See our latest report, (survey) experiments with vaccine messaging. In first experiment, we varied the messenger-the question, what impact did this have on vaccine resistance? Treatment effects below (higher = more vaccine resistance).1/7
COVIDstates.org Image
Take away: lots of potential for a backfire here. Celebrities and athletes have null effects, and anyone with a political hue risks driving away anti-partisans more than they persuade same partisans. (See this figure for partisan breakdowns). 2/7 Image
Fauci is an illuminating case, because he has become a partisan figure, and evoking him makes Democrats a bit less resistant, but Republicans and Independents substantially more resistant, with a net effect of increasing resistance. 3/7
But, if we vary message type, we find substantial treatment effects to reduce resistance, especially if own doctor or "scientists" recommend. Interestingly, appeals from people you know and to preventing harm more effective in counties with more cases. 4/7 Image
Not shocking, and it does highlight, especially, the role that community embedded professionals will play in persuading people to be vaccinated. 5/7
All the usual caveats re the generalizability of survey experiments/etc. 6/7

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More from @davidlazer

11 Aug 20
What is the relationship between the pandemic and the protests? Check out our latest 50 state survey examining the relationship between the size of the protests and the pandemic.

covidstates.org

A few key take aways (1/n):
(1) The protests were BIG: about 5% of respondents participated in the protests, & even in the states with the fewest protesters, about 2% of adults reported participating. Truly historic in terms of scale and geographic scope (2/n)
(2) The protests, unsurprisingly, heavily tilted young, with a remarkable ~10% of people in their 20s participating, and ~1% of people older than 60. 8.5% of African Americans, 6.3% of Hispanics, and 4.0% of whites and Asian Americans participated (3/n)
Read 6 tweets
24 Jan 19
Happy to announce our paper, just out this minute in @sciencemagazine, which examines the prevalence of Fake News on Twitter during the 2016 election
w/ @grinbergnir @Ldfriedl @_kenny_joseph @Briony_Swire science.sciencemag.org/content/363/64…
1/N
Some of our key findings:

(1) Fake news was moderately prevalent during the election. About 5% of election related content people were exposed to was fake news.
2/N
(2) Exposure was highly concentrated among a few people. The large majority of the fake news content was in the feeds of 1% of our sample.
3/N
Read 8 tweets

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