Twitter could label politicians’ lies via community fake news cops
Twitter is testing new radical new features designed to combat the spread of fake news and misleading information, while drawing another thick line in the sand between it and Facebook.
An NBC News report detailing a ‘leaked demo of new features’, shows Twitter could soon place highly-visible labels on tweets from public figures and politicians if they’re believed to contain lies or misinformation.
The warnings will be community sourced, according to the report, with Twitter users earning points and badges if they “contribute in good faith and act like a good neighbour” and “provide critical context to help people understand information they see.”
If the community deems a tweet ‘harmfully misleading’, for example, a bright orange or red label will be placed beneath it, explaining that the tweet’s visibility will be reduced. Top reports from the community detailing why the tweet could be considered as such will sit below the tweet. Other users will also be invited to participate.
In screenshots from the report, a tweet from Bernie Sanders, the potential democratic nominee for president is shown, while misinformation about the coronavirus is also called out by the community.
“We’re exploring a number of ways to address misinformation and provide more context for tweets on Twitter,” a Twitter spokesperson said. “Misinformation is a critical issue and we will be testing many different ways to address it.”
The tools could come into effect as soon as March 5, according to the NBC News report.
If you, like us, were thinking that the system could be abused to devalue the tweets of those with conflicting opinions, Twitter believes the points-based system will discourage the trolls, rather than encourage them. The more points earned by a user, the more their vote will count towards the tweet being labelled.
Facebook, of course, has controversially refused to police the factual nature of statements and advertisements from leading politicians citing freedom of speech reasons.