A team from the Shanghai Institute of Technology sought to study whether accuracy made any difference to whether a post goes viral on social media. They cited a concern about “the digital misinformation that threatens our democracy”:
The paper found that even though individuals may prefer to read and share “quality information”, factors such as “information overload and limited attention” contributed to “a degradation of the market’s discriminative power”.
In other words, Qiu and colleagues concluded, quality material and the rate at which it spreads across the internet “reveals a weak correlation”. Low quality material – fake news, complete rubbish – is just as likely to go viral as the good stuff. Andrew Masterson, “Fake news journal paper revealed as fake news” at Cosmos
Their June 2017 study retracted by Nature earlier this month, had been quoted widely, due to widespread concern about the risk that “fake news” skews election results.
But according to Retraction Watch, the authors asked for the retraction on the grounds of both flawed data and a software glitch. Correct numbers caused their findings to collapse. “They found that high quality memes tended to spread far more often and more broadly than low quality ones – the exact opposite of the published results.” Cosmos
RetractionWatch, a site that reports on science data gone bad, quotes a researcher who commends his colleagues for retracting the article. RetractionWatch concludes,
Not surprisingly, the article, which appeared in Nature Human Behaviour, received considerable media attention, including coverage in Smithsonian, Ars Technica and many other outlets. And it has continued to serve as fodder for articles about the spread of lies in cyberspace…
So, while it may indeed be “true” that a lie is halfway around the world before the truth gets its pants on, we still don’t know why. Adam Marcus, “Oft-quoted paper on spread of fake news turns out to be…fake news” at Retraction Watch
Intuitively, most of us would expect the researchers’ corrected outcome to be more likely than their original one. False or doubtful information can be exciting. But, once its uncertain status is known, those who continue to disseminate it are classed as unreliable sources. Thus, doubtful news is dropped whereas confirmed news continues to circulate. This would hold as true for social media today as for a company cafeteria in the 1970s.
While fake news domains were widely believed to heavily influence the US 2016 election, researchers who define fake news as “ knowingly false or misleading content created largely for the purpose of generating ad revenue” reported earlier this month that “sharing this content was a relatively rare activity”: “The vast majority of Facebook users in our data did not share any articles from fake news domains in 2016 at all.”
The recent research is consistent with experience and previous research. It’s never been clear that most people literally believe fake news or that it makes much difference in the politics of a free society. Or that trying to “do something” about it would not diminish actual news. Anyone familiar with the way media are constructed will realize that much of the contents at any given time could be described as fake news anyway, depending on your definition. But it is usually low impact as well.
Of course, the term “fake news” has largely come to mean merely news that a party wishes were not so widely disseminated. That fact, in turn, touches on the question of what commentators mean when they say that fake news threatens “our democracy,” as the Shanghai-based researchers did.
Note: Information regarding the paper:
Abstract: Social media are massive marketplaces where ideas and news compete for our attention. Previous studies have shown that quality is not a necessary condition for online virality and that knowledge about peer choices can distort the relationship between quality and popularity. However, these results do not explain the viral spread of low-quality information, such as the digital misinformation that threatens our democracy4. We investigate quality discrimination in a stylized model of an online social network, where individual agents prefer quality information, but have behavioural limitations in managing a heavy flow of information. We measure the relationship between the quality of an idea and its likelihood of becoming prevalent at the system level. We find that both information overload and limited attention contribute to a degradation of the market’s discriminative power. A good tradeoff between discriminative power and diversity of information is possible according to the model. However, calibration with empirical data characterizing information load and finite attention in real social media reveals a weak correlation between quality and popularity of information. In these realistic conditions, the model predicts that low-quality information is just as likely to go viral, providing an interpretation for the high volume of misinformation we observe online. (paywall) c Xiaoyan Qiu, Diego F. M. Oliveira, Alireza Sahami Shirazi, Alessandro Flammini & Filippo Menczer, Limited individual attention and online virality of low-quality information, Nature Human Behaviour volume 1, Article number: 0132 (2017) Published: 26 June 2017 This article was retracted on 07 January 2019. More.
See also: Science confronts credibility issues
Extra! Extra! A handy guide to the normal fake news