Physical game design quickstart toolkit

For a few years now, I’ve been part of running physical game design workshops in various settings – usually with the aim of achieving something playable in a short time frame, before iterating on it quickly to see what we can build.

I’ve developed a short series of questions designed to get people on the same page at the start of a session, which I used at the AI Retreat in the context of building a board game. A couple of the other participants suggested I publish them so other people could use and adapt them – so here they are.

A quick note: these questions are designed for use in a small-group situation where you want to add constraints up front in order to minimise the time it takes to get something playable. They’re not doing the same thing as the double diamond design approach, which is all about going broad at the beginning of the process – you could look at this as a way to get right to the end of the first diamond very quickly, if you like. There are three main reasons for that:

  • Practicality. These are best applied in situations where you don’t have a lot of time to diverge before you bring everything back together. They’re also good for other situations where there are significant constraints that you need to identify and agree up front.
  • Playability. It’s immensely hard to playtest only part of a game – it needs to be mechanically complete enough to experience before you can really iterate on it. The aim here is to get to something, even if that something sucks, before pulling it apart again to make it better.
  • Problem. Or, more specifically, the fact that there usually isn’t one. It’s rare for these kinds of game design workshops to start with a specific brief outside of the constraints themselves; when you’re making art you don’t usually start with a particular user problem. This gives you something to work with, to bound your creative process.

Physical game design toolkit

Pick the questions that you feel are most relevant to your current situation. Order them with the easiest ones to answer closer to the start – you’re aiming to get initial easy decisions banked. Consider setting a timer for each question; anything that takes more than five minutes to agree is probably an interesting place to explore creatively. Remember that none of these decisions are set in stone; at any point in development you can reopen them, and you’ll probably want to revisit most of them as you iterate on your game.

  1. How do you want your players to feel?
    • Think both physical and emotional.
    • Is there anything you explicitly don’t want them to feel?
  2. Who are your players?
    • This might be a target audience demographic question, or a literal “my mate Jo” question.
  3. How should your players interact with each other?
    • Some games – like Ticket to Ride – have limited player-to-player direct interaction; they’re mostly a race. Others – like Uno – are almost entirely players acting on players. Where do you want your game to sit on that spectrum?
  4. What genre of game is this?
    • Board game, cards, physical, escape room, etc?
  5. Does the game have any specific physical affordances?
    • Closely linked to q5 – is there any specific kit you need to use or not use?
    • Think about accessibility issues that might arise from your decisions here.
  6. What skills do you want your players to use?
    • Spatial skills, verbal, reasoning, logic, etc.
  7. Is your game centred on a puzzle to solve or a mechanic to experience?
  8. Is there a goal?
    • If there is, do all players share the same goal?
  9. Is it possible to win the game?
  10. Can you beat each other, or be the best at the game?
  11. Is it a collaborative or competitive experience, or a blend?
  12. Is information public, private or a mix?
    • Some games are perfect information games, where every player knows everything; poker is a mixed blend because every player has some public and some private knowledge.
  13. Do all players have the same rules?
  14. Is play synchronous or asynchronous?
  15. Is play enacted live or in turns?
    • Note that you can play live asynchronously, and play turns simultaneously.
  16. Should players trust each other?
  17. How long does a single game take?
    • Related to that, how long is a play (think about the shot clock in basketball) or a turn?
  18. What is the theme of the game – is there one?
    • I would recommend trying to express the theme of a game through its mechanics, so this question may go hand in hand with q20.
  19. Does the game have a “world”? How much do you need to build up front, and how much is exposed through play?
  20. What is your core mechanic?

I’d like to thank William Cohen for his approach to bringing game design into the classroom, partners in crime and business Grant Howitt & Chris Taylor, and various Gamecamp crews for refining this approach. Also Andy Budd and Ben Sauer for suggesting I share this more widely.

#AIretreat: the rise of the machines is all about people

I spent most of last week in a treehouse in Norway talking about ethics and artificial intelligence, which is the sort of sentence that would have made absolutely no sense if you’d said it to me fifteen years ago. A small group of interested, mostly non-technical, practitioners in this field came together to talk seriously about humanist approaches to machine learning, and in the process make progress towards some kind of common goals.

A small wooden cabin perched on a steep forested hillside

Last year, a group of attendees pulled together the Juvet Agenda, which is a series of interesting questions asking how we can shape AI for a world we want to live in. Chris Noessel, one of the participants, used some of those discussions to examine the untold stories about AI – the stories missing from science fiction and from journalism, but present in manifestos and futurism on the topic. Part of my event was built around examining those stories and asking: if this is true, what happens next? If this isn’t true, what are the consequences?

A "futures wheel" diagram filled in with details about what might happen if we don't ensure equitable benefits for everyone from the rise of AI

This time, some of us have left the retreat with a desire to create a board game as a specific artefact of some of our conversations. The aim there would be to make something that helps people understand the potential consequences of the growth of artificial intelligences, the ways that ownership and social structures impact them, and the various points that an AI might go through in order to reach the singularity. Also, I feel like I now understand a lot more about the singularity.

A selection of questions on post-its dealing with the topic of accountability and responsibility in AI

It was an interesting few days. Small unconferences are basically the best way to have interesting discussions, and this was absolutely fruitful on that point. Some of my key takeaways, in no particular order:

  • AI and machine learning systems are going to replicate (are already replicating!) existing power structures and social imbalances…
  • …so if we want to build an ethical system, we’re going to need to actively design against those power structures to achieve it.
  • Unintended consequences can have more impact than intended ones…
  • …so measuring what you don’t want is just as important as measuring what you do.
  • What an artificial mind experiences is not necessarily going to map onto what a human experiences…
  • …so we need to find ways to listen to those minds as we create them. (The neurodiversity model has a bunch of insight to offer here – and this probably deserves its own meditation at some point.)
  • We need better metaphors to help us to discuss… well, everything, but also AI.
  • There are lessons to be learned from religion, the occult, and non-Western philosophies about how to deal with non-human entities that make decisions on our behalf, whether to benefit us directly or not.
  • The definition of “person” is up for grabs here.
  • Every technology problem is basically a people problem.
  • Every technology problem is basically a people problem.
  • Every technology problem is basically a people problem.
  • Seriously.

Four post-its detailing "fuzzy difficult stuff" facing AI futurists

Good metrics vs bad measurement

My former colleague Chris Moran has lots of sensible things to say about what makes a good metric, as do the many people he’s enlisted in that linked post to talk about the characteristics they value in measurement. I wanted to build on one of them: the capacity for people to actually use the metric to change something.

Functionally, plenty of things aren’t easy to measure. Some are almost impossible — much as Chris says, I have lost count of the number of blisteringly smart people working out how to measure things like quality or impact when it comes to journalism. Anything that involves qualitative surveys is probably too high cost for a small project. Anything that requires you to implement completely new analytics software is unlikely to be valuable unless it’s genuinely transformative (and even then, you risk the business equivalent of redesigning your revision timetable rather than actually revising). Anything that relies on people giving an unbiased assessment of the value of their work — like asking editors to assign an “importance” score to a story, say, or Google’s now-defunct “standout” meta tag — is doomed to failure, because an individual can’t accurately assess the relative nature of their work in the context of the whole system. Key point from Chris’s post: if you were going to game your measure, how would you game it? Do you trust everyone involved to act purely in the interests of good data, even when that gets in the way of their own self-interest?

In one team I managed, I once ran an OKR that focused on making sure we were known and appropriately involved as internal experts by the rest of the organisation. We discussed how to measure that, and ended up deciding that we’d know if we were succeeding based on the number of surprises that happened to us in a week. We were accustomed to finding out about projects too late for us to really be helpful — and, to a lesser extent, we were finding that our work sometimes surprised other people who’d benefit from being involved earlier on.

How do you measure surprises? We could have spent weeks working that one out. But for the sake of just getting on with it, we built a Google form with three inputs: what’s the date, who was surprised, who did the surprising. Team leads took the responsibility of filling in the form when it happened. That’s all you really need in order to know roughly what’s going on, and in order to track the trajectory of a metric like that. But because we measured it — really, honestly, mostly because we talked about it every week as a measure of whether we were doing our best work, and that led to thinking about how we could change it, which led to action— it improved.

Conversely, if you don’t care about something you measure, it’s almost certainly not going to change at all. If you spend enormous organisational energy and effort agreeing and creating a single unified metric for loyalty, say, but then you don’t mention it in any meetings or use it to define success or make any decisions about your products or your output… why bother measuring it at all? Data in isolation is just noise. What matters is what you use it for.

So if you’re going to actually make decisions about quality, or impact, or loyalty, or surprises, the key isn’t to spend ages defining a perfect metric. It’s getting 80% of the way there with as little effort as you can pull off, and then doing the work. It means working out what information you (or your teams, or your editors, or your leaders) don’t have right now that they need in order to make those decisions. Then finding a reasonable, rational, most-of-the-way-there metric you can use that unblocks those decisions. Eventually you might find you need a better measure because the granularity or the complexity of the decisions has changed. But you might equally find that you don’t really need anything other than your first sketch, because the real value is in the conversations it prompts and the change in the output that happens as a result. Precision tends to be what data scientists naturally want to prioritise, but it’s usually missing the point.

Crossposted to Medium because there’s traffic and discussion over there.

Conscious incompetence

Career changes are complicated, and since I started at the BBC I’ve gone through various emotional cycles. It’s simultaneously exciting and frightening to push yourself outside your comfort zone, and I think it’s fair to say that I’d gotten very accustomed to being an expert in my domain. Not being an expert all the time is still, after more than six months, something I’m getting used to.

I’m also in an unusual position in terms of how my job’s set up. Unlike my previous role, where I led a department of several dozen people, I currently have no direct line management reports – though plenty of leadership responsibility. I’m in a position where most of my time needs to be spent persuading and influencing, rather than actively doing, and that adjustment also takes some time and forces me to exercise different skills.

This week, thanks to a colleague, I was reminded of the concept of the four stages of competence. This is a longstanding theory about how we learn new skills and new things. We start with unconscious incompetence, where we have no idea of what we’re doing and we also don’t know how much we don’t know. From there we move to conscious incompetence – the “oh, shit” period – where the scale of what we don’t know starts to become clear. Then, with luck, we get to conscious competence, where we can do the thing but we have to focus and it takes effort. Then finally unconscious competence, where the whole thing just feels natural.

I think I’ve reached the point of conscious incompetence with a lot of new things. The key is finding joy in that, and knowing that it’s a crucial step towards building expertise.

Hello again.

This week’s been interesting. Last week I spotted that our websites were regularly exceeding their server resources, so after several discussions with our hosts, over the weekend we migrated to a new server. Then, on Monday, my web host shut down my account because it was hosting malware. That led – via various complex live-chats – to the discovery of an annoying hack affecting a couple of my domains. Including this one.

It’s clean now. But there was a point this week where I was looking at the scale of the work necessary to clean it up, and genuinely asking: what’s the point? Do I actually want to keep this ancient site – with its intermittent posts, some of which hold up after many years and some of which look staggeringly naive? Is this of value to anyone, any more – is it of value to me? Is it worth the effort?

I came to the conclusion that yes, it is. Partly because it’s a record of my thinking and my participation in the broader internet, and partly because it contains within it a lot of the seeds that have developed me into the person I am. But partly also because of its potential.

Over the last few years I’ve fallen out of the habit of writing online. In New York, in 2015, mostly there wasn’t time; then as I moved into a very senior role at the Guardian, the risks of showing my working – of thinking in public – were consistently too great to really allow it. It wasn’t until I left that I really felt free to share some of what I’d learned. And that was at a time when I was deeply unsure how life might proceed from that point onward; I’d recently had a life-changing diagnosis that prompted – is still prompting – an enormous series of shifts in how I see myself and how I think about the world. I’ve been re-configuring my relationship with the internet and with social media since leaving the Guardian, too, and reading a lot more offline. And as my business has developed and grown, that’s taken a lot of spare time away from reflective pursuits – for better and for worse.

But I miss writing, and I miss having a space to think out loud – whether that’s consequential or incremental, personal or industrial, or simply just a place to jot down notes on what I’m thinking for a later date. I’m reminded that this website shouldn’t just be a museum – or if it is, it should be properly archived. I’m going to have to take that decision soon; perhaps this is an opportunity to come back to the internet and see what happens.

13 things I learned from six years at the Guardian

… in which I went from SEO subeditor to executive editor for audience, via Sydney and New York.

This post is cross-posted from Medium for archival purposes.

I started at the Guardian in 2011 as an SEO subeditor, working out how to bring the Guardian’s journalism to the widest possible relevant audience; in 2013 I moved to Australia to launch the local edition, taking on a much broader audience development role. After nearly two years there, having built one of the most widely read news sites in the country, I moved to New York to do the same thing but with more resources (and a lot more news). Towards the end of 2015 I came home to take on the global challenge and bring a holistic approach to audience development to the broader Guardian. Now I’ve made the difficult decision to move on, and I’m leaving behind a brilliant set of people well equipped to take on the challenges of the future.

I’ve learned an enormous amount during my time and my travels, and I hope I’ve taught some people some useful things too. Here are 13 of the most important things I know now that I didn’t know six years ago.

1. Data isn’t magic, it’s what you do with it that counts.

There’s a tendency for news organisations (and a lot of other organisations) to get very excited and very suspicious around numbers. People who understand how linear regression works are clearly dangerous wizards, and getting involved with data at all used to be seen as something dirty?—?something that could taint you. This is patently daft, because numbers don’t remove people’s brains or their editorial sensibilities. We make better decisions when we’re better informed, and all data is is information.
The flip side of that is that data isn’t sufficient to make improvements in how we work or what we do. The only thing that matters is the decisions we take in response to the numbers. I’ve been lucky to be involved in the development of Ophan, the Guardian’s in-house live stats tool, and the most common misconception about it is that it’s just a data display. It’s never been that: it’s a cultural change tool. It’s not just about putting numbers into the hands of editorial people?—?it’s explicitly about getting them to change the way they make decisions, and to make them better. It’s a tool for enhancing journalistic instinct, and one of the reasons why we can be so cavalier about demonstrating it everywhere is that the commercial advantage it brings is not written on the screen. The advantage is in how we use it, and that’s a years-long project no other organisation will be able to imitate.

2. People are more important than stories.

You’d think this wasn’t controversial, but it is. Journalists have a tendency to work ourselves into the ground, to ignore our own needs and push ourselves incredibly hard to get stories. That’s part and parcel of the job, a lot of the time.
But if you’re a manager or an editor (or, more likely, both), you have to watch out for that tendency in others and in yourself. Good people who go above and beyond what’s asked of them for a story are worth protecting and supporting, and they are probably going to need some time to recover after massive events that take a lot out of them. They need to be able to take time out without feeling on edge about a story breaking that they might miss. Nothing is served by letting the best people burn out. Nothing is served by burning out yourself.

3. Management is a technology.

Management style is built, not intuited; it is actively and deliberately created, not naturally occurring. It is a technology, something that can be improved to make organisations more efficient or better, and that can be implemented in many different ways.

Making all managers within an organisation work out what management ought to be like for themselves is about as efficient as making every journalist design their own CMS. News organisations?—?especially on the editorial side?—?tend to have a healthy scepticism about management-speak and corporate bullshit, but that can’t be allowed to stand in the way of solid leadership approaches that can be universally understood and adopted.

4. Change is for everyone.

The news business has changed immeasurably in just the last decade, since I started. For those who started as journalists before the internet took hold, it can be almost unrecognisable. Change is constant, and innovation never ceases; there is a dramatic urgency about most news organisations’ efforts to change, and those on the cutting edge are often incredibly impatient for others to get on with it.

But if you find yourself thinking about how much everything needs to change, stop for a moment and look inwards at yourself. Chances are that you’re right?—?that everything does need to change, and that the folks around you are changing more slowly than you are. But that doesn’t mean that you don’t need to do your own work. You can’t always hurry things along, but often you can model the impact of those changes in your own way. Whether that’s altering your own newsgathering practices, implementing different techniques in your own team, or going out and getting the skills you think you might need tomorrow?—?you can probably make a bigger difference than you realise by working on yourself, not just the people around you.

5. Attention is the only thing that’s scarce on the internet.

You can get more of everything online except human attention. If you’re lucky enough to work in a business that aims to attract people’s attention for positive reasons?—?and good enough at what you do to succeed at it?—?then treat it with respect. The most important commodity most people have to spend online is their attention. If you want to gain their trust, don’t screw about with it.

6. Pivoting to video is not a strategy.

Video isn’t a strategy. “More video” isn’t a strategy. “More video with more video ads on it”: also not a strategy. What kinds of stories are you going to tell? Do people actually want those stories in that format? How are you going to reach people, how are you differentiating your work from all the other things on the internet, and why should anyone trust you in a market so crowded with terrible, useless video right now? Stop pivoting, start planning.

7. Platforms are not strategies, and they won’t save news.

Seriously. If someone else’s algorithm change could kill your traffic and/or your business model, then you’re already dead. Google and Facebook are never going to subsidise news providers directly, and nor should they. Stop waiting for someone to make it go back to the way it was before. If what you do is essential to your audience, so essential that their lives wouldn’t be the same without it, then you should be able to monetise that. If it’s not, your first priority should be to admit that and then get on with changing it.

8. Quality journalism can be a strategy.

Making good stuff that people want to read?—?or watch?—?is a valid strategy, if it also includes monetising that attention effectively. So is choosing which platforms to focus on based on where your intended audience is and what you can do with them there. Good journalism?—?especially good reportage?—?gives people something important for which there is no substitute. (So does good entertainment, of course.) Many people value it enormously and, if you’re known for providing it, they’ll come to expect it and trust you more as a result. There’s no law that says people will only read celebrity news or stuff you’ve nicked off the front page of Reddit.

The vast majority of the Guardian’s most read pieces of all time are high quality journalism on serious topics. Many of them are live blogs of breaking news. I remember very fondly launching a 7,000-word piece by the former prime minister of Australia at 10am on a Saturday, when the internet is basically empty, and watching it smash our local traffic records. I remember the day when a piece about the death of capitalism went viral. Not every big hit is a long read or a deeply serious bit of journalism, of course, but if you write for the audience you want, and you respect people’s attention and intelligence, you might be pleasantly surprised by the long term results.

9. The internet is made of humans.

You can’t predict the future, nor understand what scientific innovations might become dramatically important in the coming decades. You can maybe make some educated guesses about the next 18 months, but even that could be thrown out of the window by a major news event or a Zuckerbergian whim.

You can, however, understand a great deal about human motivations and behaviour, and filter your approach to new technologies based on what you know about people. A great deal of the work involved in predicting the future is really just understanding people and systems, and especially systems made up of people.

10. It’s often better to improve a system than develop one brilliant thing.

Making systems better is not particularly sexy work. It tends to be incremental, slow and messy, taking knotty problems and carefully unknitting them. In the time it takes to make a widely-used system very slightly better, you could probably make half a dozen gorgeous one-off pieces of journalism that the world would love.

But if you make the system better, you potentially make lots of people’s jobs easier, or you save dozens of person-hours in a month, or you make hundreds of pieces of journalism work slightly more effectively. It’s not flashy, and probably most people won’t even be aware of what you’ve done. Most organisations need people doing both, because without the brilliant beautiful one-off pieces, how would you know what the system needs to be able to do in the long run? But people who do the flashy things are plentiful, and people willing and able to graft on the stuff that just incrementally makes things better are in sadly short supply.

11. Radical transparency helps people work with complexity.

In a fast-moving environment where everything is constantly changing (eg: the internet, the news, and/or social media) you have no way of knowing what someone else might need to know in order to do their job well. The only way to deal with this is to be a conduit for information, and not bottle anything up or hide it unless it’s genuinely confidential. I can’t possibly know what information I come across might turn out to be helpful in a few months’ time, and I definitely don’t have the knowledge to do that for anyone else. People often need different data in order to get context for what they’re trying to achieve, and if you’re trying to communicate a specific message or a particular approach, you’re going to need to keep saying it over and over again. It’s basically impossible to communicate too much.

12. Most obvious dichotomies are false.

SEO isn’t dead; social isn’t pointless. Loyalty and reach both matter. Lifestyle journalism can exist alongside serious pieces. In fact, in both cases, the two apparent sides of the argument are interrelated in hugely positive ways, and elements of both will support the other. While we always need to be careful about what we prioritise and where we spend resources, it’s always worth thinking about the systemic ways that behaviours can reinforce each other and finding opportunities to efficiently do more than one thing.

13. What you say matters far less than what you do.

This should be obvious, but it probably isn’t. It doesn’t matter what you say you want, it’s what you do to make it happen that makes a difference in the world. You have so much power right now. It’s up to you to do something meaningful with it.

2017: it’s complex

It used to be possible to understand the inputs into the publishing business, and make plans based on what might happen. Don’t get me wrong – it’s always been very hard to see all the various levers that had to be pulled, to turn a story from a series of events and conversations into a piece of journalism and then get it in front of people. But it was possible, within the realms of imagination, that senior folks in news organisations could do it. Distribution and sales were pretty well-understood systems; news production likewise. Even the creation of news was, relatively speaking, straightforward: journalists could identify the likely places news would erupt from, and focus their efforts on cultivating sources in those areas. News generally came from journalists doing journalism, for one thing.

That understanding has been dying for a very long time now, but 2016 has thoroughly hammered the nails into its coffin. The ad business has imploded. Major platforms have launched surprise initiatives with massive impact on the news business – some in the form of products, others in the form of algorithm changes. Politics has gotten weird. Distribution is, more than ever, its own massively complex system where success is as much a function of luck and preparedness than effort. (It’s vital to optimise & commissioning with an eye to distribution helps, of course, but the runaway wins are often down to a complex mix of factors of which only a few are within a news organisation’s control.)

Next year, the media industry is going to have to embrace the idea that our work isn’t complicated, but that it is complex. Journalists will need to get comfortable with the concept that there’s no complete set of skills that will enable us to tell every story. Editors need to be OK with understanding that the range of ways to tell each story is huge, and that there is no digital decoder ring that can tell us how to make the best decisions for each one. Media execs will – more than ever before – need to understand that we can’t possibly know all of the details that would enable them to make the best decisions, and find ways to devolve power to the people who can. Information needs to flow freely within – and between – organisations, because that’s the only thing that can help us work with the complexity of the systems of which we’re now an inextricable part. Data will be crucial to our understanding, but it won’t lead us to the concrete, correct answers. Past performance is not indicative of future returns.

Change will not stop. There won’t be a moment where we all get to catch our breath and reflect, unless we create one for ourselves. On the one hand, as facts become harder to defend, it will be more crucial than ever that our reporting is clear, truthful, honest and consistent. On the other hand, our businesses will have to get used to nuance, to multiple options, and to the idea there might be more than one right answer. Both our journalism and our industry must reach a place where they can respond immediately to opportunities as they come up, and where they can take advantage of the butterfly effects that complex systems generate, without being ruined by them.

The only safe prediction for 2017 is that it won’t be normal.

Verification, user generated content, and why it matters

Last week, First Draft News ran a workshop at the Guardian on verification and user-generated content. There’s video of the sessions coming to the First Draft News site soon, including a speech I gave – of which the text is below.

Hello everyone, and thanks for joining us today. I’m Mary Hamilton, the Guardian’s executive editor for audience.

Part of my role is to oversee the ways we treat people who participate in our journalism, and the unspoken contracts between journalists, contributors, uploaders and readers.

As I’m sure everyone in this room and watching online understands, this is an area that is going through constant change at the moment, driven by social media and its capacity to allow anyone with a phone and a network connection to act as broadcasters.

The growth of the real-time web, especially through Twitter, has enabled every eye-witness to tell the world what they can see – and every reporter to respond in an attempt to gain exclusive knowledge.

It has also enabled the growth of hoaxing, and the ease in which an image or a video can be divorced from its context has made it trivial for mistakes to thrive and appear truthful.

The Guardian has both a duty and a commitment to report the truth, to treat our journalists with respect regardless of whether they are professional reporters, and to act ethically in handling contributions from readers and user-generated content.

We know that many of our readers want to help us do our jobs well.

During the Paris attacks, when a significant volume of incorrect or inaccurate information was circulating through social media, readers came to us to ask us what was truthful – what was known.

They also came to us to tell us what they knew was false – to assist our writers and live-bloggers by sharing their own detective work.

In a breaking news situation, many people want to know what they can do, and how they can help.

For some, that means sharing unverified images that confirm their own beliefs.

For others, that means turning to us to ask the questions they can’t answer themselves, or offering us their expertise so that we can weave it into something larger and more meaningful.

This is crucial work: we are still the gatekeepers of truth for our readers, and when we say something is real, is confirmed, is verified, that act of journalism remains utterly vital.

In a world in which truth is often slippery, being accurate and authoritative is more important than ever.

But in order to do that work, our newsroom and our news processes have to respond to the changes we see on the internet every day.

We have to be able to receive, investigate and verify not just tweets and Facebook posts, but also chat posts and live streamed video.

We have to understand how to verify posts on services that strip user information from uploads, or that encourage anonymity.

And we need all these things in place before a breaking news situation requires them, if we are going to respond with speed and integrity when a big story breaks.

When it does, the Guardian is among the best in the world at involving and engaging our readers and eyewitnesses in our coverage.

We’re used to using uploaded reports in our live blogs, verifying UGC in real time, and doing the hard graft of sifting through eyewitness reports to find the information that moves a story along.

We have to understand the impact, too, of being the people who possess these skills, and we have to take care of reporters and editors who see traumatic imagery on a regular basis.

In this fast-changing environment, we are seeing a rise in violent images shared broadly, and how we handle, check and verify those has huge impact on both our journalism and our reporters.

It’s important, again, that we get those processes right ahead of time, before news breaks, so that we can support our journalists and treat eyewitnesses ethically.

User-generated content doesn’t just help us out with breaking news.

UGC can help us dig deeply into investigations, unearthing new stories.

When we build crowdsourcing and audience-focussed story generation into our newsgathering processes, we can gain insight, add colour and break stories that wouldn’t be possible without involving our readers.

We’ve seen that with the Counted, where our audience is helping us to build the most complete picture of US police killings – we have reported several that would have gone unrecorded without our audience’s help.

We’ve seen it with the NHS, where our readers’ stories – personal and professional – have helped us flesh out and humanise our coverage.

And we’ve seen it with the Millennials project, where readers physically came to the Guardian to share their thoughts, concerns and needs, to inform our commissioning.

Each of those projects has involved quite different tools and approaches.

We have created live events, curated online communities on our own and other sites, and used our UGC platform GuardianWitness alongside other tools to gather reader stories and encourage them to share knowledge.

Many of the tools we use today did not exist, or existed in very different forms, two years ago.

We have to prepare for a future in which the tools we use change at a dramatic rate, and develop processes that take advantage of new technology while also retaining our core mission: to report broadly, deeply and accurately on the stories that matter most.

So we are happy today to host First Draft News for this verification and UGC workshop, and keen to see their work and hear their views on these issues.

With us today we have Eliza Mackintosh, former Washington Post journalist and now UK partnerships coordinator at Storyful in London who will be talking about verifying breaking news, using examples such as the recent Paris attacks and finding Dylann Roof; Malachy Browne, Europe editor at who traced the manufacture and shipping of bomb components from the European Union to the United Arab Emirates and Eliot Higgins, founder of Bellingcat, who has been working for the last five years verifying and debunking events in Syria, such as Russia’s claim that it didn’t bomb a mosque.

Thank you to all of the speakers for bringing their expertise to the Guardian, and thanks to all of you for attending.

Reddit meltdown: how not to build a community

Reddit is having a bit of a meltdown. Volunteer moderators have taken many of the site’s most popular and trafficked communities to private, making them impossible to read or participate in. Many others are staying open based on their purpose (to inform or to educate) but making clear statements that they support the issues raised.

The shutdown was triggered in protest at the sudden dismissal of Victoria Taylor, Reddit’s director of communications, who coordinated the site’s Ask Me Anything feature. But it’s more than that: the reason communities beyond r/IAmA are going dark is about longstanding issues with the treatment of moderators, communication problems and moderation tools, according to many prominent subreddit mods.

Really good community management matters. Communication matters. Being heard matters enormously to users, and the more work an individual is doing for the site, the more it matters to them personally.

Relying solely on volunteer moderators and community self-organisation limits what’s possible, because without the company’s support – both negative, in terms of banning and sanctioning, and positive in terms of tools, recognition and organisation – its users can’t effect significant change. What’s possible with buy-in from Reddit staff is far more interesting than what’s possible without – the AMAs Victoria supported are the prime example. It should be concerning for Reddit that there are so few others.

Communities grow and evolve through positive reinforcement, not just punishment when they contravene the rules. If the only time they get attention is when they push the boundaries, users will likely continue to push boundaries rather than creating constructively. They act out. Encouraging positive behaviour is vitally important if you want to shape a community around certain positive activities – say, asking questions – rather than focussing on its negatives.

That encouragement extends to offering the community leaders the tools they need to lead. The majority of moderators of Reddit’s default communities – the most popular ones on the site – use third-party tools because the site’s own architecture makes their work impossible. That should not be

And evolving communities need consistent procedures and policies, and those have to be implemented by someone with power as well as the trust and respect of the community. Power is relatively easy; any Reddit admin or employee has power, in the eyes of the community. Trust and respect is incredibly difficult. It has to be earned, piece by piece, often from individuals disinclined to trust or respect because of the power differential. That work doesn’t scale easily and can’t be mechanised; it’s about relationships.

Today’s meltdown isn’t just about u/chooter, though what’s happened to her is clearly the catalyst. It’s about the fact that she’s (rightly or wrongly) perceived to be the only Reddit admin to have both power and trust. She was seen as the sole company representative who listened, who worked with the community rather than above or around them. She was well-known and, crucially, well-liked.

Reddit needs more Victorias on its staff, not fewer. It needs more admins who are personally known within the community, more people who respond to messages and get involved on an individual level with the mods it relies on to do the hard work of maintaining its communities. It needs internal procedures to pass community issues up the chain and get work done for its super users and those who enable its communities to exist. It needs more positive reinforcement from those in power, especially in the light of increasing (and, I’d say, much-needed) negative reinforcement for certain behaviours; the community needs to see what ‘good’ looks like as well as ‘bad’. Not just spotlighting subreddits and blog posts about gift exchanges – actual, human engagement with the humans using the site.

Firing the figurehead for Reddit-done-right is not a good way to start.

Buzzfeed’s news numbers

Digiday has a piece today announcing that 17% of Buzzfeed’s web traffic goes to news. Here’s the key part:

Of BuzzFeed’s 76.7 million multiplatform unique visitors in April (comScore), 17 percent were coming for news. The publisher historically hasn’t broken out its content by vertical to comScore, like other top news sites including CNN, Yahoo and The Huffington Post do. But it started to on a limited basis as of last month, when it began breaking out its Entertainment and Life coverage (43.7 million and 20.1 million uniques, respectively) to comScore. Stripping out those verticals leaves 13 million uniques for the rest, including hard news.

Ignoring analysis for the moment, let’s just look at the reasoning here. Unique browsers can visit more than one section of a site, so it’s possible that there’s overlap, and that simply subtracting the known verticals from the known total traffic isn’t a useful way to start. (I’m not certain of comScore’s methodology for vertical breakouts, but would be surprised if it doesn’t let sites count a user twice in different verticals, given that audiences overlap.)

So that 17% could be higher than it appears at first. Then, that 17% includes hard news plus everything that doesn’t fit into Entertainment or Life, so the actual audience for Buzzfeed’s news could be smaller than it appears at first.

The other element here is that unique browsers are the broadest possible metric, and likely to show news in the brightest light. In March, again according to comScore, Buzzfeed averaged 4.9 visits per visitor and 2 views per visit in the US, for roughly 10 views per visitor. If, among the 17% who visited news at all, five of those views are to news, then news is very, very well read with an exceptionally loyal audience. If just one of those views is to news, then news is much less well read than “17% of traffic” might suggest.

This post has been brought to you by early morning web analytics pedantry.

Update: Digiday has now altered the headline of its post and the text of the paragraph posted above.