Abstract Presidential elections can be forecast using information from political and economic conditions, polls, and a statistical model of changes in public opinion over time. However, these “knowns” about how to make a good presidential election forecast come with many unknowns due to the challenges of evaluating forecast calibration and communication. We highlight how incentives may shape forecasts, and particularly forecast uncertainty, in light of calibration challenges. We illustrate these challenges in creating, communicating, and evaluating election predictions, using the Economist and Fivethirtyeight forecasts of the 2020 election as examples, and offer recommendations for forecasters and scholars.
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Assessing Candidate Preference through Web Browsing History
Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preference—including the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of voters’ candidate preferences in ways that are difficult
to achieve using polls.
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- Award ID(s):
- 1703592
- NSF-PAR ID:
- 10096153
- Date Published:
- Journal Name:
- 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Page Range / eLocation ID:
- 158 to 167
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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