Hacking elections with data

Download PDF

[cross-post from Medium]

Cambridge Analytica basically used customer segmentation and targeting: standard advertising stuff (and some cynicism about that: iirc one of the other campaigns ditched them) that will probably become standard for campaigns if it hasn’t already (full disclosure: am helping out on a campaign). Cool (if unethical) use of surveys as probes though. Get the feeling they didn’t do as much as they could have done, but that was enough. Not sure how I feel about gaming elections right now: part of me says bad, another part that it’s politics as normal, just scaled and personalised.

Meanwhile, on the Democratic side, big data seems to be a problem. We need to fix this. So repeat after me “big data is not data science”. Get the data, study the data, but understand that exploring data is just part of the arc between questions and storytelling, that humans are complex and working with them is about both listening and persuading.

Let’s try data-supported hearts-and-minds.

Bursting the right bubbles

Download PDF

[cross-post from Medium]

First, understand the bubble

It’s hard to argue with people if you don’t know where they’re coming from. One way is to ask: engage with people who are vehemently disagreeing with you, find out more about them as people, about their environments and motives. Which definitely should be done, but it also helps to do some background reading…

The Guardian’s started in on this: a round-up of 5 non-liberal articles every week, complete with backgrounder on each author and why the article is important. It doesn’t hurt that some of these authors are friends of friends and therefore maybe approachable with some questions. It’s also worth checking out things like BlueFeed RedFeed.

I’ve taken some flack lately for trying to understand Trump supporters. I’m slowly coming round to amending that to trying to understand Trump voters — especially the ones who voted with their noses held.

Us vs Them

I still can’t be pulled into “them vs us”. God it would be easier sometimes to build barricades and see ‘them’ as evil people supporting an evil leader, but truth is we’re all human, and unless we get out of this together, it’s going to be way beyond hard to get out at all.

I spent the lead-up to the election in a house in Trump country (neighbours with guns and banners about them having guns and I don’t mean in a jolly countryside lets bag some pheasants kinda way; Trump signs and flags everywhere including my local pub; that damned hat on people I’d spend time at the bar with) and whilst I wouldn’t necessarily ask for empathy (those flags were on some damn nice houses), I would ask for understanding the narratives and doing some deep soul-searching to see if any of them might be true, because that’s where we start the honest conversations about where we are today.

And yes, I’m going to fight by every nonviolent means possible too — I have much less to lose than others around me (older, no children, nobody who depends on me); I’m hoping that if enough of us fight that way, we’ll never have to fight in the alternative.

Could Ring Theory help?

I’ve been thinking a lot today about people, their interactions, margins, fairness and how to be a better ally, friend and compatriot (thank you Barnaby for making me think hard about this). We humans are complex beasts: en masse it’s hard to apply things like Ring Theory (the “comfort in, dump out” theory for comforting individuals and the layers of people around them that we often talked about in the Crisismappers’ Cancer Survival Chat — yes, there was such a thing, yes there were many people you wouldn’t expect in it, no I didn’t — I was there as an admin/ friend…) because everyone has different pain from different things, especially when that pain is being deliberately seeded in many areas at once. I’m not sure what the answer is, but mutual compassion, respect and walking in the other guys’ shoes have definitely got to be in there somewhere.

Bottom line: understand where other people are coming from. See if you can get them to understand where you are coming from too. Am not talking about the trolls (ignore them), but the people who are genuinely trying to argue with you…

Fake News Isn’t About Truth, It’s About Gaming Belief Systems

Download PDF

[cross-post from Medium]

Thinking about #fakenews. Starting with “what is it”.

* We’re not dealing with truth here: we’re dealing with gaming belief systems. That’s what fake news does (well, one of the things; another thing it does is make money from people reading it), and just correcting fake news is aiming at the wrong thing. Because…
* Information leaves traces in our heads, even when we know what’s going on. If I jokingly tell you that I’ve crashed your car, then go ‘ha ha’, you know that I didn’t crash your car, but I’ve left a trace in your head that I’m an unsafe driver. The bigger the surprise of the thing you initially believe, the bigger the trace it leaves (this is why I never make jokes like that).
* That’s important because #fakenews isn’t about the thing that’s being said. It’s about the things that are being implied. Always look for the thing being implied. That’s what you have to counter.
* Some of those things are, e.g. “Liberals are unpatriotic”. “Terrorists are a real and present threat *to you*”. Work out counters for these, and mechanisms for those counters. F’example: wearing US flags at protests and being loudly patriotic whilst standing up for basic rights is a good idea.
* Yes, straighten the record, but you’re not aiming at the person (or site) spouting fake news. What you *are* trying to change is their readers’ belief in whether something is true.
* America is a big country. Not everyone can go and see what’s true or not. Which means they have to trust someone else to go look for them. The Internet is even bigger. Some of the things on it (e.g. beliefs about other people’s beliefs) don’t have physical touchpoints and are impossible to confirm or deny as ‘truth’.
* Which means you’re trying to change the beliefs of large groups of people, who have a whole bunch of trust issues (both overtrust for in-group, and serious distrust of out-group people) and no direct proof.
* You know who else hacks trust and beliefs in large groups? Salesmen and advertisers. Learn from them (oh, and propagandists, but you might want to be careful what you learn there).
* People often hold conflicting beliefs in their heads (unless they’re Aspie: Aspies have a hard time doing this). Niggling doubts are levers, even when people are still being defensive and doubling-down on their stated beliefs. Look for the traces of these.
* But go gentle. Create too much cognitive dissonance, and people will shut down. Learn from the salesmen on this.
* People are more likely to trust people they know. Get to know the people whose beliefs you want to change (even if it means hanging out in conservative chat channels). Also know that your attention is a resource: learn to distinguish between people who are engaged and might listen (hint: they’re often the ones shouting at you), people who won’t, and sock puppets.
* More advertising tricks: look for influencers (not just on Twitter ‘cos it’s easy goddammit; check in the real world too). There’s only one you: use that you wisely.

Some reading:
* A field guide to earthlings (the Aspie reference)
* Social psychology: a very short introduction

How to culture jam a populist

The Internet is made of beliefs

Download PDF

[cross-post from Medium]

“Most people don’t have the time or headspace to handle IW: we’re going to need to tool up. Is not much, but I’m talking next month on belief, and how some of the pre-big-data AI tools and verification methods we used in mapping could be useful in this new (for many) IW world… am hoping it sparks a few people to build stuff.” — me, whilst thoroughly lost somewhere in Harlem.

Dammit. I’ve started talking about belief and information warfare, and my thoughts looked half-baked and now I’m going to have to follow through. I said we’d need to tool up to deal with the non-truths being presented, but that’s only a small part of the thought. So here are some other thoughts.

1) The internet is also made of beliefs. The internet is made of many things: pages and and comment boxes and ports and protocols and tubes (for a given value of ‘tubes’). But it’s also made of belief: it’s a virtual space that’s only tangentially anchored in reality, and to navigate that virtual space, we all build mental models of who is out there, where they’re coming from, who or what to trust, and how to verify that they are who they say they are, and what they’re saying is true (or untrue but entertaining, or fantasy, or… you get the picture).

2) This isn’t new, but it is bigger and faster. The US is a big country; news here has always been either hyperlocal or spread through travelers and media (newspapers, radio, telegrams, messages on ponies). These were made of belief too. Lying isn’t new; double-talk isn’t new; what’s new here is the scale, speed and number of people that it can reach.

3) Don’t let the other guys frame your reality. We’re entering a time where misinformation and double-talk are likely to dominate our feeds, and even people we trust are panic-sharing false information. It’s not enough to pick a media outlet or news site or friend to trust, because they’ve been fooled recently too; we’re going to have to work out together how best to keep a handle on the truth. As a first step, we should separate out our belief in a source from our belief in a piece of information from them, and factor in our knowledge about their potential motivations in that.

4) Verification means going there. For most of us, verification is something we might do up front, but rarely do as a continuing practice. Which, apart from making people easy to phish, also makes us vulnerable to deliberate misinformation. We want to believe stuff? We need to do the leg-work of cross-checking that the source is real (crisismappers had a bunch of techniques for this, including checking how someone’s social media profile had grown, looking at activity patterns), finding alternate sources, getting someone to physically go look at something and send photos (groups like findyr still do this). We want to do this without so much work every time? We need to share that load; help each other out with #icheckedthis tags, pause and think before we hit the “share” button.

5) Actions really do speak louder than words. There will most likely be a blizzard of information heading our way; we will need to learn how to find the things that are important in it. One of the best pieces of information I’ve ever received (originally, it was about men) applies here: “ignore everything they say, and watch everything they do”. Be aware of what people are saying, but also watch their actions. Follow the money, and follow the data; everything leaves a trace somewhere if you know how to look for it (again, something that perhaps is best done as a group).

6) Truth is a fragile concept; aim for strong, well-grounded beliefs instead. Philosophy warning: we will probably never totally know our objective truths. We’re probably not in the matrix, but we humans are all systems whose beliefs in the world are completely shaped by our physical senses, and those senses are imperfect. We’ll rarely have complete information either (e.g. there are always outside influences that we can’t see), so what we really have are very strong to much weaker beliefs. There are some beliefs that we accept as truths (e.g. I have a bruise on my leg because I walked into a table today), but mostly we’re basing what we believe on a combination of evidence and personal viewpoint (e.g. “it’s not okay to let people die because they don’t have healthcare”). Try to make both of those as strong as you can.

I haven’t talked at all about tools yet. That’s for another day. One of the things I’ve been building into my data science practice is the idea of thinking through problems as a human first, before automating them, so perhaps I’ll roll these thoughts around a bit first. I’ve been thinking about things like perception, e.g. a camera’s perception of a car color changes when it moves from daylight to sodium lights, and adaptation (e.g. using other knowledge like position, shape and plates) and actions (clicking the key) and when beliefs do and don’t matter (e.g. they’re usually part of an action cycle, but some action cycles are continuous and adaptive, not one-shot things), how much of data work is based on chasing beliefs and what we can learn from people with different ways of processing information (hello, Aspies!), but human first here.