Today I am at the “Data Science for Media Summit” hosted by The Alan Turing Institute & University of Edinburgh and taking place at the Informatics Forum in Edinburgh. This promises to be an event exploring data science opportunities within the media sector and the attendees are already proving to be a diverse mix of media, researchers, and others interesting in media collaborations. I’ll be liveblogging all day – the usual caveats apply – but you can also follow the tweets on #TuringSummit.

Introduction – Steve Renals, Informatics

I’m very happy to welcome you all to this data science for media summit, and I just wanted to explain that idea of a “summit”. This is one of a series of events from the Alan Turing Institute, taking place across the UK, to spark new ideas, new collaborations, and build connections. So researchers understanding areas of interest for the media industry. And the media industry understanding what’s possible in research. This is a big week for data science in Edinburgh, as we also have our doctoral training centre so you’ll also see displays in the forum from our doctoral students.

So, I’d now like to handover to Howard Covington, Chair, Alan Turing Institute

Introduction to the Alan Turing Institute (ATI) – Howard Covington, Chair, ATI

To introduce ATI I’m just going to cut to out mission, to make the UK the world leader in data science and data systems.

ATI came about from a government announcement in March 2014, then bidding process leading to universities chosen in Jan 2015, joint venture agreement between the partners (Cambridge, Edinburgh, Oxford, UCL, Warwick) in March 2015, and Andrew Blake, the institute’s director takes up his post this week. He was before now the head of research for Microsoft R&D in the UK.

Those partners already have about 600 data scientists working for them and we expect ATI to be an organisation of around 700 data scientists as students etc. come in. And the idea of the data summits – there are about 10 around the UK – for you to tell us your concerns, your interests. We are also hosting academic research sessions for them to propose their ideas. 

Now, I’ve worked in a few start ups in my time and this is going at pretty much as fast a pace as you can go.

We will be building our own building, behind the British Library opposite the Frances Crick building. There will be space at that HQ for 150 peaople. There is £67m of committed funding for the first 5 years – companies and organisations with a deep interest who are committing time and resources to the institute. And we will have our own building in due course.

The Institute sits in a wider ecosystem that includes: Lloyds Register – our first partner who sees huge amounts of data coming from sensors on large structures; GCHQ – working with them on the open stuff they do, and using their knowledge in keeping data safe and secure; EPSRC – a shareholder and partner in the work. We also expect other partners coming in from various areas, including the media.

So, how will we go forward with the Institute? Well we want to do both theory and impact. So we want major theoretical advances, but we will devote time equally to practical impactful work. Maths and Computer Science are both core, but we want to be a broad organisation across the full range of data science, reflecting that we are a national centre. But we will have to take a specific interest in particular interest. There will be an ecosystem of partners. And we will have a huge training programme with around 40 PhD students per year, and we want those people to go out into the world to take data sciences forward.

Now, the main task of our new director, is working out our science and innovation strategy. He’s starting by understanding where our talents and expertise already sit across our partners. We are also looking at the needs of our strategic partners, and then the needs emerging from the data summits, and the academic workshops. We should then soon have our strategy in place. But this will be additive over time.

When you ask someone what data science is that definition is ever changing and variable. So I have a slide here that breaks the rules of slide presentations really, in that it’s very busy… But data science is very busy. So we will be looking at work in this space, and going into more depth, for instance on financial sector credit scoring; predictive models in precision agriculture; etc. Undercutting all of these is similarities that cross many fields. Things like security and privacy is one such area – we can only go as far as it is appropriate to go with people’s data, and issue both for ATI and for individuals.

I don’t know if you think that’s exciting, but I think it’s remarkably exciting!

We have about 10 employees now, we’ll have about 150 this time next year, and I hope we’ll have opportunity to work with all of you on what is just about the most exciting project going on in the UK at the moment.

And now to our first speaker…

New York Times Labs – Keynote from Mike Dewar, Data Scientist

I’m going to talk a bit about values, and about the importance of understanding the context of what it is we do. And how we embed what we think is important into the code that we write, the systems that we design and the work that we do.

Now, the last time I was in Edinburgh was in 2009 I was doing a Post Doc working on modelling biological data, based on video of flies. There was loads of data, mix of disciplines, and we were market focused – the project became a data analytics company. And, like much other data science, it was really rather invasive – I knew huge amounts about the sex life of fruit flies, far more than one should need too! We were predicting behaviours, understanding correlations between environment and behaviour. I’

I now work at the New York Times R&D and our task is to look 3-5 years ahead of current NYT practice. We have several technologists there, but also colleagues who are really designers. That has forced me up a bit… I am a classically trained engineer – to go out into the world, find the problem, and then solve it by finding some solution, some algorithm to minimise the cost function. But it turns out in media, where we see decreasing ad revenue, and increasing subscription, that we need to do more than minimise the cost function… That basically leads to click bait. So I’m going to talk about three values that I think we should be thinking about, and projects within that area. So, I shall start with Trust…

Trust

It can be easy to forget that much of what we do in journalism is essentially surveillance, so it is crucial that we do our work in a trustworthy way.

So the first thing I want to talk about is a tool called Curriculum, a Chrome browser plug in that observes everything I read online at work. Then it takes chunk of text, aggregates with what others are reading, and projects that onto a screen in the office. So, firstly, the negative… I am very aware I’m being observed – it’s very invasive – and that layer of privacy is gone, that shapes what I do (and it ruins Christmas!). But it also shares what everyone is doing, a sense of what collectively we are working on… It is built in such a way as to make it inherently trustworthy in four ways: it’s open source so I can see the code that controls this project; it is fantastically clearly written and clearly architected so reading the code is actually easy, it’s well commented, I’m able to read it; it respects existing boundaries on the web – it does not read https (so my email is fine) and respects incognito mode; and also I know how to turn it off – also very important.

In contrary to that I want to talk about Editor. This is a text editor like any others… Except whatever you type is sent to a series of micro services which looks for similarity, looking for NYT keyword corpos, and then sends that back to the editor – enabling a tight mark up of their text. The issue is that the writer is used to writing alone, then send to production. Here we are asking the writer to share their work in progress and send it to central AI services at the NYT, so making that trustworthy is a huge challenge, and we need to work out how best to do this.

Legibility

Data scientists have a tendency towards the complex. I’m no different – show me a new tool and I’ll want to play with it and I enjoy a new toy. And we love complex algorithms, especially if we spent years learning about those in grad school. And those can render any data illegible.

So we have [NAME?] an infinite scrolling browser – when you scroll you can continue on. And at the end of each article an algorithm offers 3 different recommendation strands… It’s like a choose your own adventure experience. So we have three recommended articles, based on very simple recommendation engine, which renders them legible. These are “style graph” – things that are similar in style; “collaborative filter” – readers like you also read; “topic graph” – similar in topic. These are all based on the nodes and edges of the connections between articles. They are simple legible concepts, and easy to run so we can use them across the whole NYT corpus. They are understandable to deal with so has a much better chance of resonating with our colleagues.

As a counter point we were tasked with looking at Behavioural Segmentation – to see how we can build different products for them. Typically segmentation is done with demography. We were interested, instead, on using just the data we had, the behavioural data. We arranged all of our pageviews into sessions (arrive at a page through to leave the site). So, for each session we representated the data as a transition matrix to understand the probability of moving from one page to the next… So we can perform clustering of behaviours… So looking at this we can see that there are some clusters that we already know about… We have the “one and dones” – read one article then move on. We found the “homepage watcher” who sit on the homepage and use that as a launching point. The rest though the NYT didn’t have names for… So we now have the “homepage bouncer” – going back and forth from the front page; and the “section page starter” as well, for instance.

This is a simple caymeans (?) model and clustering, very simple but they are dynamic, and effective. However, this is very very radical at NYT, amongst non data scientist. It’s hard to make it resonate to drive any behaviour or design in the building. We have a lot of work to do to make this legible and meaningful for our colleagues.

The final section I want to talk about is Live…

Live

In news we have to be live, we have to work in the timescales of seconds to a minute. In the lab that has been expressed as streams of data – never ending sequences of data arriving at our machines as quickly as possible.

So, one of our projects, Delta, produces a live visualisation of every single page views of the NYT – a pixel for person starting on the globe, then pushing outwards, If you’ve visited the NYT in the last year or so, you’ve generated a pixel on the globe in the lab. We use this to visualise the work of the lab. We think the fact that this is live is very visceral. We always start with the globe… But then we show a second view, using the same pixels in the context of sections, of the structure of the NYT content itself. And that can be explored with an XBox controller. Being live makes it relevant and timely, to understand current interests and content. It ties people to the audience, and encourages other parts of the NYT to build some of these live experiences… But one of the tricky things of that is that it is hard to use live streams of data, hence…

Streamtools, a tool for managing livestreams of data. It should be reminscent of Similink or LabView etc. [when chatting to Mike earlier I suggested it was a superpimped, realtime Yahoo Pipes and he seemed to agree with that description too]. It’s now on it’s third incarnation and you can come and explore a demo throughout today.

Now, I’ve been a data scientist and involved when we bring our systems to the table we need to be aware that what we build embodies our own values. And I think that for data science in media we should be building trustworthy systems, tools which are legible to others, and those that are live.

Find out more at nytlabs.com

Q&A

Q1) I wanted to ask about expectations. In a new field it can be hard to manage expectations. What are your users expectations for your group and how do you manage that?

A1) The expectations in R&D, in which we have one data scientist and a bunch of designers. We make speculative futures, build prototypes, bring them to NYT, to the present, to help them make decisions about the future. In terms of data science in general at NYT… Sometimes things look magic and look lovely but we don’t understand how they work, in other places it’s much simpler, e.g. counting algorithms. But there’s no risk of a data science winter, we’re being encouraged to do more.

Q2) NYT is a paper of record, how do you manage risk?

A2) Our work is informed by a very well worded privacy statement that we respect and build our work on. But the other areas of ethics etc. is still to be looked at.

Q3) Much of what you are doing is very interactive and much of data science is about processing large sets of data… So can you give any tips for someone working with Terrabytes of data for working with designers?

A3) I think a data scientist essentially is creating a palate of colours for your designer to work with. And forcing you to explain that to the designer is useful, and enables those colours to be used. And we encourage that there isn’t just one solution, we need to try many. That can be painful as a data scientist as some of your algorithms won’t get used, but, that gives some great space to experiment and find new solutions.

Data Journalism Panel Session moderated by Frank O’Donnell, Managing Editor of The Scotsman, Edinburgh Evening News and Scotland on Sunday

We’re going to start with some ideas of what data journalism is

Crina Boros, Data Journalist, Greenpeace

I am a precision journalist.  and I have just joined Greenpeace having worked at Thomson Reuters, BBC Newsnight etc. And I am not a data scientist, or a journalist. I am a pre-journalist working with data. At Greenpeace data is being used for investigate journalism purposes, areas no longer or rarely picked up by mainstream media, to find conflicts of interest, and to establish facts and figures for use in journalism, in campaigning. And it is a way to protect human sources and enable journalists in their work. I have, in my role, both used data that exists, created data when it does not exist. And I’ve sometimes worked with data that was never supposed to see the light of data.

Evan Hensleigh, Visual Data Journalist, The Economist

I was originally a designer and therefore came into information visualisation and data journalism by a fairly convoluted route. At the Economist we’ve been running since the 1890s and we like to say that we’ve been doing data science since we started. We were founded at the time of the Corn Laws in opposition to those proposals, and visualised the impact of those laws as part of that.

The way we now tend to use data is to illustrate a story we are already working on. For instance working on articles on migration in Europe, and looking at fortifications and border walls that have been built over the last 20 to 30 years lets you see the trends over time – really bringing to life the bigger story. It’s one thing to report current changes, but to see that in context is powerful.

Another way that we use data is to investigate changes – a colleague was looking at changes in ridership on the Tube, and the rise of the rush hour – and then use that to trigger new articles.

Rachel Schutt, Chief Data Scientist, Newscorp

I am not a journalist but I am the Chief Data Scientist at Newscorp, and I’m based in New York. My background is a PhD in statistics, and I used to work at Google in R&D and algorithms. And I became fascinated by data science so started teaching an introductory course at Columbia, and wrote a book on this topic. And what I now do at Newscorp is to use data as a strategic asset. So that’s about using data to generate value – around subscriptions, advertising etc. But we also have data journalism so I increasingly create opportunities for data scientists, engineers, journalists, and in many cases a designer so that they can build stories with data at the core.

We have both data scientists, but also data engineers  – so hybrid skills are around engineering, statistical analysis, etc. and sometimes individual’s skills cross those borders, sometimes it’s different people too. And we also have those working more in design and data visualisation. So, for instance, we are now getting data dumps – the Clinton emails, transcripts from Ferguson etc. – and we know those are coming so can build tools to explore those.

A quote I like is that data scientists should think like journalists (from DJ Patel) – in any industry. In Newscorp we also get to learn from journalists which is very exciting. But the idea is that you have to be investigative, be able to tell a story, to

Emily Bell says “all algorithms are editorial” – because value judgements are embedded in those algorithms, and you need to understand the initial decisions that go with that.

Jacqui Maher, Interactive Journalist, BBC News Labs
I was previously at the NYT, mainly at the Interactive News desk in the newsroom. An area crossing news, visualisation, data etc. – so much of what has already been said. And I would absolutely agree with Rachel about the big data dumps and looking for the story – the last dump of emails I had to work with were from Sarah Palin for instance.

At the BBC my work lately has been on a concept called “Structured Journalism” – so when we report on a story we put together all these different entities in a very unstructured set of data as audio, video etc. Many data scientists will try to extract that structure back out of that corpus… So we are looking at how we might retain the structure that is in a journalist’s head, as they are writing the story. So digital tools that will help journalists during the investigative process. And ways to retain connections, structures etc. And then what can we do with that… What can make it more relevant to readers/viewers – context pieces, ways of adding context in a video (a tough challenge).

If you look at work going on elsewhere, for instance at the Washington Post working on IS, are looking at how to similarly add context, how they can leverage previous reporting without having to do that from scratch.

Q&A/Discussion

Q1 – FOD) At a time when we have to cut staff in media, in newspapers in particular, how do we justify investing in data science, or how do we use data science.

A1 – EH) Many of the people I know came out of design backgrounds. You can get pretty far just using available tools. There are a lot of useful tools out there that can help your work.

A1 – CB) I think this stuff is just journalism, and these are just another sets of tools. But there is a misunderstanding that you don’t press a button and get a story. You have to understand that it takes time,  there’s a reason that it is called precision journalism. And sometimes the issue is that the data is just not available.

A1 – RS) Part of the challenge is about traditional academic training and what is and isn’t included here.. But there are more academic programmes on data journalism. It’s a skillset issue. I’m not sure that, on a pay basis, whether data journalists should get paid more than other journalists…

A1 – FOD) I have to say in many newsrooms journalists are not that numerate. Give them statistics, even percentages and that can be a challenge. It’s almost a badge of honour as wordsmiths…

A1 – JM) I think most newsrooms have an issue of silos. You also touched on the whole “math is hard” thing. But to do data journalism you don’t need to be a data scientist. They don’t have to be an expert on maths, stats, visualisation etc. At my former employer I worked with Mike – who you’ve already heard from – who could enable me to cross that barrier. I didn’t need to understand the algorithms, but I had that support. You do see more journalist/designer/data scientists working together. I think eventually we’ll see all of those people as journalists though as you are just trying to tell the story using the available tools.

Q2) I wanted to ask about the ethics of data journalism. Do you think that to do data journalism there is a developing field of ethics in data journalism?

A1 – JM) I think that’s a really good question in journalism… But I don’t think that’s specific to data journalism. When I was working at NYT we were working on the Wikileaks data dumps, and there were huge ethical issues there and around the information that was included there in terms of names, in terms of risk. And in the end the methods you might take – whether blocking part of a document out – the technology mignt vary but the ethical issues are the same.

Q2 follow up FOD) And how were those ethical issues worked out?

A1 – JM) Having a good editor is also essential.

A1 – CB) When I was at Thomson Reuters I was involved in running womens rights surveys to collate data and when you do that you need to apply research ethics, with advice from those appropriately positioned to do that.

A1 – RS) There is an issue that traditionally journalists are trained in ethics but data scientists are not trained in ethics. We have policies in terms of data privacy… But much more to do. And it comes down to the person who is building a data model – ad you have to be aware of the possible impact and implications of that model. And risks also of things like the Filter Bubble (Pariser 2011).

Q3 – JO) One thing that came through listening to ? and Jackie, it’s become clear that journalism is a core part of journalism… You can’t get the story without the data. So, is there a competitive advantage to being able to extract that meaning from the data – is there a data science arms race here?

A3 – RS) I certainly look out to NYT and other papers I admire what they do, but of course the reality is messier than the final product… But there is some of this…

A3 – JM) I think that if you don’t engage with data then you aren’t keeping up with the field, you are doing yourself a professional misservice.

A3 – EH) There is a need to keep up. We are a relatively large group, but nothing like the scale of NYT… So we need to find ways to tell stories that they won’t tell, or to have a real sense of what an Economist data story looks like. Our team is about 12 or 14, that’s a pretty good side.

A3 – RS) Across all of our businesses there are 100s in data science roles, of whom only a dozen or so are on data journalism side.

A3 – JM) At the BBC there are about 40 or 50 people on the visual journalism team. But there are many more in data science in other roles, people at the World Service. But we have maybe a dozen people in the lab at any given moment.

Q4) I was struck by the comment about legibility, and a little bit related to transparancy in data. Data is already telling a story, there is an editorial dimension, and that is added to in the presentation of the data… And I wonder how you can do that to improve transparancy.

A4 – JM) There are many ways to do that… To show your process, to share your data (if appropriate). Many share code on GitHub. And there is a question there though – if someone finds something in the data set, what’s the feedback loop.

A4 – CB) In the past where I’ve worked we’ve shared a document on the step by step process used. I’m not a fan of sharing on GitHub, I think you need to hand hold the reader through the data story etc.

Q5) Given that journalims is about holding companies to account… In a world where, e.g. Google, are the new power brokers, who will hold them to account. I think data journalism needs a merge between journalism, data science, and designers… Sometimes that can be in one person… And what do you think about journalism playing a role in holding new power brokers to account.

A5 – EH) There is a lot of potential. These companies publish a lot of data and/or make their data available. There was some great work on 5:38 about Uber, based on a Freedom of Information request to essentially fact check Uber’s own statistics and reporting of activities.

Q6) Over the years we’ve (Robert Gordan Univerity) worked with journalists from various organisations. I’ve noticed that there is an issue, not yet raised, that journalists are always looking for a particular angle in data as they work with it… It can be hard to get an understanding from the data, rather than using the data to reinforce bias etc.

A6 – RS) If there is an issue of taking a data dump from e.g. Twitter to find a story… Well dealing with that bias does come back to training. But yes, there is a risk of journalists getting excited, wanting to tell a novel story, without being checked with colleagues, correcting analysis.

A6 – CB) I’ve certainly had colleagues wanting data to substantiate the story, but it should be the other way around…

Q6) If you, for example, take the Scottish Referendum and the General Election and you see journalists so used to watching their dashboard and getting real time feedback, they use them for the stories rather than doing any real statistical analysis.

A6 – CB) That’s part of the usefulness of reason for reading different papers and different reporters covering a topic – and you are expected to have an angle as a journalist.

A6 – EH) There’s nothing wrong with an angle or a hunch but you also need to use the expertise of colleagues and experts to check your own work and biases.

A6 – RS) There is a lot more to understand how the data has come about, and people often use the data set as a ground truth and that needs more thinking about. It’s somewhat taught in schools, but not enough.

A6 – JM) That makes me think of a data set called gdump(?), which captures media reporting and enables event detection etc. I’ve seen stories of a journalist looking at that data as a canonical source for all that has happened – and that’s a misunderstanding of how that data set has been collected. It’s close to a canonical source for reporting but that is different. So you certainly need to understand how the data has come about.

Comment – FOD) So, you are saying that we can think we are in the business of reporting fact rather than opinion but it isn’t that simple at all.

Q7) We have data science, is there scope for story science? A science and engineering of generating stories…

A7 – CB) I think we need a teamwork sort of approach to story telling… With coders, with analysts looking for the story… The reporters doing field reporting, and the data vis people making it all attractive and sexy. That’s an ideal scenario…

A7 – RS) There are companies doing automatic story generation – like Narrative Science etc. already, e.g. on Little League matches…

Q7 – comment) Is that good?

A7 – RS) Not necessarily… But it is happening…

A7 – JM) Maybe not, but it enables story telling at scale, and maybe that has some usefulness really.

Q8/Comment) There was a question about the ethics and the comment that nothing needed there, and the comment about legibility. And I think there is conflict there about

Statistical databases  – infer missing data from the data you have, to make valid inferences but could shock people because they are not actually in the data (e.g. salary prediction). This reminded me of issues such as source protection where you may not explicitly identify the source but that source could be inferred. So you need a complex understanding of statistics to understand that risk, and to do that practice appropriately.

A8 – CB) You do need to engage in social sciences, and to properly understand what you doing in terms of your statistical analysis, your P values etc. There is more training taking place but still more to do.

Q9 – FOD) I wanted to end by coming back to Howard’s introduction. How could ATI and Edinburgh help journalism?

A9 – JM) I think there are huge opportunities to help journalists make sense of large data sets. Whether that is tools for reporting or analysis. There is one, called Detector.io that lets you map reporting for instance that is shutting down and I don’t know why. There are some real opportunities for new tools.

A9 – RS) I think there are areas in terms of curriculum, on design, ethics, privacy, bias… Softer areas not always emphasised in conventional academic programmes but are at least as important as scientific and engineering sides.

A9 – EH) I think generating data from areas where we don’t have it. At the economist we look at China, Asia, Africa where data is either deliberately obscured or they don’t have the infrastructure to collect it. So tools to generate that would be brilliant.

A9 – CB) Understand what you are doing; push for data being available; and ask us and push is to be accountable, and it will open up…

Q10) What about the readers. You’ve been saying the journalists have to understand their stats… But what about the readers who know how to understand the difference between reading the Daily Mail and the Independent, say, but don’t have the data literacy to understand the data visualisation etc.

A10 – JM) It’s a data literacy problem in general…

A10 – EH) Data scientists have the skills to find the information and raise awareness

A10 – CB) I do see more analytical reporting in the US than in Europe. But data isn’t there to obscure anything. But you have to explain what you have done in clear language.

Comment – FOD) It was once the case that data was scarce, and reporting was very much on the ground and on foot. But we are no longer hunter gatherers in the same way… Data is abundant and we have to know how we can understand, process, and find the stories from that data. We don’t have clear ethical codes yet. And we need to have a better understanding of what is being produced. And most of the media most people consume is the local media – city and regional papers – and they can’t yet afford to get into data journalism in a big ways. Relevance is a really important quality. So my personal challenge to the ATI is: how do we make data journalism pay?

And with that we are off for lunch and demos, but I’ll be blogging again from 1.50 (afternoon programme is below)… 

Ericsson, Broadcast & Media Services – Keynote from Steve Plunkett, CTO

Audience Engagement Panel Session
• Paul Gilooly – Director of Emerging Products, MTG
• Steve Plunkett – CTO, Broadcast & Media Services, Ericsson
• Pedro Cosa – Data Insights and Analytics Lead, Channel 4
• Hew Bruce-Gardyne – Chief Technology Officer, TV Squared
• Jon Oberlander (Moderator), University of Edinburgh

Networking Break with Demo Sessions

BBC – Keynote from Michael Satterthwaite, Senior Product Manager

Unlocking Value from Media Panel Session
• Michael Satterthwaite – Senior Product Manager, BBC
• Adam Farqhuar – Head of Digital Scholarship, British Library
• Gary Kazantsev R&D Machine Learning Group, Bloomberg
• Richard Callison – brightsolid
• Moderator: Simon King, University of Edinburgh

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