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VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:CAM Colloquium & CDSES Distinguished Lecture - Johan Ugander\,
  Management Science & Engineering\, Stanford University
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260515T053339Z
UID:tag:localist.com\,2008:EventInstance_47445051109494
DTSTART:20241206T204500Z
DTEND:20241206T214500Z
DESCRIPTION:Jointly hosted by CAM\, CDSES\, and ORIE\n \n\nTitle: Bridging-
 based fact-checking moderates the diffusion of false information on social
  media\n\nAbstract: Social networks scaffold the diffusion of information 
 on social media. Much attention has been given to the spread of true vs. f
 alse content on social media\, including the structural differences betwee
 n their diffusion patterns. However\, much less is known about how platfor
 m interventions on false content alter the diffusion of such content. In t
 his work\, we estimate the causal effects of a novel fact-checking feature
 \, Community Notes\, adopted by Twitter (now X) to solicit and vet crowd-s
 ourced fact-checking notes for false content. An important aspect of this 
 feature is its use of a bridging-based decision algorithm whereby fact-che
 cking notes are shown only if they are seen as broadly informative and hel
 pful by users from across the political spectrum. To estimate the causal e
 ffect of bridging-based fact-checking\, we gather detailed time series dat
 a for 40\,000 posts for which notes have been proposed and use synthetic c
 ontrol methods to produce counterfactual estimates of a range of diffusion
 -based outcomes. We find that attaching fact-checking notes significantly 
 reduced the reach of and engagement with false content. In reducing reach\
 , we observe that diffusion trees for fact-checked content are less deep\,
  but not less broad\, than synthetic control estimates for non-fact-checke
 d content with similar reach. This finding contrasts notably with differen
 ces between false vs. true content\, where false information diffuses fart
 her\, but with structural patterns that are otherwise indistinguishable fr
 om those of true information\, conditional on reach.\n\nBio: Johan Ugander
  is an Associate Professor at Stanford University in the Department of Man
 agement Science & Engineering\, within the School of Engineering. His rese
 arch develops algorithmic and statistical frameworks for analyzing social 
 networks\, social systems\, and other large-scale social and behavioral da
 ta. Prior to joining the Stanford faculty he was a postdoctoral researcher
  at Microsoft Research Redmond 2014-2015 and held an affiliation with the 
 Facebook Data Science team 2010-2014. He obtained his Ph.D. in Applied Mat
 hematics from Cornell University in 2014. His awards include a NSF CAREER 
 Award\, a Young Investigator Award from the Army Research Office (ARO)\, s
 everal Best Paper Awards\, and the 2016 Eugene L. Grant Undergraduate Teac
 hing Award from the Department of Management Science & Engineering.
GEO:42.443451;-76.481506
LOCATION:Frank H. T. Rhodes Hall\, 655
SUMMARY:CAM Colloquium & CDSES Distinguished Lecture - Johan Ugander\, Mana
 gement Science & Engineering\, Stanford University
URL;VALUE=URI:https://events.cornell.edu/event/cam-colloquium-johan-ugander
 -management-science-engineering-stanford-university
CATEGORIES:Colloquium
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