Twitter tests labels, community moderation for lies by public figures
Twitter users could earn points and badges for rating "harmfully misleading" content.
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(Reuters) - Twitter Inc said on Thursday that it was testing a new community moderation approach that would enable users to identify misleading information posted by politicians and public figures and add brightly colored labels under those tweets.
NBC News earlier reported that Twitter company documents showing a mockup of the new approach were accessible on a publicly available website, although it did not identify the site.
The company said that the leaked illustrations showed one possible iteration of a new approach to combat misinformation.
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“We’re exploring a number of ways to address misinformation and provide more context for Tweets on Twitter,” Twitter said in a statement. “This is a design mockup for one option that would involve community feedback. Misinformation is a critical issue and we will be testing many different ways to address it.”
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A spokesman said the project was in the early stages and not currently staffed.
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The mockup obtained by NBC News showed a plan to develop a “community reports” feature, which Twitter likened to Wikipedia. Twitter users would earn points and badges for rating “harmfully misleading” content.
(REUTERS/Dado Ruvic)
Twitter and other social media companies are under intense pressure to stem lies and misinformation on their platforms.
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They have introduced measures in recent months designed to head off interference in the U.S. presidential election in November, although critics say they are insufficient compared to the scale of the problem.
Twitter announced in the summer that it would label and de-emphasize tweets that broke its rules but came from important public figures like politicians. But it has not yet done so for any tweets.
The company also banned political ads in the fall and said earlier this month that it would apply “false” warning labels to tweets containing synthetic or deceptively edited forms of media and remove any that are likely to cause harm.