Megan A. Brown
Megan A. Brown is a PhD student at the School of Information at the University of Michigan and a Graduate Research Affiliate at NYU’s Center for Social Media and Politics.
Her research is centered on the online information ecosystem. She studies political bias in algorithmic systems, the effect of platform governance and moderation policies on the spread of political content, and research infrastructure to support computational social science. Formerly, Megan was a Senior Research Engineer at CSMaP, where she collected and maintained large-scale collections of social media and digital trace data for social science research. She received her Bachelor of Arts in Politics with a minor in Middle East & Islamic Studies from New York University in 2019.
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Related Research & News
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Journal Article
To Moderate, or Not to Moderate: Strategic Domain Sharing by Congressional Campaigns
Electoral Studies, 2025
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Working Paper
Web Scraping for Research: Legal, Ethical, Institutional, and Scientific Considerations
Working Paper, December 2024
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Working Paper
Reaching Across the Political Aisle: Overcoming Challenges in Using Social Media for Recruiting Politically Diverse Respondents
Working Paper, August 2024
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Journal Article
Digital Town Square? Nextdoor's Offline Contexts and Online Discourse
Journal of Quantitative Description: Digital Media, 2024
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Book
Online Data and the Insurrection
Media and January 6th, 2024
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Journal Article
Estimating the Ideology of Political YouTube Videos
Political Analysis, 2024
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Policy
The Problem with TikTok’s New Researcher API is Not TikTok
While TikTok’s Researcher API is a promising step in the right direction, mandated data requirements are paramount for ensuring that these lines of research continue.
March 1, 2023
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Analysis
Echo Chambers, Rabbit Holes, and Ideological Bias: How YouTube Recommends Content to Real Users
We find that YouTube’s recommendation algorithm does not lead the vast majority of users down extremist rabbit holes, although it does push users into increasingly narrow ideological ranges of content in what we might call evidence of a (very) mild ideological echo chamber.
October 13, 2022
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Journal Article
Election Fraud, YouTube, and Public Perception of the Legitimacy of President Biden
Journal of Online Trust and Safety, 2022
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Analysis
Republicans Are Increasingly Sharing Misinformation
Republican candidates have dramatically increased how much they share from unreliable sources in just two years.
August 29, 2022
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Working Paper
Echo Chambers, Rabbit Holes, and Algorithmic Bias: How YouTube Recommends Content to Real Users
Working Paper, May 2022
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Analysis
Gender-Based Online Violence Spikes After Prominent Media Attacks
Our research finds that after a prominent male media personality targets a female journalist, the prevalence of hateful speech targeting those journalists increases in the immediate aftermath, often taking days to decrease.
January 26, 2022
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Analysis
Twitter Banned Marjorie Taylor Greene. That May Not Hurt Her Much.
She’s gaining followers and ‘likes’ on other social media platforms, our research finds.
January 14, 2022
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Analysis
Trendless Fluctuation? How Twitter’s Ethiopia Interventions May (Not) Have Worked
Twitter’s decision to deactivate trending topics in Ethiopia did not reduce the volume or toxicity of tweets about the civil war.
January 11, 2022
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Working Paper
Network Embedding Methods for Large Networks in Political Science
Working Paper, November 2021
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Analysis
Twitter Amplifies Conservative Politicians. Is it Because Users Mock Them?
Our research suggests conservative politicians are ‘ratioed’ more often, which may explain why they’re in your timeline.
October 27, 2021
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Journal Article
SARS-CoV-2 RNA Concentrations in Wastewater Foreshadow Dynamics and Clinical Presentation of New COVID-19 Cases
Science of the Total Environment, 2022
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Journal Article
Twitter Flagged Donald Trump’s Tweets with Election Misinformation: They Continued to Spread Both On and Off the Platform
Harvard Kennedy School (HKS) Misinformation Review, 2021
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Analysis
Do Twitter Warning Labels Work?
Twitter put warning labels on hundreds of thousands of tweets. Without a hard block, tweets continue to spread — especially tweets by President Trump.
December 9, 2020
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News
Text Classification Using a Transformer-Based Model
We're committed to supporting open and accessible science, which includes promoting the creation and use of open-source software, providing high-quality replication materials for our publications, and contributing to existing open-source tools and frameworks. To do so, we created an open-source tool to make using transformers easier and explain how to use it here.
December 8, 2020
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