Why polls fail to predict elections

Author:

Zhou Zhenkun,Serafino Matteo,Cohan Luciano,Caldarelli Guido,Makse Hernán A.ORCID

Abstract

AbstractIn the past decade we have witnessed the failure of traditional polls in predicting presidential election outcomes across the world. To understand the reasons behind these failures we analyze the raw data of a trusted pollster which failed to predict, along with the rest of the pollsters, the surprising 2019 presidential election in Argentina. Analysis of the raw and re-weighted data from longitudinal surveys performed before and after the elections reveals clear biases related to mis-representation of the population and, most importantly, to social-desirability biases, i.e., the tendency of respondents to hide their intention to vote for controversial candidates. We propose an opinion tracking method based on machine learning models and big-data analytics from social networks that overcomes the limits of traditional polls. This method includes three prediction models based on the loyalty classes of users to candidates, homophily measures and re-weighting scenarios. The model achieves accurate results in the 2019 Argentina elections predicting the overwhelming victory of the candidate Alberto Fernández over the incumbent president Mauricio Macri, while none of the traditional pollsters was able to predict the large gap between them. Beyond predicting political elections, the framework we propose is more general and can be used to discover trends in society, for instance, what people think about economics, education or climate change.

Funder

Special Fund for Fundamental Scientific Research of the Beijing Colleges in CUEB

HUMANE-AI-NET

Publisher

Springer Science and Business Media LLC

Subject

Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems

Reference46 articles.

1. Tourangeau R, Conrad FG, Couper MP. The science of web surveys. New York: Oxford University Press; 2013.

2. Kennedy C, Blumenthal M, Clement S, Clinton JD, Durand C, Franklin C, McGeeney K, Miringoff L, Olson K, Rivers D, et al. An evaluation of the 2016 election polls in the United States. Public Opin Q. 2018;82(1):1–33.

3. Durand C, Blais A. Quebec 2018: a failure of the polls? Can J Polit Sci/Revue Canadienne de Science Politique. 2020;53(1):133–50.

4. Duncan P, The Guardian. How the pollsters got it wrong on the EU referendum. 2016. https://www.theguardian.com/politics/2016/jun/24/how-eu-referendum-pollsters-wrong-opinion-predict-close. Accessed 14 Oct 2021.

5. Cohn N. The Upshot, New York Times. Why Polls Have Been Wrong Recently. 2016. https://www.nytimes.com/2016/01/08/upshot/why-polls-have-been-wrong-recently.html. Accessed 14 Oct 2021.

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3