Impact of Fake News on US Election

After our systems predicted Trump’s win, we were asked a number of times about the impact of Fake News (and Bots, Russian Hacking etc – we will cover those in separate posts) and here is a summary of some of the useful research we looked at:

Stanford/ NYU Research

Firstly, research by Hunt Allcott of NYU and Matthew Gentzkow of Stanford,  published by Stanford University looked at the sources and takeup of Fake News. They defined “fake news” as “news stories that have no factual basis but are presented as facts”. By news stories they meant stories that originated in social media or the news media, i.e. excluded false statements originated by political candidates or major political figures. They also excluded websites well-known to be satirical, such as the Onion.

Firstly, they found that in the US elections, people mainly got their news by from sources other than websites and social media (see pie chart below, left). But online media (websites and social media) was where most Fake News was disseminated. They also looked at how Fake News was disseminated on the online media (below, right) and the majority was transmited via social media with a significant minority going direct (to websites or their feeds) or finding it in search results, This contrasts hugely with how top news was disseminated, mainly via older channels but online the major source was via direct access and then search.

They also looked at how people reacted to Fake News, vs. Mainstream media news, and also inserted Placebo news (stories they made up) to test reactions.  The chart below shows how people reacted:

The Figure presents the share of headlines that survey respondents that recall seeing (blue bar) vs. recall seeing and also believing (red bar). They averaged responses across all the headlines within four categories of headlines they presented – “Big” true stories; Smaller true stories; Fake stories and Placebo stories that they had made up headlines for. In short they found that 15 percent of people reported seeing the Fake stories, and 8 percent reported seeing and believing them (about 55%). But the chart also shows a number of other interesting tendencies:

• Rates of both seeing and believing are much higher for true than fake stories
• They are higher for the “Big True” headlines (the major headlines leading up to the election) than for the “Small True” headlines (the more minor fact-checked headlines that were gathered from Snopes and PolitiFact).
• Placebo fake news articles, which never actually circulated, are approximately equally likely to be recalled and believed as the Fake news articles that did actually circulate.
• This false recall rate is similar for Fake and Placebo articles, this suggests that the raw responses significantly overstate the circulation of Fake news articles, and that the true circulation of Fake news articles was quite low

The last test they did was to model what impact Fake News would have had to make to shift opinion in the most closely fought wards to ensure the Democrats won. For Clinton to have won the election, Trump’s margin of victory would have to decrease by ~ 0.51% of the voting age population, which would have shifted Michigan, Pennsylvania, and Wisconsin into Clinton wins and deliver the Electoral College. Thus, the core question was whether fake news could have increased Trump’s margin of error by more than 0.51 percent of the voting age population. The table below summarises the outcome of their model. The column on the far right looks at how many times more  effective the Fake News would have had to be compared to TV advertising to have had to have shifted the vote. For example, on line 1 a Fake News story as it performed in reality would have had to be 37 time more effective to shift opinion. If recall was 75% of all stories (line 2), it would still have had to be 27 times more effective. Line 8 shows that if Fake News shares were 20x greater it would still have to have been 13 times more effective

Their overall conclusion was that Fake News was very unlikely to have had a major effect:

Social media were not the most important source of electionnews, and even the most widely circulated fake news stories were seen by only a small fraction of Americans. For fake news to have changed the outcome of the election, a single fake news storywould need to have convinced about 0.7 percent of Clinton voters and non-voters who saw it toshift their votes to Trump, a persuasion rate equivalent to seeing 36 television campaign ads

•  We suspect the Stanford study underplays impact on people, our empirical observation is that many people believe what they want to believe no matter how untrustworthy the source, and will go to great mental gymnasitcs to justify belief even when its shown to be totally false, so that IPSOS figure of 75% may well be closer then the c 55% (8% of 15%) of the Stanford study.
• Also, the Stanford model is generic and averaged, if (and it’s a big if) Cambridge Analytica was able to pinpoint just the people it needed to persuade, in just the wards it needs to persuade them, actual impact could be higher.

In other words this may underplay the impact of Fake News, but even so the model is still showing it has to be a LOT more effective to actually swing the votes. Our view is its a marginal contributor, but in a 50/50 split election (which in effect this was) even small margins can be effective. especially if used in conjunction with a number of other small nudges.

Expect more use of Fake News in future campaigning, and in attempts to persuade in general.

We have looked at how our systems can counter this, and believe we have some solutions