Academic researchers have responded by trying to understand the scope of the problem, identifying the most misinformation-filled social media networks, organized government efforts to spread false information, and even prominent individuals who are the sources of misinformation.
While you might expect these to be young, Internet-savvy individuals who automate their sharing, it turns out this population tends to be older, female, and very, very prone to clicking the “retweet” button.
The work, done by Sahar Baribi-Bartov, Briony Swire-Thompson, and Nir Grinberg, relies on a panel of over 650,000 Twitter accounts that have been associated with voting registrations in the US, using full names and location information.
The researchers first identified tweets made by these users, which contain political content, using a machine-learning classifier that had previously been validated by having its calls checked by humans.
From this population, Baribi-Bartov, Swire-Thompson, and Grinberg identify just 2,107 accounts that are responsible for 80 percent of the tweets linking to sources of misinformation.
For the analyses they perform, the superspreaders are compared to a random sample of the total population and the heaviest sharers of links to reliable news sources.
The original article contains 472 words, the summary contains 187 words. Saved 60%. I’m a bot and I’m open source!
This is the best summary I could come up with:
Academic researchers have responded by trying to understand the scope of the problem, identifying the most misinformation-filled social media networks, organized government efforts to spread false information, and even prominent individuals who are the sources of misinformation.
While you might expect these to be young, Internet-savvy individuals who automate their sharing, it turns out this population tends to be older, female, and very, very prone to clicking the “retweet” button.
The work, done by Sahar Baribi-Bartov, Briony Swire-Thompson, and Nir Grinberg, relies on a panel of over 650,000 Twitter accounts that have been associated with voting registrations in the US, using full names and location information.
The researchers first identified tweets made by these users, which contain political content, using a machine-learning classifier that had previously been validated by having its calls checked by humans.
From this population, Baribi-Bartov, Swire-Thompson, and Grinberg identify just 2,107 accounts that are responsible for 80 percent of the tweets linking to sources of misinformation.
For the analyses they perform, the superspreaders are compared to a random sample of the total population and the heaviest sharers of links to reliable news sources.
The original article contains 472 words, the summary contains 187 words. Saved 60%. I’m a bot and I’m open source!