Twitter labels 300,000 misleading tweets

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Twitter labels 300,000 misleading tweets



Social media giant, Twitter, said it labelled approximately 300,000 tweets for disputed and “potentially misleading” contents about the U.S. elections over the last two weeks.
Twitter’s Head of Legal, Policy and Trust, Vijaya Gadde, and its Head of Product, Kayvon Beykpour, disclosed this in a statement on Thursday evening.
According to Messrs Gadde and Beykpour, the affected tweets represent 0.2 per cent of no fewer than 150 million U.S. election-related messages sent on the platform during the two-week period.
“More than a year ago, the public told us they wanted Twitter to offer context on misleading information.
“This is the exact approach we’ve implemented on Tweets about COVID-19, synthetic and manipulated media, and the 2020 U.S. election that could contribute to offline harm.

“We continue to apply labels to add context and limit the risk of harmful election misinformation spreading without important context,” they said.
The officials said 456 of the affected tweets received warning messages and had their engagement features limited during the period between Oct. 27 and Nov. 11.
They said the enforcement actions were part of ongoing efforts to limit the spread of misleading information about election processes around the world on Twitter.
U.S. President Donald Trump is one of those affected by the company’s enforcement actions.
Mr Trump’s tweets on unsubstantiated claims about voter fraud in the election have been repeatedly labelled or covered by the company.
“We remain vigilant and will continue working to protect the integrity of the election conversation on Twitter.
“As we’ve done for many elections around the world, we will produce a longer-form retrospective of all of our work around the 2020 U.S. Election in early 2021.“We will continue to research, question, and change features that may encourage behaviours on Twitter that negatively affect the health of the public conversation,” Messrs Gadde and Beykpour added.
(NAN)



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