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Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams

Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams

by CMT Admin | Dec 8, 2020 | Misinformation

This article investigates the prevalence of high and low quality URLs shared on Twitter when users discuss COVID-19. Read more in the Journal of Computational Social Science.
Misinformation and the Impact of Social Media in Elections

Misinformation and the Impact of Social Media in Elections

by CMT Admin | Oct 8, 2020 | Book, Election 2016, Election 2020, Misinformation

Lisa Singh and Ceren Budak presented virtually for an event hosted by the McCourt school’s Massive Data Institute and the Institute of Politics and Public Service (GU Politics) about the impact of misinformation in the 2016 election and what it could mean for...
Twitter and COVID-19: Preliminary Study Finds Information and Misinformation Equally Shared

Twitter and COVID-19: Preliminary Study Finds Information and Misinformation Equally Shared

by CMT Admin | Apr 3, 2020 | Misinformation

Read more on Georgetown’s McCourt School of Public Policy website.
Event: Ceren Budak speaks at U-M Dissonance Event Series on “Catching Fake News”

Event: Ceren Budak speaks at U-M Dissonance Event Series on “Catching Fake News”

by Jule Krüger | Nov 30, 2018 | Misinformation, Uncategorized

On November 27, 2018, S3Mc’s Ceren Budak participated in a panel on fake news organized by the U-M Dissonance Event Series.

Recent Posts

  • Harris’ border visit and economy speech capture attention as warning signs emerge for campaign
  • What’s the latest news about the Trump-Harris race? It depends on who you ask
  • How the Trump-Harris debate dominated Americans’ political conversation and elevated false claims about migrants
  • Here’s what the conversation around Harris and Trump looks like among Americans ahead of the debate
  • Polling data shows Trump campaign’s difficulty in framing the conversation around Kamala Harris

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The Social Science and Social Media Collaborative is funded by the Michigan Institute for Data Science, National Science Foundation grants #1934925 and #1934494, and the McCourt School’s Massive Data Institute.