So, Twitter has expanded the 140 character limit to 280 for all users. I've used it for a week or so like this, and overall what I've found is: - It's useful as you don't have to artificially concatenate twts, and you can be clearer in what you mean - ...

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broadstuff"broadstuff" - 5 new articles

  1. Twitter is for bullet points, not essays
  2. Dulce et decorum est, pro Patria mori....but not such a good plan if you don't
  3. Adversarial Perturbation - fooling AI Image Processing, one Pixel at a time
  4. Fake News about Fake News, or How do I hire those Russians?
  5. Czech Election - Our predictive model correct(ish) again
  6. More Recent Articles

Twitter is for bullet points, not essays

So, Twitter has expanded the 140 character limit to 280 for all users. I've used it for a week or so like this, and overall what I've found is:

- It's useful as you don't have to artificially concatenate twts, and you can be clearer in what you mean
- If you stick to around 140 characters and stick to the point, it flows as well as the original.

But...
- Some users (too many) put no new, useful information in the extra 50%, the signal-to-noise ratio is effectively halved
- A pageful of 280 character twts is half as much information as one of 140 character twts, and given too many are full of woffle it can be even worse.
- A 280 character tweet needs some formatting functions to be easy to read.

Now no doubt Twitter could put in basic editing functions, but this seems to be adding complexity to a system where the major attraction is speed and simplicity.

IMO Twitter is for fast-to-read bullet point information, not micro-essays, and especially not micro-essays by the verbose.
    

Dulce et decorum est, pro Patria mori....but not such a good plan if you don't



Lest we never forget, not all unknown soldiers are dead

(Rogers cartoon, Pittsburgh Post-Gazette)
    

Adversarial Perturbation - fooling AI Image Processing, one Pixel at a time

Cat registering as Guacamole on Google Inception V3


Covered in boingboing and LabSix- what happens when people start to spoof image processing algorithms..Can you make a cat look like guacamole?

Three researchers from Kyushu University have published a paper describing a means of reliably fooling AI-based image classifiers with a single well-placed pixel.

It's part of a wider field of "adversarial perturbation" to disrupt machine-learning models; it's a field that started with some modest achievements, but has been gaining ground ever since.

But the Kyushu paper goes further than any of the research I've seen so far. The researchers use 1, 3 or 5 well-placed pixels to fool a majority of machine-classification of images, without having any access to the training data used to produce the model (a "black box" attack).

Must admit I'm intrigued and a bit heartened, there may be a way out of the continual CCTV snooping world that is emerging after all. Research paper is over here .
    

Fake News about Fake News, or How do I hire those Russians?

Latest news on the Russians on Facebook brouhaha from Recode

At Facebook, roughly 126 million [or about half the voting population] users in the United States may have seen posts, stories or other content created by Russian government-backed trolls around Election Day, according to a source familiar with the company’s forthcoming testimony to Congress....

Google, which previously had not commented on its internal investigation, will break its silence: In a forthcoming blog post, the search giant confirmed that it discovered about $4,700 worth of search-and-display ads with dubious Russian ties. It also reported 18 YouTube channels associated with the Kremlin’s disinformation efforts, as well as a number of Gmail addresses that “were used to open accounts on other platforms.”

And Twitter will tell Congress that it found more than 27,000 accounts tied to a known Russian-sponsored organization called the Internet Research Agency:

There is a bit more from NBC:

Facebook says in the testimony that while some 29 million Americans directly received material from 80,000 posts by 120 fake Russian-backed pages in their own news feeds, those posts were “shared, liked and followed by people on Facebook, and, as a result, three times more people may have been exposed to a story that originated from the Russian operation
.
Beforehand, Facebook had said they had found about $100,000 of Russian spend on the platform over the election.

To put these numbers into context, the Clinton campaign spent about $1.4 billion on media during this period, and that is without the free publicity from nearly every US media organ being mainly on the Democrat side. The Russian media pieces would have been swamped by this spend and volume, never mind all the rest of the pieces of media on the average social media timeline. The Trump campaign spent c $1bn itself, not on Russians.

A BBC article says Facebook estimates that:

"just one in 23,000 or so messages shared on the network were from the Russians".

That's a ratio of 23,000/1 (about 0.004%), and if you compare the ratio between the Russian spend of c $100k vs the c $2.5 bn spent by Trump & Clinton (25,000/1) then they are no more effective in getting their message transmitted than any other piece of media on average. If the Russian spend is underestimated then they are in fact quite poor at writing attractive content, unless of course the volume is also underestimated in which case it approaches this average again. .

The election was won on c 55,000 voters voting for Trump in marginal wards, of a total of c 130m voters. about 0.04% of all voters. So for the Russians to have "stolen the election" with 0.004% of all Ads, their Ads must be 10x as persuasive as all others just to be have an equal chance. Or they have to be extremely targeted. But from the above, it seems the Russians reached c 50% of the potential voting population or about 130m people , so doing the paper-napkin maths at the most optimistic (The 130m messages seen went only to the "right 50% of the 130m who actually voted) so 23,000/1 is now 11,500/1, about 0.02%, so they are 2x more targeted and thus have to be only 5x as persuasive.

So in order to believe the Russians "stole the election" you either have to believe that either:

(i) They are extraordinarily persuasive copywriters and great targeters to boot, and can do wonders on a shoestring, beating the best US Ad agencies that Democrat money could buy (they bought the best) or

(ii) Facebook (and the others) are massively underestimating both their spend and % of all messaging, but it has to be at least 10x more just to get parity on reaching those 0.04% of marginal voters who turned it for Trump, never mind persuading them

Or alternatively, you have to believe they were a pretty marginal force in the election (albeit with intent).

Incidentally, we used our data analytics technology to predict Trump's win (and a few other recent elections too) correctly, and we did it by analysing social media support for Trump vs Clinton. It was highly predictive, forecasting a neck and neck race, and we edged it to Trump based on small signals. We're pretty confident our system is fairly good at negating the obvious bots (which are the ones that operate in volume) but there's no doubt we saw some Russian Fake News in the results. However our main impression is by and large people knew which candidate they wanted (or quite frequently, which one they most certainly did not want) and shared media that agreed with them in their own filter bubbles.



    

Czech Election - Our predictive model correct(ish) again

Although we didn't track the Czech Election with the full system (unlike these 5), we have mapped it to our OECD political predictive model which we built after the Norwegian and Dutch elections. It states that:

1, The main Centre-Right party moves considerably more Right, adopting quite a few of the Far-Right policies and narratives. This ploy may lose some of its more centrist supporters (to who though?) but it prevents the Far Right from taking far more right wing supporters. If it doesn't (Germany) then the Far Right takes a larger share

2. The Centre-Left is decimated, their various "Deplorables" classes - traditional working class voters - go both left and right. This leads to a rise in the Far-Left numbers. The hard to predict move is the Centre left voters who move rightwards, as there is no natural home for them at the moment, and they seem to scatter depending on local conditions.


So far it's The Czech Election mapped to this, with one "known" exception we've seen before (the "French Disconnection"):

- Parties closer to EU liberal establishment values were left massively depleted. The ruling centre-left Social Democrats (CSSD), saw its share of the vote tumble to become the sixth-largest party.

- Far-right and anti-establishment groups made gains in the election. Far Right SPD support was up to 10.6%, and The Pirates will make their debut in parliament with 10.8% seats. The centre-right Civic Democrats second with 11% each.

- But, as with Macron in France. a populist centreist party, ANO (Yes) collected a share of almost 30%. Our model predicts the Centre left moves right and left, but in most other countries there has not been an easy centre-right choice - except France with a populist centre-ist, and now the Czech Republic.


And as elsewhere, more parties with a wider range on the polltical spectrum are in the parliament, making it harder to put together larger majority coalition.

Resulting setup is:

ANO (Yes): 29.6% +11%
Civic Democratic Party: 11.3% +4%
The Czech Pirate Party: 10.8% +8%
Freedom and Free Democracy party (SPD) : 10.6% New Entrant +10.6%
Communist Party of Bohemia and Moravia (KSCM): 7.8% -7%
Social Democrats (CSSD): 7.3% -13%
KDU-CSR (Christian Democrats) 5.8% -1%
TOP-09 (Liberal) 5.3% -7%


More is certainly to come, as the EU still refuses to shift it's position. As mathematical historian Peter Turchin points out "the governing elites of the EU behave as though they all believe these disintegrative trends that I and others have written about are just a "blip". The more they believe that, we predict the less it will be.

    

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