Spikes and Variance
Using Google Trends to Detect and Forecast Protests
Bibliographic Data
| ID | 7971027 |
|---|---|
| Authors | Joan C Timoneda (0000-0001-7057-872X, Purdue University West Lafayette, corresponding author), Erik Wibbels (0009-0007-6723-3414, Duke University) |
| Year | 2022 |
| Volume | 30 |
| Issue | 1 |
| Pages | 1-18 |
| Publication date | 2022-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/pan.2021.7 |
| OpenAlex | W3154644855 |
| Language | EN |
| Citations received | 9 |
| References cited | 42 |
Google search is ubiquitous, and Google Trends (GT) is a potentially useful access point for big data on many topics the world over. We propose a new ‘variance-in-time’ method for forecasting events using GT. By collecting multiple and overlapping samples of GT data over time, our algorithm leverages variation both in the mean and the variance of a search term in order to accommodate some idiosyncracies in the GT platform. To elucidate our approach, we use it to forecast protests in the United States. We use data from the Crowd Counting Consortium between 2017 and 2019 to build a sample of true protest events as well as a synthetic control group where no protests occurred. The model’s out-of-sample forecasts predict protests with higher accuracy than extant work using structural predictors, high frequency event data, or other sources of big data such as Twitter. Our results provide new insights into work specifically on political protests, while providing a general approach to GT that should be useful to researchers of many important, if rare, phenomena
Big data · Data mining · Data science · Econometrics · Economics · Event (particle physics · Extant taxon · Sample (material · Variance (accounting · Variation (astronomy · Work (physics · Computer Science · Data-Driven Disease Surveillance · Engineering · Mathematics · Media Influence and Politics · Misinformation and Its Impacts
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Capturing the Fourth Estate
Understanding the impact of the 2018 voter ID pilots on turnout at the London local elections
The Effect of the 2020 Black Lives Matter Protests on Police Budgets
The Parable of Google Flu
Forecasting private consumption
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A re-assessment of reporting bias in event-based violence data with respect to cell phone coverage
Precedents, Progress, and Prospects in Political Event Data
Estimating Grouped Data Models with a Binary-Dependent Variable and Fixed Effects via a Logit versus a Linear Probability Model
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Birds of the Same Feather Tweet Together
Can Structural Conditions Explain the Onset of Nonviolent Uprisings
Predicting Conflict in Space and Time
Predicting Armed Conflict, 2010-20501
Machine Coding of Event Data Using Regional and International Sources
An Automated Information Extraction Tool for International Conflict Data with Performance as Good as Human Coders
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A Global Model for Forecasting Political Instability
A Closer Look at Reporting Bias in Conflict Event Data
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The perils of policy by p-value
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Ethnicity, Insurgency, and Civil War
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| Unique citing works | 9 |
|---|---|
| Citations per year | 2,25 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |