Nearly a quarter of web content shared on Twitter by users in the battleground state of Michigan during the final days of last year’s US election campaign was so-called fake news, according to a University of Oxford study. Researchers at the Oxford Internet Institute (OII) also determined that these users shared approximately as many fake news items as “professional news” over the same period.
The report, published on Monday, concludes that links to fake news stories accounted for 23 per cent of the links tweeted by a sample of 140,000 Michigan-based users during the ten days up to November 11 last year.
A second OII report, also published on Monday, stated that the share rate of fake news was only 10 per cent among users tweeting about the recent German presidential election between February 11 and 13. Moreover, professional news was shared at almost double the rate it had been in the Michigan study — 45 per cent.
Primary Sources: Read the Complete Research Articles From the Oxford Internet Institute:
Computational propaganda distributes large amounts of misinformation about politics and public policy over social media platforms. The combination of automation and propaganda can significantly impact public opinion during important policy debates, elections, and political crises. We collected data on automation and junk news using major hashtags related to politics in the state of Michigan in the lead up to the 2016 US Presidential Election. (1) In Michigan, conversation about politics over Twitter mirrored the national trends in that Trump related hashtags were used more than twice as often as Clinton-related hashtags. (2) Social media users in Michigan shared a lot of political content, but the amount of professionally researched political news and information was consistently smaller than the amount of extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news. (3) Not only did such junk news “outperform” real news, but the proportion of professional news content being shared hit its lowest point the day before the election.
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Computational propaganda distributes large amounts of misinformation about politics and public policy over social media platforms. The combination of automation and propaganda can significantly impact public opinion during important policy debates, elections, and political crises. We collected data on bot activity and junk news using a set of hashtags related to the German Federal Presidency Elections in February 2017. We find that (1) traffic about the far-right Alternative für Deutschland and their candidate Albrecht Glaser accounted for a surprisingly large portion of Twitter activity given their share of voter support. (2) Overall, the impact of political bots was minor, with highly automated accounts generating a small fraction of the Twitter traffic about the election. (3) Social media users in Germany shared many links to political news and information, and the ratio of professional news to junk news shared by German Twitter users was 4 to 1.
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