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, ve 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 summarise the outcome of their model. In summary, 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 was would have had to be 37 time more effective to shift opinion. If recall was 7% of all stories, it would have had to be 27 times more effective. Line 8 sows 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
Another study was done by IPSOS for Buzzfeed on the impact of Fake News on Facebook, as Facebook had by far the largest reach of any social network for Fake News (see study here
) and conclusions were in line with the above work:
IPSOS Online survey of 1,007 American adults
Percentage of consumed news in the past the month by channel showed
- Facebook (55%)
- Broadcast TV (56%).
- Print newspapers (39%)
- Cable news (38%)
- “Social media (generally)” (33%)
- Newspapers’ websites (33%).
Print, TV and Twitter was relatively more trusted than Facebook
- 74% of those who’d gotten news from print newspapers
- 59% of respondents saying they trust news from TV all or most of the time.
- 49% of people who had gotten news from Twitter,
Far lower trust of news on Facebook all or most of the time
- 18% of respondents trust news on Facebook all or most of the time
- 30% said “about half of the time,”
- 44% said they rarely or almost never trust news on Facebook.
- 8 % Don’t Know
However, other research by IPSOS suggests that trust is not the same as belief — Another poll by Ipsos/BuzzFeed News foundon average about 75% of American adults believed fake news headlines about the election when they recalled seeing them. This contradicts the Stanford finding of c 55%, but as their model showed, even that belief level would not have changed the election outcome
In short, both studies show a minority of news was received from the online world, and it was by and large not widely believed, so the impact was relatively small. However, 2 caveats to the Stanford work:
- 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 gymnastics to justify their 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 needed to persuade them, actual impact could be higher
In other words this may underplay the total 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.
Also, the Stanford model's definition of "Fake News" is very strict - we believe there is far more "False" news - news that bends the truth, or is economical with it - in circulation, and that acts in a similar manner. A lot of this sort of news is meat and drink to more "respected" media as well (and it is they that are leading the complaints against "Fake" news).
At any rate, 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
Amazon has had a patent granted to
fly blimp warehouses above cities so drones can deliver your goodies from the blimps. Leaving aside the "how the hell do you get a patent for something so damn obvious and that has already been done
", the question is why blimps?
The answer is the appalling logistic costs of transporting products by drone. As we showed in the previous note on this area (over here
), drone delivery has some major problems:
(i) Small payload - many trips are required to satisfy even a moderately large order, a weekly shop would take 20+ drones to deliver. Heavy lifting drones are unlikely to be a feature of urban environments anytime soon, they are very dangerous if anything goes wrong.
(ii) Each trip has two very unproductive components
- firstly, the trips to and from warehouses, which are a long way (tens of miles) from where most customers are
- secondly, as payload is small, there is no ability to do a number of drops in one run as you can with a van, so each and every drop is essentially a point to point trip from warehouse to customer which increases transport costs per trip and reduces trips/drone per day, so asset utilisation falls
So there are essentially two solutions to the problem - either put physical warehouses in very high cost urban land (ie buy the Royal Mail or Big Yellow Storage) which hugely increases costs of warehouse square footage, or have mobile warehouses that move in closer so reducing the back and forth distance each drone flies.
Why blimps? Simply put, our analysis shows that the sheer number of drones needed to replace vans would be tantamount to huge swarms of these devices, the noise, flying traffic and risk of having them at street level would intolerable. Thus the only other available option is to fly them up and down to hovering blimps. Downside is the blimps are aircraft and thus have to fly at a safe height (tens of thousand feet), so drones (which, remember, for this application are basically helicopters so cannot benefit from any lift generated by wings) will have to expend a lot of energy climbing
We say only available option - the other option is of course vans, travelling on roads. These are high payload carrying, can optimise multiple drops and are non intrusive as they stick to existing transport networks. Best of all, they don't fall out of the sky.
Also, as we showed in the previous note, vans have a useful device - called a driver - that can negotiate that most tricky of problems, the last yards delivery to the customer....