I guess like quite a few Data Analytics/Insights companies, we’ve been tracking the US election using our systems (more details on our DataSwarm system here) just to see what it is telling us vs what we can see in the press etc, and vs. what others are saying.
Anyway, one of the predictors we follow closely is Nate Silver, as his 5-38 operation has called quite a few elections accurately before. Like many others, he is tracking the rise of Trump from what was considered a low (c 13.6% chance of election) just after the Democrat National Convention, and a steady rise since then till now they are about neck and neck (see their time chart here, just below the breakline.)
This (and similar) is interesting to us, as our system has only ever seen Trump out in front from the get go, the only thing that has varied is how far. To be sure this is because we are looking at different factors, we are looking at the memetic impact of Trump vs Clinton, not poll data, and Trump has never flagged in the meme race.
A quick refresher on Memetics – the term meme was coined by Richard Dawkins as a “mental gene”, and its role is to replicate itself by colonising other minds. Like genes, memes travel in groups called “memeplexes”, and the ones that are prevalent in a culture or subsector are what we term the “zeitgeist”. There is a branch of mathematics called Memetic Analysis which is quite useful for analysing this, its a sort of cross breed between the maths of Viruses spreading and feedback-loop System Dynamics. Anyway, the chart above shows the output of what we call “zeitgeist tracking”, that presents as a “data swarm” (especially as it moves over time) of the relative position of relevant memes to a topic (in this case the US Election), here shown on on two quite useful axes:
– Relevance (Y Axis) – the relevance of a meme to the topic being examined (not all memes in a memeplex are relavant, some are fellow travellers – just as not all genes impact the area being examined, and others are just passengers)
– Influence (X Axis) – the uptake of the meme, a function of reach and transfer rate.
Also shown are 2 other blobs of memes – on the Y axis are relevant ones that haven’t yet gained much traction (the Darwinian Stew of memes in the culture waiting to find believers – Terry Pratchett’s Small Gods describes this issue perfectly) and on the X axis, memes that are second order – they are part of the memeplex but not highly relevant to the topic under examination (nonetheless, they can be useful for splitting out sub-tribes)
We also tracked the primaries at the time, and Trump moved off ahead of all the Republican hopefuls wit gathering pace, and never looked back. That allowed us useful data to to calibrate the memetic algorithms and now we want to see what they predict for the US Election outcome. We have been tracking the US Elections since the Republican Caucus, and Trump has always been in front, even during the Democrat National Convention. If the lessons of the Primaries hold true, he will win – not by much, but he will win, right now our system says he holds the dominant Zeitgeist and the rate of change is not slowing vs a vis Clinton.
Of course things can still go totally wrong, and tonight’s Candidate’s Debate may be just that, as in memetic terms the debate will be throwing around a number of highly relevant memes via a medium that has a potentially huge Influence. We are quite looking forward to seeing how it pans out memetically!
(Update – 12 hours on, we see no sign at all that Clinton has made any discernable difference in relative positioning yet. It’s probably too early to see enough data to be clear until US has had a full day to talk about it, so by tomorrow morning (28 September) UK time.. The mainstream media pundits are already saying she won, but as far as we can see the data is saying it was too close to make any difference to the big picture. The risk for Clinton is that (if Brexit is anything to go by) the MSM tend to suffer from confirmation bias and readily believe their own agendas. We expect to see a lot of attempts by the Clinton media to change the memes in circulation in the next few weeks.)
The essential Caveats
To be sure, this approach is far from foolproof but we are curious to see what it can do. The major pitfalls are:
Firstly, the General Election is somewhat more complex terrain than the standard fare of our system (customer service optimisation, consumer insight, brand messaging, influencer identification etc etc) as the nature of political constituencies give the ecosystem a series “break points” – ie a 55% / 45% advantage for the winning candidate can still result in then losing, due to uneven spraed of support – ie a minority of “over-won” states and a majority of “just-lost” states. And to make it more interesting, the states are not the same size. But this didn’t impact the primaries that much (however, that may have been because by the time of voting nearly all the other Republican candidates’ share of the meme-space had become relatively insignificant).
Secondly, the Zeitgeist is a measure of the recordable transmission of an idea and we know from following UK elections that people tend to publically talk politically acceptable and privately vote in their own interest, but Trump (in general) is the non-acceptable face so those showing positive interest in him are probably under, not over represented in the public discourse. We do track Zeitgeist sentiment, and pro Trump sentiment is on average lower than pro Clinton, but not hugely so (see above note re acceptable), as neither are particularly popular (or more accurately, both are highly polarised) but strictly speaking meme transmission is Wildean in our experience – what is more important is the scale of the conversation.