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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

  • We asked Americans what they’d heard about Trump and Harris throughout the campaign. Here’s what they told us
  • What Americans heard about Trump heading into the campaign’s final week: fascism and french fries
  • A survey asked what Americans were hearing most about the candidates. One of the most common words for Trump was ‘liar’
  • How hurricane season is affecting the way Americans follow the Trump-Harris race
  • Harris media blitz and Trump campaign rallies capture Americans’ attention

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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.