Mapping the Infodemic: Geolocating Reddit Users and Unsupervised Topic Modeling of COVID-19-Related Misinformation

The problem of geolocating Reddit users without access to the author information API is tackled in this study. Using subreddit data, we analyzed and identified user location based on their interactions within location-specific subreddits. Using unsupervised learning methods such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) algorithms, we examined conversations about COVID-19 and immunization across the U.S., focusing on COVID-19 vaccination. Our topic modeling identifies four themes: humor and sarcasm (e.g., jokes about microchips), conspiracy theories (e.g., tracking devices and microchips in the COVID-19 vaccine), public skepticism (e.g., debates over vaccine safety and freedom), and vaccine brand concerns (e.g., Pfizer, Moderna, and booster shots). Our geolocation analysis shows that regions with lower vaccination rates often exhibit a higher prevalence of misinformation-labeled comments. For example, counties such as Ada County (Idaho), Newton County (Missouri), and Flathead County (Montana) showed both a low vaccine uptake and a high rate of false information. This study provides useful information on the many different examples of misinformation that are disseminated online. It gives us a better understanding of how people in different parts of the U.S. think about getting a COVID-19 vaccine.

Authors

Institutions

Publication Details

Journal
Information
Published
2025-08-28
DOI
https://doi.org/10.3390/info16090748
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mapping the Infodemic: Geolocating Reddit Users and Unsupervised Topic Modeling of COVID-19-Related Misinformation

Zeynep Ertem, Jeremy Blackburn, Maaz Amjad, Jay Patel et al.
Information
Misinformation and Its Impacts
article

Mapping the Infodemic: Geolocating Reddit Users and Unsupervised Topic Modeling of COVID-19-Related Misinformation

Zeynep Ertem, Jeremy Blackburn, Maaz Amjad, Jay Patel, Lulu Alarfaj
article en

Abstract

The problem of geolocating Reddit users without access to the author information API is tackled in this study. Using subreddit data, we analyzed and identified user location based on their interactions within location-specific subreddits. Using unsupervised learning methods such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) algorithms, we examined conversations about COVID-19 and immunization across the U.S., focusing on COVID-19 vaccination. Our topic modeling identifies four themes: humor and sarcasm (e.g., jokes about microchips), conspiracy theories (e.g., tracking devices and microchips in the COVID-19 vaccine), public skepticism (e.g., debates over vaccine safety and freedom), and vaccine brand concerns (e.g., Pfizer, Moderna, and booster shots). Our geolocation analysis shows that regions with lower vaccination rates often exhibit a higher prevalence of misinformation-labeled comments. For example, counties such as Ada County (Idaho), Newton County (Missouri), and Flathead County (Montana) showed both a low vaccine uptake and a high rate of false information. This study provides useful information on the many different examples of misinformation that are disseminated online. It gives us a better understanding of how people in different parts of the U.S. think about getting a COVID-19 vaccine.

InformationVol. 16(9)
Texas Tech University (US), Binghamton University (US)
Openalex Percentile: Top 69%
Misinformation and Its Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.