scholar
Kim, S., Schneider, S., & Alvarez, R. M. (2019). Evaluating the quality of changes in voter registration databases. American Politics Research. doi: 10.1177/1532673X19870512Google Scholar
King, G., Lam, P., & Roberts, M. E. (2017). Computer-assisted keyword and document set discovery from unstructured text. American Journal of Political Science, 61(4), 971–988.Google Scholar
Klasnja, M., Barberá, P., Beauchamp, N., Nagler, J., & Tucker, J. (2018). Measuring public opinion with social media data. In Atkeson, L. R. & Alvarez, R. M. (Eds.), The Oxford handbook of polling and survey methods. Oxford University Press.Google Scholar
Kumar, S., Morstatter, F., & Liu, H. (2015). Analyzing Twitter data. In Mejova, Y., Weber, I., & Macy, M. W. (Eds.), Twitter: A digital socioscope (pp. 21–51). Cambridge University Press.Google Scholar
Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. In Soviet Physics Doklady, 707–710.Google Scholar
Levin, I., Pomares, J., & Alvarez, R. M. (2016). Using machine learning algorithms to detect election fraud. In Alvarez, R. M. (Ed.), Computational social science: Discovery and prediction (pp. 266–294). Cambridge University Press.Google Scholar
Li, C., & Sun, A. (2014). Fine-grained location extraction from tweets with temporal awareness. In Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval (pp. 43–52). New York, NY, USA: ACM.Google Scholar
Lin, Y., Keegan, B., Margolin, D., & Lazer, D. (2013). Rising tides or rising stars? Dynamics of shared attention on Twitter during media events. CoRR, abs/1307.2785. Retrieved from http://arxiv.org/abs/1307.2785Google Scholar
Lindeman, M., & Stark, P. B. (2012). A gentle introduction to risk-limiting audits. IEEE Security and Privacy, Special Issue on Electronic Voting. Retrieved from https://www.stat.berkeley.edu/~stark/Preprints/gentle12.pdfGoogle Scholar
Liu, A., Srikanth, M., Adams-Cohen, N., Alvarez, R. M., & Anandkumar, A. (2019). Finding social media trolls: Dynamic keyword selection methods for rapidly-evolving online debates. doi: http://dx.doi.org/abs/1911.05332Google Scholar
McGee, J., Caverlee, J., & Cheng, Z. (2013). Location prediction in social media based on tie strength. In Proceedings of the 22nd ACM international conference on information & knowledge management (pp. 459–468). New York, NY, USA: ACM.Google Scholar
McKinney, S. M., Houston, J. B., & Hawthorne, J. (2013). Social watching a 2012 Republican presidential primary debate. American Behavioral Scientist, 58, 556–573.Google Scholar
Mebane, W. R. (2008). Election forensics: The second-digit Benford’s law test and recent American presidential elections. In Alvarez, R. M., Hall, T. E., & Hyde, S. D. (Eds.), Election fraud: Detecting and deterring electoral manipulation (pp. 162–181). Brookings Institution Press.Google Scholar
Mebane, W. R. (2011). Comment on “Benford’s law and the detection of election fraud.” Political Analysis, 19(3), 269–272.Google Scholar
Montgomery, J. M., Olivella, S., Potter, J. D., & Crisp, B. F. (2015). An informed forensics approach to detecting vote irregularities. Political Analysis, 23(4), 488–505. doi: 10.1093/pan/mpv023Google Scholar
Murthy, D. (2015). Twitter and elections: Are tweets, predictive, reactive, or a form of buzz? Information, Community & Society, 18(7), 816–831.Google Scholar
Myakgov, M., Ordeshook, P. C., & Shaikin, D. (2009). The forensics of election fraud. Cambridge University Press.CrossRefGoogle Scholar
Newcombe, H. B., Kennedy, J. M., Axford, S., & James, A. P. (1959). Automatic linkage of vital records. Science, 130, (3381) 954–959.CrossRefGoogle ScholarPubMed
O’Connor, B., Balasubramanyan, R., & Routledge, B. R. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the fourth international AAAI conference on weblogs and social media. Washington, DC: AAAI.Google Scholar
Orange County Registrar of Voters. (2018). Register to vote, or change your name, address or party. Retrieved from www.ocvote.com/registration/register-to-vote/Google Scholar
Orange County Registrar of Voters. (2019). Orange county registrar of voters 2018 risk limiting audit pilot project report. Retrieved from https://bit.ly/2YYTBbXGoogle Scholar
Roberts, M. E., Stewart, B. M., Tingley, D., et al. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58(4), 1064–1082. doi: 10.1111/ajps.12103Google Scholar
Rozenas, A. (2017). Detecting election fraud from irregularities in vote-share distributions. Political Analysis, 25(1), 41–56. doi: 10.1017/pan.2016.9Google Scholar
Sajuria, J., & Fabrega, J. (2016). Do we need polls? Why Twitter will not replace opinion surveys, but can complement them. In Snee, H., Hine, C., Morey, Y., Roberts, S., & Watson, H. (Eds.), Digital methods for social science: An interdisciplinary guide to research innovation (pp. 87–104). Palgrave Macmillan.Google Scholar
Särndal, C.-E., & Lundström, S. (2005). Estimation in surveys with nonresponse. John Wiley & Sons.Google Scholar
Schuman, H., & Presser, S. (1996). Questions and answers in attitude surveys: Experiments on question form, wording, and context. SAGE Publications.Google Scholar
Selker, T. (2005). Election auditing is an end-to-end procedure. Science, 308(5730), 1873–1874.Google Scholar
Stark, P. B. (2009). Risk-limiting postelection audits: Conservative p-values from common probability inequalities. IEEE Transactions on Information Forensics and Security, 4(4), 1005–1014.Google Scholar
Steinert-Threkeld, Z. C. (2018). Twitter as data. Cambridge University Press.Google Scholar
Theocharis, Y., Barberá, P., Fazekas, Z., Popa, S. A., & Parnet, O. (2016). A bad workman blames his tweets: The consequences of citizens’ uncivil Twitter. Journal of Communication, 66(6), 1007–1031.Google Scholar
Vallis, O., Hochenbaum, J., & Kejariwal, A. (2014). A novel technique for long-term anomaly detection in the cloud. In 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 14). Philadelphia, PA. Retrieved from www.usenix.org/conference/hotcloud14/workshop-program/presentation/vallisGoogle Scholar
Vosoughi, S. (2015). Automatic detection and verification of rumors on Twitter. Ph.D. Thesis, Massachusetts Institute of Technology.Google Scholar
Williams, V. (2018, November 29). Lawsuit by Abrams PAC continues debate over voter suppression in bitter Georgia governor’s race. The Washington Post. Retrieved from www.washingtonpost.com/politics/lawsuit-alleges-voter-suppression-in-bitter-georgia-governors-race-and-seeks-protections-for-future-races/2018/11/29/750afc20-f353-11e8-aeea-b85fd44449f5_story.htmlGoogle Scholar
Winkler, W. E. (1988). Using the em algorithm for weight computation in the Fellegi–Sunter model of record linkage. In Proceedings of the section on survey research methods, American Statistical Association (Vol. 667, p. 671).Google Scholar
Winkler, W. E. (1990). String comparator metrics and enhanced decision rules in the Fellegi–Sunter model of record linkage. In Proceedings of the Section on Survey Research Methods American Statistical Association. (pp. 354–359).Google Scholar
Winkler, W. E., & Thibaudeau, Y. (1991). An application of the Fellegi–Sunter model of record linkage to the 1990 US Decennial Census. US Bureau of the Census, 1–22.Google Scholar
Yancey, W. E. (2005). Evaluating string comparator performance for record linkage. Technical Report RR2005/05, US Bureau of the Census.Google Scholar
Zhang, M., Alvarez, R. M., & Levin, I. (2019). Election forensics: Using machine learning and synthetic data for possible election anomaly detection. PLoS ONE, 14. doi: http://dx.doi.org/10.1371/journal.pone.0223950Google Scholar

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