60 Scientific American, December 2020 Graphic by Jen Christiansen
SOURCE: DIMITAR NIKOLOV AND FILIPPO MENCZER (data)ger-lasting food) as negative information (such as risk
of meltdown or possible harm to health).
The first person in the social diffusion chain told
the next person about the articles, the second told the
third, and so on. We observed an overall increase in
the amount of negative information as it passed along
the chain—known as the social amplification of risk.
Moreover, work by Danielle J. Navarro and her col-
leagues at the University of New South Wales in Aus-
tralia found that information in social diffusion
chains is most susceptible to distortion by individu-
als with the most extreme biases.
Even worse, social diffusion also makes negative
information more “sticky.” When Jagiello subsequent-
ly exposed people in the social diffusion chains to the
original, balanced information—that is, the news that
the first person in the chain had seen—the balanced
information did little to reduce individuals’ negative
attitudes. The information that had passed through
people not only had become more negative but also
was more resistant to updating.
A 2015 study by OSoMe researchers Emilio Fer-
rara and Zeyao Yang analyzed empirical data about
such “emotional contagion” on Twitter and found that
people overexposed to negative content tend to then
share negative posts, whereas those overexposed to
positive content tend to share more positive posts.
Because negative content spreads faster than positive
content, it is easy to manipulate emotions by creat-
ing narratives that trigger negative responses such as
fear and anxiety. Ferrara, now at the University of
Southern California, and his colleagues at the BrunoKessler Foundation in Italy have shown that during
Spain’s 2017 referendum on Catalan independence,
social bots were leveraged to retweet violent and
inflammatory narratives, increasing their exposure
and exacerbating social conflict.RISE OF THE BOTS
InformatIon qualIty is further impaired by social bots,
which can exploit all our cognitive loopholes. Bots are
easy to create. Social media platforms provide so-called
application programming interfaces that make it fair-
ly trivial for a single actor to set up and control thou-
sands of bots. But amplifying a message, even with just
a few early upvotes by bots on social media platforms
such as Reddit, can have a huge impact on the subse-
quent popularity of a post.
At OSoMe, we have developed machine-learning
algorithms to detect social bots. One of these, Botom-
eter, is a public tool that extracts 1,200 features from
a given Twitter account to characterize its profile,
friends, social network structure, temporal activity pat-
terns, language and other features. The program com-
pares these characteristics with those of tens of thou-
sands of previously identified bots to give the Twitter
account a score for its likely use of automation.
In 2017 we estimated that up to 15 percent of active
Twitter accounts were bots—and that they had played
a key role in the spread of misinformation during the
2016 U.S. election period. Within seconds of a fake news
article being posted—such as one claiming the Clinton
campaign was involved in occult rituals—it would be
tweeted by many bots, and humans, beguiled by the1 429More than 15,000 Twitter users are plotted
on this matrix. The size of each dot
represents the number of accounts that
share that political bias/misinformation
coordinate, ranging from one to 429.Political Bias of Twitter Users (inferred by set of news sources shared by user)Very liberal Center Very conservativePercent of Users’ Tweets That Share Links from Low-Credibility Sources1007550250Risk of spreading misinformationLowVulnerability High
to Fake News
A study of Twitter users that rated
their political leanings found that
both liberals and conserv atives
end up sharing information from
sites that repeatedly post news
of low credibility (as identified
by independent fact-checkers).
Conservative users are somewhat
more susceptible to sharing fake
news, however.