Twitter Users Matched to Voter Registration for COVID-19 Polarization Analysis

Late 2020 data from over 50 million tweets analyzing partisan polarization in discussions about COVID-19 lockdowns, masks, and vaccines across the United States and Canada. The dataset was created by Zachary Yang and last updated on April 20, 2026. It likely contains user-level data linking Twitter activity to political affiliation.

Use Cases

• Modeling the correlation between regional vaccination rates and online polarization based on the described negative relationship.
• Analyzing temporal spikes in partisan discourse linked to specific political events mentioned in the description.
• Comparing cross-national polarization dynamics between the U.S. and Canada as highlighted in the study.
• Investigating geographic heterogeneity in polarization patterns across conservative and liberal states.

Strengths

• Analysis is based on a large corpus of over 50 million tweets.
• Covers a critical temporal period during the early COVID-19 pandemic in late 2020.
• Includes cross-national comparison between the United States and Canada.
• Explicitly links online polarization to measurable public health outcomes like vaccination rates.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data reflects the discourse of politically engaged Twitter users, which may not be representative of the general population.

Provenance

Source

figshare

Collection Method

Computational analysis of Twitter data, with users matched to voter registration.

Time Range

Late 2020

Freshness

Last updated 2026-04-20 17:58:14; freshness should be verified.

Geography

United States and Canada