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The story behind the story

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Study of 225 pieces of COVID-19-related content rated false or misleading: 59% remain up with no warning label on Twitter, 27% on YouTube, and 24% on Facebook

Facebook and YouTube do better, but still leave some misinformation up  —  More than half of the misinformation …

Washington Post Craig Timberg

Context & Ripple Effects

A month after reporting that coronavirus misinformation was persisting across all three platforms despite their partnerships with health organizations (the March New York Times account), the Post now has numbers: of 225 flagged COVID-19 items, 59% remain up with no warning label on Twitter, 27% on YouTube, and 24% on Facebook.

The ranking matters because each company had publicly framed its response as working — the study converts that framing into a measurable enforcement gap, with Twitter the clear laggard among the three.

First-order effects

  • Twitter faces the most direct exposure: nearly six in ten flagged items stayed up unlabeled, undercutting its stated collaboration with health authorities and inviting scrutiny of its labeling pipeline.
  • Facebook and YouTube can point to comparatively better rates, but both still leave roughly a quarter of flagged content untouched, so none of the three can claim clean hands on the same dataset.

Second-order effects

  • Fact-checking groups gain ammunition to push from spot audits to systematic monitoring — the approach Avaaz later applied when it found 56% of fact-checked COVID misinfo in major non-European languages unactioned by Facebook (its European-language audit).
  • Attention shifts toward high-reach accounts rather than volume alone, anticipating the 'superspreader' framing NewsGuard used when it found Twitter failing to act on large verified accounts (the NewsGuard report) and Facebook's later dispute over twelve such accounts.

Third-order effects

  • If the pattern holds, platform moderation becomes a permanent measurement battleground: independent audits quantify enforcement gaps, platforms dispute the methodology, and the cycle repeats across topics — as later label reviews of midterm candidates' false claims showed the same Twitter-Facebook shortfalls persisting years on (the midterms label review).
  • Engagement economics keep the incentive structure tilted against removal: the NYU finding that known misinfo publishers drew six times more engagement than sources like WHO suggests the gap is not just capacity but business model.

The trend: Platform content moderation is converging on an audit-driven accountability loop in which independent studies measure enforcement gaps faster than the platforms close them, making moderation performance a recurring public metric rather than a one-off controversy.

Discussion

  • @_felixsimon_ @_felixsimon_ on x
    And, I should add, it takes the effort of ordinary people. We can all make a difference. - If you see misinformation doing the rounds in your WhatsApp group, etc. let ppl. know that it's false - Be kind & assume that ppl. didn't mean harm - Provide a fact check, if possible
  • @rasmus_kleis Rasmus Kleis Nielsen on x
    What are some of the main formats, sources, and claims of #COVID19 misinformation, and how have platforms responded? New @risj_oxford research out, lead author @jsbrennen, analyzing sample of 225 pieces Full factsheet here: https://reutersinstitute.politics.ox.ac .uk/ ... Key fin…
  • @marklittlenews Mark Little on x
    The false news virus is incubated on the margins of the internet. But it takes mainstream influencers to spread it far and wide. https://twitter.com/...
  • @jason_kint @jason_kint on x
    interesting @risj_oxford paper on misinformation and covid-19 just dropped. 88% of the samples analyzed were on social media and many still aren't labeled (24% on FB, 27% - YouTube, 59% - Twitter). /1 https://reutersinstitute.politics.ox.ac .uk/ ...
  • @jason_kint @jason_kint on x
    Anyway, that's it for now. Have a read of their presentation of the findings. Sadly, it's likely early in the COVID disinfo cycle but very, very real. will be looking out for thoughts from @noUpside, @selectedwisdom, other experts. /end https://reutersinstitute.politics.ox.ac .uk…
  • @marklittlenews Mark Little on x
    Misinformation often defined by manipulation rather than outright lies. New report from @risj_oxford says 59% of COVID-19 misinfo examined was “existing, often true information spun, twisted, recontextualised”. Just 38% completely fabricated. https://reutersinstitute.politics.ox.…
  • @oiioxford @oiioxford on x
    New factsheet from @oiioxford and @risj_oxford on #COVID19 misinformation featured in Washington Post. Full report: https://reutersinstitute.politics.ox.ac .uk/ ... On Twitter, almost 60 percent of false claims about coronavirus remain online — without a warning label https://www…