Was asked how we do this at Broadstuff Towers, in the light of our extraordinarily good prediction record ....so in true Listicle BS style, here are 10 "Red Flags" (to use the emotive form of "pointers"). The more of these it scores, the more the chance ...

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broadstuff"broadstuff" - 5 new articles

  1. 10 ways to tell if the latest New New Tech is bullshit, or real....
  2. Dutch Polis Surveillance
  3. Oculus Sales Rift - as predicted...
  4. The impact of Fake News on the US Election
  5. Do you Tronc Altaba?
  6. More Recent Articles

10 ways to tell if the latest New New Tech is bullshit, or real....

Was asked how we do this at Broadstuff Towers, in the light of our extraordinarily good prediction record :-D ....so in true Listicle BS style, here are 10 "Red Flags" (to use the emotive form of "pointers"). The more of these it scores, the more the chance of hype, bullshit and eventual shocking, painful collapse:

Some of the red flags are more traditional analysis based - "Type 1" - about the core technology, economics or regulatory frameworks:

(i) It uses the hype tech du jour (aka AI or IoT today) - hype tech is typically nowhere near the promised capability of the Hype, so is a good sign of impending failure

(ii) It requires "this time it will be different" economics or business models or human behaviour or regulation

(iii) Or similarly, the previous "real life" incarnation works nothing like this new thing (eg Uber is going to revolutionise the economics of the previously wafer thin margin taxi industry)

(iv) It mangles some fundamental law of physics or engineering (you'd be surprised how many do...drone delivery for eg)

(v) It goes against/arbitrages some regulatory or legal principle which you know will eventually be slapped on it.

However you can also discern quite a lot by "Type 2" red flags - analysing the activities of the commentariat, whose job it is to raise hype. (as noted above, hype is a good sign of an impending failure) - these are typical signs of this process:
(vi) It's a prediction from Planet Mobile (over 11 years of writing Broadstuff, we've found Mobile predictions are always the most, er, optimistic)

(vii) Most Silicon Valley Tech journalists think its great (the SV wolfpack too often operates somewhere between the poles of groupthink and shilling)

(viii) It's by a journo who writes paeans about SV companies (or if its already a book, run for the hills).

(ix) It's about the latest idea of a Person/Co who is currently a SV darling. Bonus flag if is a company is preparing for IPO

(x) it's pushed by a large Tech Co but is out of their usual sphere of competence.

As an aside, most Tech media, especially free to reader, is optimistic in nature. Also most Tech journalists are not STEM trained so don't always know what's happening "under the hood" - so caveat reador. Right now nothing beats the Type 1 BS around AI stuff, except maybe the Type 2 BS around the sexier Unicorns.
    

Dutch Polis Surveillance

Dutch election outcome, swing by ward (blue = right, orange = left) Image hat tip to @JossedeVoogd, dank je wel



As you may recall, we have set our analytic engines onto election watching for Brexit last year (see here), which it got, and it managed to predict the Trump election result (see here). We have now set it onto watching the French and German elections, and will have something to say about the French one soon.

Tracking the data flow is one thing, but to make systems predictive it is necessary to build systemic mathematical models that can dynamically adjust as new data comes in (this is the basis of machine learning as well) and for that it's worth doing a quick analysis of the Dutch election to see if some form of model is emerging. Our analysis is that the following occurred in Holland:

1, The main Centre-Right party moved considerably more Right (see diagram above), adopting quite a few of the Far-Right policies and narratives. It lost some of its more centrist supporters but prevented the Far Right from robbing its more right wing supporters.

2. The Centre-Left was decimated, We suspect that what happened in the UK and US has played out here too, in that the "white blue collars" went left and right - ie the pattern of the white blue collar class deserting their traditional party affiliation holds true here as it did in UK and US. It's where they went that differs from the US and UK, in that there was quite a shift to more Left parties like the Greens (arguably if Bernie Sanders had stayed in the US race US as a 3rd, it could have looked more like this).

3. We have a hypothesis (awaiting more detail) that the white blue collars are not in such a bad position economically in the EU as in the UK/US (better training, better working conditions and welfare), so are not as desperate/willing to embrace the populist option as a last hope - yet (see * below).

At any rate the above is a good start for modelling how Germany and France will play out, in that we assume the main Right Wing parties will adopt more Far Right clothing, and move considerably to the right to ensure they don't leak support there. (To an extent this is arguably what the Tories are doing, betting that their more centre-ist voters won't go to LibDems or Labour)

What this will mean is that a shift to the Right will be of similar size to the US/UK across the EU, just the "traditional" parties have learned from UK/US to embrace not reject the Far Right policies, to keep themselves in power. But the policies will shift to the Right so the effect is similar.

Anyway, if one uses that as an initial dynamic flow system model, it is then possible to calibrate the social media monitoring to look at cause and effect and the deltas, and refine the model.

An aside - one of the main reasons polls got it "wrong" in the US and the UK was an unwillingness of mainstream groups to believe the incumbent side could lose (see here). In Holland it was the opposite before the election, a common assumption was the Far Right would do way better than they did - but within hours of the outcome they were saying it was "back to normal/Far Right was defeated". This is very wrong too, as noted above. It will be interesting to see if the mainstream media/polls behave in the same way in France and Germany.

*A note - The summer had not yet come at the time of the Dutch election, and won't really have started by the French one - but it will have ended by the German election and if there has been a repeat of the migrant flows of recent years, one can hypothesize it will be much harder stemming the voter flow to the the Far Right than in Holland.
    

Oculus Sales Rift - as predicted...

Business Insider:

Facebook is closing hundreds of its Oculus VR pop-ups in Best Buys after some stores went days without a single demo

Well, that was quick - Broadstuff predictions for Tech 2017, No. 11, Dec 31 2016:

11. AR/VR - Useful bits of AR will become integrated into Mobile and Wearable devices over time, VR will be a niche pursuit until (if) price points come down hugely and even then its not likely to expand much farther than the gaming aficionado market. Resist all blandishments that this is the future, it won't be.


Hate to say "We told ya", but.....
    

The impact of Fake News on the 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.

Fake News Stanford

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:

Fake News vs Placebo

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

Fake News shitinng Election

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

Our Conclusion


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

 
    

Do you Tronc Altaba?

Tronc won the "silly renaming" rights for 2016, and we aren't long in 2017 for the first contender - Bits of Yahoo! not sold to Verizon will be named Altaba (without an ! even!) - TechCrunch:

Despite hiccups*, Yahoo’s planned sale to Verizon appears to be moving forward — but some portions of the company will be left behind and renamed Altaba Inc.

Yahoo is hanging on to its 15 percent stake in Alibaba and its 35.5 percent stake in Yahoo Japan, and those assets will survive as an investment company under the new name Altaba Inc., as the rest of Yahoo integrates with Verizon. The assets had previously been nicknamed Remain Co.

There is no truth in the rumour that Verizon will be re-named Verizon!

*That'll be the many millions of accounts found hacked in 2013/14, and the Peanut Butter problem
    

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