Trust, Fake News and the future of journalism

Looking back on some tumultuous years in journalism, including Donald Trump’s campaign against fake news and the rise of the digital area, we asked Jimmy Wales, founder of Wikipedia and WikiTribune, five quick questions about his view on the current state of trust in journalism.

 

We interviewed Jimmy Wales at the GEN Summit 2018 in Lisbon where he did a session on trust with Matt Kelly (Archant Group) and Ed Williams (Edelman UK) © Rainer Mirau for GEN
 

 

How would you describe the state of trust in journalism?

Journalism has been under huge financial pressure for a few years and somehow lost its way. However, trust is now starting to get back after the public realized that quality journalism matters.

 

Access to Wikipedia is free. Does that mean that trust is free?

Trust is about honesty and this does not really cost anything. The other way round, money can corrupt honesty.

 

How do you go about Fake News?

We have to manage them with trust. In the mainstream and quality media we’ve got to do all things right and share transcripts, audios, … things to prove what we are saying. Only this way we can restore trust and show the people that we are not simply making something up.

 

How do you verify data for Wikipedia?

We verify the data with very old-fashioned techniques, like transcripts, interviews and documents. All of this is very old-fashioned journalism. If you look at later techniques, data journalism, for instance, is a very important tool in journalism of the modern world. So much can be learned from large sets of data, particularly financial contributions to politicians. It is a rich source of very good information.

 

How do you see the future of journalism?

I am optimistic about journalism in the future because it is a core function in society. And even if the transition from digital business models has been very difficult, I do not think that the public does not care about the truth anymore. They do. We just have to find models to make it work!

 

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Michaela Gruber is a journalism and media management student, based in Vienna, Austria. During her studies she spent a semester abroad in France, where she started working for HEI-DA.

As the company’s communication officer, she is in charge of the Data Journalism Blog and several social media activities. This year, Michaela was HEI-DA’s editor covering the Data Journalism Awards in Lisbon, Portugal.

 

 

Tips on local data journalism from Alan Renwick

Newspapers which tend to primarily focus on local issues, often represent the majority of media outlets in each country’s media landscape. They deliver a number of important journalistic functions, like holding local authorities to account, supporting democratic and civic needs and shaping the overall reputation of journalism. Thanks to their powerful human interest reporting, they shine a spotlight on local issues and offer unique local content that is is not necessarily found elsewhere.

 


As co-funder of the startup Urbs Media and director of the RADAR project, Alan Renwick has over 30 years experience in local, national and international news industries. Within his work, he provides local newsrooms with a regular feed of data-driven stories and scale up local news production by combining the work of journalists and Artificial Intelligence.

 

 

In this interview, he tells us why local journalists should not hesitate to use data within their work and how to turn national datasets into local content:

 

How can local journalists use data in their reporting?

There are many things that local journalists can do with data journalism. They already have their own data journalism efforts, like their own datasets, they are investigating their own local issues, and they have a lot of unique content. It is all just starting on a national level with national datasets but after that, local journalists have to unlock the fact that these datasets have got a lot of local information within them and can be used complementary to anything else they are doing. So they might put some local interviews around it or make some analysis and build it up in a much more colourful, local story.

 

The Bureau Local is a collaborative, investigative journalism network in the UK which stands as a great example of how data journalism is thriving

 

You are the director of RADAR (Reports And Data And Robots), a news agency that combines humans and machines to deliver data-based news stories to local newsrooms. How can local journalists make use of those stories?

What we do with RADAR is to hopefully give those local newsrooms skills and raw material to work with. This comes in form of a regular feed of data- driven stories for that they might normally not have the time and resources to look at. The reason for that is that most of them have quite a high philosophy behind them and there is a high number of data stories around.

So, what we actually do then is to take national datasets and do a fusion of local and national journalism. That means, we try to understand the generics of national data and see how these stories might vary for every local area. Then we look at the different variables to figure out how many different outcomes there might be and write story templates for every eventuality. For instance, we might put the numbers from a spreadsheet into a sentence such as “since w, house prices in x have increased by y/fallen by z/stayed the same”. This means that if you have 500 rules you might have 500 different stories. From the same data you might have different types of stories with different headlines and different content.

 

RADAR is a collaboration between the Press Association and Urbs Media that delivers news stories to local media, combining the work of humans and machines.

 

What is your top advice for local journalists who want to work with data?

Many local newsrooms want to write very few numbers into their stories even if all the sources come from data. That is why my advice for local journalists is to treat data journalism just like any other source. There might be a broader, softer story that comes from it. They just have to understand the genesis of it, where it comes from, then integrate it, be sure of what it means, what the facts are and then create the story in their very traditional way.

Learn the data skills but as you use them, use them in the way that you would use any other source.

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Michaela Gruber is a journalism and media management student, based in Vienna, Austria. During her studies she spent a semester abroad in France, where she started working for HEI-DA.

As the company’s communication officer, she is in charge of the Data Journalism Blog and several social media activities. This year, Michaela was HEI-DA’s editor covering the Data Journalism Awards in Lisbon, Portugal.

 

Tips on news product prototypes from Bella Hurrell

We asked Bella Hurrell, Deputy Editor of the BBC News Visual Journalism Team, about what makes a good product prototype and what are the challenges that you have to face when building them. In this video, she shares with us the tools that the BBC uses for building their prototypes and what their vision is.

Build quick and dirty prototypes that you can test with people. Don’t invest huge amounts of time in something if you are not that sure about it […] and give up when it is a good time to do it!

 

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Michaela Gruber is a journalism and media management student, based in Vienna, Austria. During her studies she spent a semester abroad in France, where she started working for HEI-DA.

As the company’s communication officer, she is in charge of the Data Journalism Blog and several social media activities. This year, Michaela was HEI-DA’s editor covering the Data Journalism Awards in Lisbon, Portugal.

 

Tips on building chat bots from Quartz’s John Keefe

When talking to John Keefe, Product Manager & Bot Developer at Quartz, he encourages the journalism community to experiment with chat bots and try different tools. In this video, he shares some tips and tricks with us on what platforms to use and how journalists can build chat bots themselves.

Building chat bots is not as hard as it seems!
I would say, just give it a try!

 

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Michaela Gruber is a journalism and media management student, based in Vienna, Austria. During her studies she spent a semester abroad in France, where she started working for HEI-DA.

As the company’s communication officer, she is in charge of the Data Journalism Blog and several social media activities. This year, Michaela was HEI-DA’s editor covering the Data Journalism Awards in Lisbon, Portugal.

 

From Asia and beyond: experts discuss data journalism challenges

This article was originally published on the Data Journalism Awards Medium Publication managed by the Global Editors Network. You can find the original version right here.

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How easy (or difficult) is it to access data in China, Malaysia, Kenya, and other countries? Are there tested business models for data journalism in different parts of the world? How do you promote data literacy in newsrooms where innovation is not a priority? We’ve gathered international experts to tackle those questions, and discuss government interference, the pace of learning, and managerial issues.

 

 

Darren Long, head of graphics at South China Morning Post (Hong Kong), Kuek Ser Kuang Keng from Data N and former Google fellow at PRI (Malaysia), Eva Constantaras, Google Scholar from Internews and expert in data journalism for Africa, Asia and South America (originally from Greece), and Yolanda Ma from Data Journalism China, also jury member of the Data Journalism Awards competition (China), all joined us, as well as participants from other countries.

 

From left to right: Darren Long, Yolanda Ma, Eva Constantaras and Kuek Ser Kuang Keng

 

 

How widespread would you say data journalism is in your region?

 

Kuek Ser Kuang Keng: People like to see Southeast Asia as a ‘region’ but the fact is countries in this region are very diverse in terms of development stage, politics, and technology. So there’s no way to generalise them.

In Malaysia, my own country, data journalism is almost non-existent; there are only infographics. There is a strong interest among a small group of journalists, but they lack support from editors and management, who focus more on social media. Innovation in journalism is not prioritised. In neighbouring countries, such as Indonesia and the Philippines, things might be a little better, but they are still relatively far behind the West. In non-democratic countries where free press is always under siege like Cambodia, Vietnam, Laos, and Thailand, the landscape is totally different. There, the survival of independent journalism is above all other things like innovation.

Darren Long: It’s a good point. I was going to say Europe and America can feed off each other through the use of English language and a common Roman script whereas Asia is much more diverse. Press freedom is certainly an issue. Even in Hong Kong where we have a feisty and largely free press.

Visual journalism and the use of data is a good way to avoid government interference though. If you can use data to make your point from government sources, there is little they can criticise. The problem is getting public and government data. It is very hard to get consistent and reliable sources from Mainland China.

 

Yolanda Ma: In mainland China, since data journalism was introduced five years ago, it has been widely accepted and adopted by media organisations, from official newspapers to commercialised online portals. The development is limited due to the cost (both technical and human resources). It is more recognised by the industry than by the public.

Eva Constantaras: My specialty is introducing data journalism in countries where it basically doesn’t exist. General trends I see are: publishers get excited because it sounds digital and visual and sexy, mid-level editors and senior reporters are in denial about digital convergence and are afraid of it so don’t want to know anything about it, and early career journalists are excited about it for three reasons: 1. They want to still have a job once digital convergence happens 2. They think data visualisation looks fun and 3. (least common) they see how data can enrich their public interest reporting by making their stories more analytical.

 

How accessible is public data in your country? What advice do you have on how to access data (public or else)?

 

Darren Long: We have freedom of information but it’s a fine line.

Here are some useful websites: Open Data Hong Kong, Data.gov.hk and N3Con 2018.

Kuek Ser Kuang Keng: There’s no FOI in Malaysia, Singapore and other non-democractic Southeast Asian countries but it exists in Indonesia and the Philippines. While sensitive information is not available, Malaysia and Singapore governments do publish a lot of data online. Both countries have a dedicated open data portal and relevant policies.

However media in both countries don’t have a strong demand for government data nor the skill, knowledge, and habit to use data in their reporting. The main demand comes from the business/IT community which is adopting business analytics very fast. So before talking about accessing any data, there need to be awareness, skill, and knowledge within newsrooms on data journalism. It seems like this awareness is higher in Indonesia and the Philippines. There’s a specialised business data news startup in Indonesia called Katadata, that you may want to check out:

 

 

Eva Constantaras: The first excuse I get from journalists for not doing data journalism is that there isn’t enough data. In all the countries I have been in, I would not say that is among even the top 3 challenges. And partially that’s because nobody has ever used the little data there is, so they need to build up demand in order for more data to be released. The biggest challenge is finding journalists who are willing to abandon their jobs as stenographers and embrace their role as knowledge producers. This is not a problem data or technology can solve.

Darren Long: I agree with that. I find a lot of the problem is more about thinking how to visualise data in a creative manner than the non-existence of data.

Yolanda Ma: People usually have the impression that China doesn’t have much data but the reality is quite the opposite. There is tons of data, just not well published and usually unstructured. Sometimes the data is inaccurate and not reliable. There is a FOI regulation and media do use it for stories, but less for data.

But things are getting better, compared with five years ago. In China more data is released (effort has been made to convince government and also help them to get it right), the open data movement is still on and pushing for better data culture, especially collaboration between universities, companies, government, but also NGOs and citizens.

 

What are the main challenges data journalists face in your region?

 

Eva Constantaras: I think journalists underestimate the work that goes into a data story. It’s not enough to just use data to reveal the problem because of the ubiquity of corruption in so many countries. For a story to have an impact and get people’s attention, it has to measure the problem, the causes, the impact on citizens and potential solutions. That’s more work than journalists are used to. Many journalists just want to make visualisations. I tell them visualisations are the paint on the house. Their house can be a beautiful colour but if their analysis is bad, their structure is unsound, their pretty house will fall down.

Darren Long: Technology has been an issue for us. We have to create our infographics outside the company CMS and redirect the page. If we weren’t so stubborn we would have given up long ago

Kuek Ser Kuang Keng: Newsroom managers don’t have much awareness of data journalism and the digital disruption has put news companies in a tough position financially. The limited resources that news companies can allocate have been put into ‘hot’ fields like social media and video. A good number of journalists are eager to learn new skills but they don’t get much support to pick up new skills and put those skills into use. I wish technology was an issue in Malaysia. We don’t even have data or interactive team in newsrooms here. I’m the only data journalist in Malaysia.

Yolanda Ma: Talent is an issue everywhere, but the challenge beyond that is the cost — the cost to develop the skills and to maintain such a team in the newsroom. Many data stories in China are now going video or motion graphics as well to stay aligned with consumer trends.

Here is an example of data journalism on TV:

 

Parcels from Faraway Places (subtitles in English)

 

How do you overcome these challenges? What creative solutions could we find for them?

 

Kuek Ser Kuang Keng: How to overcome? I find the main hurdle lies with managers and editors, so I would approach them to provide them a better understanding of data journalism — the potential, impacts and costs, or talent needed. Another good way is to build networks among journalists who share the same interests, so they can support each other, and exchange ideas on how to convince their bosses.

Money is a huge problem in Malaysia. The digital disruption has put news companies in a tough position financially. They want something that can see quick returns, often financially

Eva Constantaras: I think we have to abandon the myth that learning data journalism is ‘fast’, something that can be picked up at a bootcamp. Someone should do a data study of how many data journalists come out of bootcamps. And how many statistically unsound stories came out by the few who did manage to produce a data story.

We want data journalism to be taken seriously so we need a serious approach to capacity building. I have a 200-hour training and production model bringing together journalists, civic hackers, and CSOs with data that has worked in a couple of countries but usually because we found committed journalists who were willing to be the lone data journalist in their newsroom. And we do a lot of outreach and convincing of editors and publishers.

 

Are there any tested business models (other than grants) for data journalism in developing countries?

 

Question from Stephen Edward (Astat Consulting, India)

Kuek Ser Kuang Keng: Unfortunately, not that I know of, but you can keep a watch on Katadata, a specialised data business news startup in Indonesia. They will increase their monetisation efforts soon.

Eva Constantaras: The only media outlet in a developing country that really sees a lot of revenuee coming from their data work is Nation Newsplex in Kenya, and part of that is because the Nation Media Group can repurpose the online data content for two different print publications and their television station. It’s still a very small team.

 

 

Donor support is also often not well structured. They want to give data reporting grants in countries without data reporters. Or they want to give funding for one-off projects that then die a slow death. It’s expensive to train and sustain a data team and most donors don’t make that investment.

Yolanda Ma: One business model that a newsroom is trying (not proved yet) is the think tank approach — they really specialised in urban data, so by digging into data and finding trends, they can actually provide the product for policy makers, urban design industry, etc.

When one data team do very well within the news organisations — another way to go is to spin off. Caixin’s former data head set up his own company last year and it provides service to other media organisations on data stories production now.

The good thing about spinning off is that you do not need to only do journalism projects — which are usually not that profitable. But by being independent you can do commercial projects as well.

Eva Constantaras: The nice thing about spinning off is also then data content can be distributed through a variety of popular media and reach a larger audience.

 

 

What can we do to get more high quality data journalism projects from the Global South? And, given that it is harder for the Global South to compete with the Global North, is there a way to build more recognition for the south?

 

Question from Ben Colmery (ICFJ Knigt Felllowships director, USA)

Yolanda Ma: There are some quite high quality data journalism projects in the South and they don’t have to compete with the North.

Kuek Ser Kuang Keng: As I mentioned earlier, there are far less reporting about the innovations including data journalism projects done by news organisation in Asia. We don’t have Nieman Lab or Poynter here (fortunately we still have djchina.org but it is in Chinese). There are good projects, often done in tough environment, but they don’t get much attention outside of their own country. I can see more and more projects from Latin America were featured in journalism portals but that kind of treatment has not reached Asia. However, language remains a challenge.

Eva Constantaras: I am not sure why they would need to compete since they have different audiences. Though one revenue model I am very interested in is encouraging Western media outlets to buy content from data journalists in the Global South instead of parachutting in their own expensive journalists who do superficial stories.

I think now the West has realized that it needs to do more data-driven reporting on the local level for rural and less educated audiences about issues they care about. I think that the value of data journalism in developing countries is exposing the roots of inequality and helping citizens make better decisions and push for a more accountable government on a local level. Those projects don’t have to be flashy. They just have to be effective and accurate.

Darren Long: I think what international news outlets do well is broad comparative visualisations based around strong concepts. I think we tend to over rely on charts and graphics in Asia.

What is interesting right now is how a market like China has incredibly deep reach through mobile phones. Massive markets do everything on their phone. The tier one cities are easily as sophisticated as the West in that area.

So if we can leverage consumption of dataviz on mobile there should be a massive appetite

 

Can you share one tip you wish you’d been given about data journalism in the region you work in?

 

Yolanda Ma: I’d say, in Asia, do start looking for opportunities for cross-border data stories.

Eva Constantaras: Identify questions that citizens need answered to improve their quality of life and build your data stories around answering those questions.

Kuek Ser Kuang Keng: Data journalism takes time and patience. Visualisation is usually the quickest and easiest part!

Yolanda Ma: To echo Eva’s point — yes, don’t just produce meaningless fancy visuals.

 

Examples of data journalism from around the world that you should go and check out:

 

Darren Long: The Singapore Reuters office is producing some stunning multimedia data visualisations.

Here’s one they did on the oil spill off China:

 

 

But they have international resources and can recruit from all over the world

Here’s an example of a story we did at South China Morning Post. The data was from the government, but they didn’t like the story. If you click on our source, the page opens with a great big disclaimer they added after we didnt take our page down:

 

 

The map itself is still up:

 

 

A few more that I like:

 

 

 

 

Kuek Ser Kuang Keng: Tempo is a highly respectable magazine in Indonesia that produces great investigative reports. But most of their data journalism projects are on print. Here’s a deck shared by their editor-in-chief that showcase some of their data stories.

 

 

Malaysiakini is also working hard in data journalism. I recently collaborated with them to produce the first newsgame in Malaysia. It explains the issue of malapportionment in Malaysian election system.

 

 

Yolanda Ma: Here is a deck I made on data journalism in China a year ago — it serves as a good overview for anyone who’s interested:

 

 

Other organisations from China you should check out: Caixin, the Paper/SixthTone, Yicai, DT.

I like IndiaSpend in India and Katadata in Indonesia too.

Eva Constantaras: Here’s an example of a story that might have been risky without government data:

 

 

Some of my favourites are IndiaSpend and Hindustan Times in India, Daily Nation Newsplex in Kenya, Ojo Publico in Peru and both La Nacion Argentia and Costa Rica.

Kuek Ser Kuang Keng: I agree with Yolanda and Eva, at the reporter level, a good number of journalists are eager to learn a new skill but they don’t get much support from editors or managers to pick up new skills and put those skills into use.

I would recommend Rappler in the Philippines, Katadata and Tempo in Indonesia. But only Katadata has a dedicated vertical for data stories

 

 

 


 

To see the full discussion, check out previous ones and take part in future ones, join the Data Journalism Awards community on Slack!

Over the past six years, the Global Editors Network has organised the Data Journalism Awards competition to celebrate and credit outstanding work in the field of data-driven journalism worldwide. To see the full list of winners, read about the categories, join the competition yourself, go to our website.


marianne-bouchart

Marianne Bouchart is the founder and director of HEI-DA, a nonprofit organisation promoting news innovation, the future of data journalism and open data. She runs data journalism programmes in various regions around the world as well as HEI-DA’s Sensor Journalism Toolkit project and manages the Data Journalism Awards competition.

Before launching HEI-DA, Marianne spent 10 years in London where she worked as a web producer, data journalism and graphics editor for Bloomberg News, amongst others. She created the Data Journalism Blog in 2011 and gives lectures at journalism schools, in the UK and in France.

Discussing the ethics, challenges, and best practices of machine learning in journalism

This article was originally published on the Data Journalism Awards Medium Publication managed by the Global Editors Network. You can find the original version right here.

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Peter Aldhous of BuzzFeed News and Simon Rogers of the Google News Initiative discuss the power of machine learning in journalism, and tell us more about the groundbreaking work they’ve done in the field, dispensing some tips along the way.

 

Machine learning is a subset of AI and one of the biggest technology revolutions hitting the news industry right now. Many journalists are getting excited about it because of the amount of work they could get done using machine learning algorithms (to scrape, analyse or track data for example). They enable them to do tasks they couldn’t before, but it also raises a lot of questions about ethics and the ‘reliance on robots’.

 

BuzzFeed’s ‘Hidden Spy Planes

 

Peter Aldhous is the brain behind BuzzFeed News’s machine learning project ‘Hidden Spy Planes’. The investigation revealed how US airspace is buzzing with surveillance aircraft operated for law enforcement and the military, from planes tracking drug traffickers to those testing new spying technology. Simon Rogers is data editor for Google (who’s also been contributing to some great work on machine learning, including ProPublica’s Documenting Hate project which provides trustworthy facts on the details and frequency of hate crimes).

We asked both of them to sit down for a chat on the Data Journalism Awards Slack team.

 

What is it about AI that gets journalists so interested? How can it be used in data journalism?

Peter Aldhous: I think the term AI is used way too widely, and is mostly used because it sounds very impressive. When you say ‘intelligence’, mostly people think of higher human cognitive functions like holding a conversation, and sci-fi style androids.

But as reporters, we’re often interested in finding the interesting things from a mass of data, text, or images that’s too big to go through manually. That’s something that computers, trained in the right way, can do well.

And I think machine learning is a much more descriptive and less pretentious label for that than AI.

Simon Rogers: There is a big gap between what we’ve been doing and the common perception of self aware machines. I look at it as getting algorithms to do some of the more tedious work.

 

Why and when should journalists use machine learning?

P.A.: As a reporter, only when it’s the right tool for the job — which likely means not very often. Rachel Shorey of The New York Times was really good on this in our panel on machine learning at the NICAR conference in Chicago in March 2018.

She said things that have solved some problems almost as well as machine learning in a fraction of the time:

– Making a collection of text easily searchable;

– Asking a subject area expert what they actually care about and building a simple filter or keyword alert;

– Using standard statistical sampling techniques.

 

What kind of ethical/security issues does the use of machine learning in journalism rise?

P.A.: I’m very wary of using machine learning for predictions of future events. I think data journalism got its fingers burned in the 2016 election, failing to stress the uncertainty around the predictions being made.

There’s maybe also a danger that we get dazzled by machine learning, and want to use it because it seems cool, and forget our role as watchdogs reporting on how companies and government agencies are using these tools.

I see much more need for reporting on algorithmic accountability than for reporters using machine learning themselves (although being able to do something makes it easier to understand, and possible to reverse engineer.)

If you can’t explain how your algorithm works to an editor or to your audience, then I think there’s a fundamental problem with transparency.

I’m also wary of the black box aspect of some machine learning approaches, especially neural nets. If you can’t explain how your algorithm works to an editor or to your audience, then I think there’s a fundamental problem with transparency.

S.R.: I agree with this — we’re playing in quite an interesting minefield at the moment. It has lots of attractions but we are only really scratching the surface of what’s possible.

But I do think the ethics of what we’re doing at this level are different to, say, developing a machine that can make a phone call to someone.

 

‘This Shadowy Company Is Flying Spy Planes Over US Cities’ by BuzzFeed News

 

 

What tools out there you would recommend in order to run a machine learning project?

P.A.: I work in R. Also good libraries in Python, if that’s your religion. But the more difficult part was processing the data, thinking about how to process the data to give the algorithm more to work with. This was key for my planes project. I calculated variables including turning rates, area of bounding box around flights, and then worked with the distribution of these for each planes, broken into bins. So I actually had 8 ‘steer’ variables.

This ‘feature engineering’ is often the difference between something that works, and something that fails, according to real experts (I don’t claim to be one of those). More explanation of what I did can be found on Github.

 

There is simply no reliable national data on hate crimes in the US. So ProPublica created the Documenting Hate project.

 

S.R.: This is the big change in AI — the way it has become so much easier to use. So, Google hat on, we have some tools. And you can get journalist credits for them.

This is what we used for the Documenting Hate project:

 

 

It also supports a tonne of languages:

 

 

With Documenting Hate, we were concerned about having too much confidence in machine learning ie restricting what we were looking for to make sure it was correct.

ProPublica’s Scott Klein referred to it as an ‘over eager student’, selecting things that weren’t right. That’s why our focus is on locations and names. Even though we could potentially widen that out significantly

P.A.: I don’t think I would ever want to rely on machine learning for reporting. To my mind, its classifications need to be ground-truthed. I saw the random forest model used in the ‘Hidden Spy Planes’ story as a quick screen for interesting planes, which then required extensive reporting with public records and interviews.

 

What advice do you have for people who’d like to use machine learning in their upcoming data journalism projects?

P.A.: Make sure that it is the right tool for the job. Put time into the feature engineering, and consult with experts.

You may or may not need subject matter expert; at this point, I probably know more about spy planes than most people who will talk about them, so I didn’t need that. I meant an expert in processing data to give an algorithm more to work with.

Don’t do machine learning because it seems cool.

Use an algorithm that you understand, and that you can explain to your editors and audience.

Right tool for the job? Much of the time, it isn’t.

Don’t do this because it seems cool. Chase Davis was really good in the NICAR 2018 panel on when machine learning is the right tool:

  • Is our task repetitive and boring?
  • Could an intern do it?
  • If you actually asked an intern to do it, would you feel an overwhelming sense of guilt and shame?
  • If so, you might have a classification problem. And many hard problems in data journalism are classification problems in disguise.

We need to do algorithmic accountability reporting on ourselves! Propublica has been great on this:

 

But as we use the same techniques, we need to hold ourselves to account

S.R.: Yep — this is the thing that could become the biggest issue in working with machine learning.

 

What would you say is the biggest challenge when working on a machine learning project: the building of the algorithm, or the checking of the results to make sure it’s correct, the reporting around it or something else?

 

P.A.: Definitely not building the algorithm. But all of the other stuff, plus feature engineering.

S.R.: We made a list:

  • We wanted to be sure, so we cut stuff out.
  • We still need to manually delete things that don’t fit.
  • Critical when thinking about projects like this — the map is not the territory! Easy to conflate amount of coverage with amount of hate crimes. Be careful.
  • Always important to have stop words. Entity extractors are like overeager A students and grab things like ‘person: Man’ and ‘thing: Hate Crime’ which might be true but aren’t useful for readers.
  • Positive thing: it isn’t just examples of hate crimes it also pulls in news about groups that combat hate crimes and support vandalized mosques, etc.

It’s just a start: more potential around say, types of crimes.

I fear we may see media companies use it as a tool to cut costs by replacing reporters with computers that will do some, but not all, of what a good reporter can do, and to further enforce the filter bubbles in which consumers of news find themselves.

 

Hopes & wishes for the future of machine learning in news?

P.A.: I hope we’re going to see great examples of algorithmic accountability reporting, working out how big tech and government are using AI to influence us by reverse engineering what they’re doing.

Julia Angwin and Jeff Larson’s new startup will be one to watch on this:

 

 

I fear we may see media companies use it as a tool to cut costs by replacing reporters with computers that will do some, but not all, of what a good reporter can do, and to further enforce the filter bubbles in which consumers of news find themselves.

Here’s a provocative article on subject matter experts versus dumb algorithms:

 

 

 

Peter Aldhous tells us the story behind his project ‘Hidden Spy Planes’:

‘Back in 2016 we published a story documenting four months of flights by surveillance planes operated by FBI and Dept of Homeland Security.

I wondered what else was out there, looking down on us. And I realised that I could use aspects of flight patterns to train an algorithm on the known FBI and DHS planes to look for others. It found a lot of interesting stuff, a grab bag of which mentioned in this story.

But also, US Marshals hunting drug cartel kingpins in Mexico, and a military contractor flying an NSA-built cell phone tracker.’

 

Should all this data be made public?

Interestingly, the military were pretty responsive to us, and made no arguments that we should not publish. Certain parts of the Department of Justice were less pleased. But the information I used was all in the public, and could have been masked from flight the main flight tracking sites. (Actually DEA does this.)

US Marshals operations in Mexico are very controversial. We strongly feel that highlighting this was in the public interest.

 

About the random forest model used in BuzzFeed’s project:

Random forest is basically a consensus of decision tree statistical classifiers. The data journalism team was me, all of the software was free and open source. So it was just my time.

The machine learning part is trivial. Just a few lines of code.

 

 

If you had had a team to help with this, what kinds of people would you have included?

Get someone with experience to advise. I had excellent advice from an academic data scientist who preferred not to be acknowledged. I did all the analysis, but his insights into how to go about feature engineering were crucial.


marianne-bouchart

Marianne Bouchart is the founder and director of HEI-DA, a nonprofit organisation promoting news innovation, the future of data journalism and open data. She runs data journalism programmes in various regions around the world as well as HEI-DA’s Sensor Journalism Toolkit project and manages the Data Journalism Awards competition.

Before launching HEI-DA, Marianne spent 10 years in London where she worked as a web producer, data journalism and graphics editor for Bloomberg News, amongst others. She created the Data Journalism Blog in 2011 and gives lectures at journalism schools, in the UK and in France.

Tips on cross-border collaborations from Mar Cabra

We got to talk to Mar Cabra, the former head of Data & Technology at the International Consortium of Investigative Journalists (ICIJ), about what makes a great cross-border collaboration. In this video, she shares with us some great tips for news teams around the world, big or small, who’d like to pump up their data investigation skills.

 

When collaborating, especially when it’s remote, includes data or goes across borders, you should think hard about what technology you’re going to use and what is the set of tools. Establish that from the beginning so that they are no complications later.

Most importantly, you need people that know how to collaborate and that are happy sharing. If not, your collaboration will fail.

Try to connect with people who have done collaborations before, do not re-invent the wheel.

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Michaela Gruber is a journalism and media management student, based in Vienna, Austria. During her studies she spent a semester abroad in France, where she started working for HEI-DA.

As the company’s communication officer, she is in charge of the Data Journalism Blog and several social media activities. This year, Michaela was HEI-DA’s editor covering the Data Journalism Awards in Lisbon, Portugal.

 

Counting crime: How journalists make sense of police data

This article was originally published on the Data Journalism Awards Medium Publication managed by the Global Editors Network. You can find the original version right here.

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Takeaways from a discussion with experts behind two of the most compelling data projects tackling crime in the US

 

As a journalist, how do you go about accessing, verifying and visualising datasets on crime and police?

 

Accessing crime and police data is crucial given the amount of shootings and police violence brought into the headlines through cases like Freddy Gray and Philando Castile.

As a journalist, how do you go about accessing, verifying, and visualising datasets on this topic? What kind of ethical questions does that raise? How do you protect the victims? We gathered experts to find out.

 

Is crime in America rising or falling? The answer is not nearly as simple as politicians sometimes make it out to be.

 

Tom Meagher is deputy managing editor for The Marshall Project which has been publishing some of the most compelling crime data journalism of the past few years. Their project Crime in Context won a Data Journalism Awards 2017 prize for its analysis of 40 years worth of national and local crime data. The Next to Die has been tracking every execution in the US for the last two years in close to real time.

Ciara McCarthy is a journalist who worked on Guardian US’s The Counted project, often referred to as an industry benchmark. It counts the number of people killed by police and other law enforcement agencies in the US throughout 2015 and 2016 to monitor their demographics and to tell the stories of how they died.

Both of them joined us during a Slack discussion dedicated to crime and police data at the beginning of November. This article gathers the best tips and advice they dared to share.

 

The Counted is the most thorough public accounting for deadly use of force in the US

 

What makes working with crime or police data different from working with any other type of data?

 

Tom Meagher: Oh, where to begin? In the US, there are a few things that make criminal justice data a little more complicated than in most other beats. First, there’s a presumption of innocence for people accused of crimes until their case works its way through the court systems. So we want to be mindful of how the people our data represents are considered. Not everyone arrested is guilty, but with data it can be easy to overlook that key fact sometimes.

And more practically, in the US the data is so, so fragmented. There are 18,000+ police agencies and thousands of courts that all seem to keep their data in their own way (if they keep it at all). It makes it really challenging to carry out national analyses of how parts of the criminal justice system are operating. There are very few one-stop-shops for data.

Ciara McCarthy: I think, for us at The Counted at least, the main issue we set out to fix was that the data we wanted to analyse and investigate simply didn’t exist. There was no comprehensive or reliable information about how many people died in police custody in the US (although there is lots of available data, of varying reliability, about other pieces of the criminal justice system).

I think that a lot of criminal justice data […] might not be complete or accurate if it’s even been collected. And to echo Tom, that’s the other main issue: With no central body keeping track of the data we were looking at, it was hard to monitor thousands of different law enforcement agencies, all of which follow slightly different policies and standards for releasing information and communicating with reporters.

Although the FBI ‘collects’ this data, it’s wildly inaccurate, and underestimates the true number of people who die in police custody at least by half. It’s optional for police departments to submit their information to the FBI, meaning that most don’t end up doing it.

 

Previously unpublished data revealed only 224 of 18,000 US law enforcement agencies reported fatal shootings in 2014 sheds new light on flawed system

 

So would your advice be to ‘build your own data’?

 

Ciara McCarthy: I think it depends! Once our team started reporting on this issue in particular, it was clear that, at least for deaths in custody, the information the federal government had would have resulted in deeply flawed analyses. But in other areas of the US criminal justice system, the data collected by the government is usable — I think it’s a matter of asking a lot of questions of an available data set before you get started and seeing whether you can make reliable analyses. And if you can’t, then yes! Build your own data.

Tom Meagher: It seems like at The Marshall Project, for nearly every significant investigative story we do, the data doesn’t exist. We have to build it ourselves. As an example, here’s a story I wrote about just a few of the really key criminal justice questions we can’t answer in the US because the data doesn’t exist.

 

After the deaths of Freddie Gray and Laquan McDonald and others — in an age when police in many cities are under greater scrutiny than they’ve been in decades — how is it that we know so little about how officers employ force to subdue suspects?

 

As data is tough to get hold of, do you have tips on how or WHERE to find crime and police data?

 

Tom Meagher: When we’re approaching a story, we have to craft a new strategy every time. For Crime in Context, we had a trove of 40+ years of the federal Uniform Crime Reporting data, but then we had to go back and contact individual police agencies to fill in dozens and dozens of holes we identified.

Then we had to call 70+ police agencies to get them to release the previous year’s data (this was in August) because the FBI didn’t have it yet. We could flag missing records in the data or reports that were suspicious (how could they have -30 assaults in a month?) and had to report each of those out. My friend Steven Rich at the Washington Post likes to say ‘the phone is the most important tool for data journalism’.

Ciara McCarthy: For us at The Counted we basically went from agency to agency to request and ask for the data. Sometimes we had to request the information under public records law, and sometimes the information (or the basics, at least) were easily distributed. The Counted was a little different from some data analysis projects in that it was live: We added new cases of people killed by police to the database each day.

 

How do you verify data related to crime and the police, especially when victims come forward to denounce wrongdoing? Any tips or best practice on crowdsourcing for such projects, and establishing trust with sources?

 

Tom Meagher: We tend to rely on official court records — lawsuit filings, courtroom testimony, decisions — and on other journalists to help us vet information. Our executions project, The Next to Die, is a sort of journalistic crowdsourcing, where we work with reporters and editors in eight other news organisations to help us amass the information that goes into our database.

 

The Next to Die aims to bring attention, and thus accountability, to upcoming executions.

 

Ciara McCarthy: A few things I’d point out from our project: First, for us, when we couldn’t give a definitive answer, we noted it (see an example right here). I think part of the genius behind our very brilliant interactive journalists who built the database was they created one that could adapt to our reporting needs as we added to the database.

So if police said someone was armed with a knife, but witnesses said the person had dropped the knife before the shooting, we usually label that ‘disputed’ in our database, and then pursue additional information to try and get a clear answer. In cases of people killed by police, the first piece of information almost always comes from authorities, and that information may or may not be true. So if there are witnesses (often there aren’t) we’ll talk to them to see if they saw something different.

Secondly, we considered The Counted to be a crowdsourced database, meaning that our readers could reach out and contact us with tips at any time. We had a ‘tip line’ of sorts on our website and we also got information from readers via Facebook, Twitter, and email. Most of the time, the people reaching out to us weren’t sources with sensitive or story-cracking information, but readers with questions about the project or people alerting us to new cases. Sometimes, though, family members of the deceased would reach out to dispute law enforcement’s characterisation of the incident, and when that happened we’d follow up on whatever information they gave us.

 

The Guardian US had a “tip line” on their website and also got information from readers via Facebook, Twitter and via email

 

Have you ever been worried of the backlash or bad impact your projects could have?

 

Tom Meagher: We try to operate in a ‘no surprises’ manner. We go to great lengths to let our subjects know what’s coming out and to give them an opportunity to respond ahead of time. A big story my colleagues undertook on these programmes where you can pay money to stay in safer or nicer jails relied heavily on freedom of information requests and data compiled from more than 25 different police jurisdictions (screenshot below). If you look at the methodology, they describe how they did the analysis and how they took it to each of those police agencies a few weeks before publication to give them a chance to dispute or comment on the analysis.

 

In what is commonly called “pay-to-stay” or “private jail,” a constellation of small city jails — at least 26 of them in Los Angeles and Orange counties — open their doors to defendants who can afford the option

 

As far as protecting sources from legal or physical harm, we’re very mindful of that. We go to great lengths to get our sources to go on the record, but if we think they’re potentially in jeopardy, we will allow them to be anonymous, provided we can vet their story independently. We don’t want to put anyone at risk of losing their jobs or of physical harm.

Ciara McCarthy: No one on our team personally encountered any threats or danger as a result of The Counted project as far as I know; I’d say the worst I personally encountered was a few mean tweets and a few terse phone calls with law enforcement officials who weren’t happy about the project. We also didn’t have a ton of anonymous sources whose identity we needed to protect (which I don’t think is something we expected starting out).

Most of the time, if witnesses or family members contradicted the police account, these (very brave) people did so pretty publicly. See, for example this article (screenshot below) telling the story of an American who filmed police violence. If there were cases where our reporters were working with anonymous sources, they were very cautious and made sure those who were providing information knew what publishing their accounts entailed.

 

When Feidin Santana filmed Walter Scott’s death, it marked a turning point in the US civil rights movement — and in Santana’s life. He and others who have taken the law into their own hands tell their stories

 

Do you encounter difficulties in streamlining key definitions (for example ‘armed’ vs ‘unarmed’, or ‘Police custody’), especially when gathering data from multiple sources? How do you resolve these differences?

 

Tom Meagher: Oh yes, all the time. We find that different agencies or different states will often use the same words but have completely different meanings. In one state, for example, they may have a crime called ‘battery’ that in a different state would be labelled ‘assault’. We first try to make sure that we understand exactly what each term means to each source. We start with getting their data dictionary (or record layout or user’s manual) to see how they define it in print. Then we’ll follow up with interviews with agency personnel to confirm our understanding of the terms. Ultimately, we’ll often create our own categorization scheme that is hopefully more accessible to readers to describe each class of records we see in the data.

In the Pay to Stay story, we had 25+ agencies all using different terms to refer to a fairly arcane set of state statutes that you really needed a law degree to understand. With lots of reporting work, we were able to generally class them as types of crimes with colloquial names (Drugs, Driving Violations) that were still accurate to the legal definitions, muddled as they were. It ultimately made it easier for our readers to grasp the importance of the different types of crimes being reported on.

“Often in data reporting, it’s tempting to be lulled into thinking that the ‘official data’ that is provided to you is rational and sensible and ready to be analyzed or visualized. In reality, we find most of the time that it’s a complete mess that requires a lot of reporting before we can even think about analyzing it to inform our reporting.” Tom Meagher (The Marshall Project)

Pay-to-stay is a curated collection of links by The Marshall Project, part of their Records project

 

Ciara McCarthy: We ran into this issue A LOT while working on The Counted project, particularly when it came to defining whether the deceased was armed or unarmed, as you noted. As you can imagine, the law enforcement definition of someone who is armed might differ from what others would consider armed, or the police account might change over time. We ran into this a lot when police shot and killed someone who was driving a car; often, they would say, they opened fire because the person in question was using the car as a weapon. (We did a bigger piece on this here).

That’s obviously super tricky, because it’s difficult to corroborate without video or a witness. A good example of this issue is the case of Zachary Hammond, a teenager who was shot and killed in South Carolina in 2015. Police initially said he drove the car toward the officers, which is why one opened fire. Surveillance footage released later showed that Hammond was driving past the officer, and not directly at him.

So I don’t have an easy answer! Sometimes the only available info we had was from police, but we’d do our best to find other sources when the police account seemed questionable. Basically, it meant a lot of extra reporting and a lot of discussions among our team members.

 

What tips do you have on visualising crime and police data? How and why do you decide whether or not to show people’s name, photo, or personal information?

 

Ciara McCarthy: With The Counted, we had built this big database, and wanted people to be able to use it and explore it and learn from it. That’s a main reason why the database included photos, whenever possible: We really wanted to put a face on each person who had died, so we weren’t only focusing on the overall number of people who died.

As for personal information, we would include what was relevant; so, for example, if a person’s medical or mental health history might have impacted their interaction with authorities, we’d be sure to note that.

 

For regular updates from The Next to Die, follow @thenexttodie on Twitter

 

Tom Meagher’s tips:

  • You want to give your data context.
  • Avoid one-year comparisons.
  • Set it against historical data as much as possible.
  • As you visualize it, try to remember that every record in that database represents a person — someone who was injured or victimized or killed, or someone who has committed crimes.
  • Try to use your visualization to emphasize their humanity as much as you can. Dots or jagged lines sometimes obscure the people they represent

 

Is there one thing you wish someone had told you before you took on The Counted and the Next To Die projects?

 

Tom Meagher: Building your own databases for open-ended projects can be very fulfilling as a journalist. You’re filling a gap in the public’s understanding of an issue. It’s very worthy. But also keep in mind that you’re committing your news organization to an endless project.

Does the story merit your time and your colleagues’ time for the indefinite future? I’d argue that The Counted and the Next to Die do. But you don’t want to make the decision without understanding the costs and all the other reporting you won’t be able to do for the next few years because you’ll have to be updating your database.

Also, these can be very emotionally taxing subjects to report on. You’re spending your entire professional life (and much of your personal life) immersed in stories of violence, and trauma, and misery. Be sure to take care of yourself and give yourself emotional outlets.

 

What do you think could be done to improve things? Do we just need more comprehensive data from authorities compiled in a standardised way?

 

Tom Meagher: The division of powers between local and state and federal governments in the US makes it complicated. There’s realistically not going to ever be a single source for reliable data. What would be a vast improvement would be if more politicians and policymakers embraced the ideas of transparency and accountability, that better, smarter data will help them and the public understand our justice systems, and to make better decisions.

As journalists, we’d certainly benefit from that change in mindset, which is still too rare here.

Ciara McCarthy: It would be lovely to get more comprehensive data, but perhaps that’s just wishful thinking. I think getting data from a variety of sources and different types of data will help — comparing a database of media reports vs. official data, for example. That’s what my team is doing with our project, anyway.

More comprehensive data from authorities would be amazing, of course, but when that’s not an option I think building your project is a great public service for newsrooms to undertake. One of my favourite things about The Counted was that, on the surface, it’s mission and premise was pretty simple: The US government should know how many people are killed by police each year. We don’t, so let’s change that.

There’s obviously a ton of different reporting that can (and should!) be done on issues related to police violence, but one thing I really liked about our project was that, at the heart of it, we were saying that we can’t have this public policy discussion without reliable data. I think having this specific, and sometimes narrow, aim for big journalism projects can be really clarifying, and help you achieve impact.

 

How does it compare in other parts of the world?

 

 

Aun Qi Koh of Malaysiakini (Malaysia): I feel like it’s the opposite problem in Malaysia as the official data comes from just one source, the Interior Ministry/Royal Malaysian Police, but it’s not very detailed, and unfortunately we don’t have many other sources of data because there aren’t many checks and balances on the police.

 

 

Shree D N of Citizen Matters (India): India has the problem of under-reporting crime data. The National Crime Records Bureau is the official data source, but underreporting usually happens. This article has some insights on the issue. The methodology used to record offences leads to under-reporting of rape, abduction and stalking.

 

 

Eva Constantaras is a data journalist and trainer who recently wrote the Data Journalism Manual for the UN Development Program.

 

During our November Slack discussion she shared with us great examples from Kenya, Afghanistan and Turkey:

“I think The Counted inspired so many other media outlets because they realized they could build their own databases using similar data collection techniques but getting away from official sources. The Kenya Nation Newsplex team used mostly media reports to compile its Deadly Force Database.

Pajhwok Afghan News maintains a database of terrorist attacks that is much more detailed than anything the government or international bodies maintain. It’s not too much work because they cover all terrorist attacks anyway so they just have to enter them into the database. And then they can generate monthly stories on trends in terrorism in Kabul and across Afghanistan without too much effort.

This paper on collaboration between civic tech and data journalists I think is also relevant. In Turkey, Dag Media works with a domestic violence NGO to track violence against women. The NGO builds the database and the journalists do the stories.”


 

To see the full discussion, check out previous ones and take part in future ones, join the Data Journalism Awards community on Slack!

Over the past six years, the Global Editors Network has organised the Data Journalism Awards competition to celebrate and credit outstanding work in the field of data-driven journalism worldwide. To see the full list of winners, read about the categories, join the competition yourself, go to our website.

 

Empowering women in media and data journalism

The deadline to apply to the Data Journalism Awards 2018 is fast approaching. As data journalists from around the world have just one week to gather their best data-driven work, we met with Mariana Santos, founder and co-director of Chicas Poderosas, also member of the competition jury. Her vision inspires women in Latin America and around the world. In this interview, she tells us how she wants to share her accomplishments as an innovator in digital media with other women, and to help them succeed as new media leaders.

 

 

Chicas Poderosas has been “changing the face of media, one woman at a time” since 2013. What specific skills do you think are most important for women who work in the media industry?

 

Depending very much in what community women work in, they need specific skills.

If we talk about Bolivia, for example, the women with whom we are working there are mainly radio communicators. They have very basic access to the internet. That is why we first trained them on the basics of how to use Facebook and Twitter, such as creating groups, using hashtags, replying and doing reports with video, audio and text. Then, we organised a 3-day hackathon to show them how to make their own audio podcast.

In other countries like Colombia, for instance, we put the emphasis on fact-checking training to show how to track social media networks, and also how to understand trends. Especially in times of elections, fact-checking is important there, as fake news have been especially spread all around social networks, and it is essential in journalism generally. 

An other skill we train women on is data-scraping, to understand how to work with data. We also organise data visualisation workshops, which are more about design and conceptualization of user journeys, as well as work on interfaces.

So, from technology, arts and journalism – everything comes together. Depending on the community we work in, we change the technology that we are using.

 

 

Why is there a difference between women and men in the media industry?

 

When you look at the development departments within newsrooms, most of them are made of men. I worked at the Guardian from 2010 to 2013, and in a group of 200 people working on development, we were only three women. You can find the same situation in Latin America, only it’s even worse. 

We want to change this! We want women to grab their future with their own hands, and understand that technology is not only the future but also the present.

In most training programmes, as soon as it is about technology, there will be way more men than women. Creating Chicas Poderosas, which has “women” (chicas) in its name, automatically attracted more women. We’ve created a space where they feel comfortable, where it’s ok to fail, where they are not being judged, and where they can share their doubts, questions and insecurities. A space where they can grow together and be better together.

 

 

One thing you are focusing on is the situation of women in politics and how to use data journalism to improve gender inequality in Central American governments. As you are mainly working in Brazil, can you give us an insight on what the situation is like for women over there?

 

From what I know, and what I’ve been feeling, the political situation is very poor. Right now you have extremely corrupted candidates who run for president. Even the former president tries to get into office again, even though he is about to be put in jail.

The situation for women is worse. They have quotas for the number of women to represent congress, parliament, and the government. But in most cases, they are actually either daughters, wifes, sisters or cousins of male presidents or males who are somehow connected to the political scheme. Therefore, they do whatever they are told to do.

That’s not what we need! With Chicas Poderosas we start a discussion in politics, asking questions such as “what does it take for women to be taken seriously in politics?”

 

 

What are you looking for when voting for projects from the Data Journalism Awards competition? 

 

Data journalism for me is the core of journalism and that is what I began with. What I look for are proposals that are varied, not laid back safe, not copying the great examples that are already there, projects that come up with something new. Engage me in the story! That’s the main thing.

Regardless of the story, I want to see variety. See out of the box, go out of your comfort zone and show me what you can do!

 

Besides teaching digital and new media skills, Chicas Poderosas offers leadership training. Why is it especially important for women?

 

In Latin America I see that women have a tendency to block themselves, to not believe that they can do the things they want to do, and therefore, there is a lack of women leaders. Why does a woman has to become more man-like in order to be seen as a leader? This is something we really want to change!

Women have so many skills and qualities that they often don’t use. This is really sad because they have characteristics that are really useful and needed for leadership. 

 

What are your main pieces of advice for women in media leadership positions? 

 

I’ve been meeting amazing women leaders in the media and we are trying to bring them to the New Ventures Lab that we have started a few weeks ago in St. Paulo, Brazil. We need more women role models in Latin America! We bring them to share their stories and insecurities, and we are trying to teach them to be very goal-driven.

As an entrepreneur you need to not only want but also to be able to do it. To run the extra mile and give a little bit more. Because you may have a full-time job, or a family to take care of. 

In order to strive within new media you have to think outside of the box, because journalism has changed – print is dying, digital is here to stay. We cannot think the same way as we did in print days. The same thing goes for leadership. Whether you are a man or a woman, use all your strengths and your skills in your execution of leadership.

 

 

What upcoming projects do you have at Chicas Poderosas?

 

The next big goal is to finish our New Ventures Lab initiative on 25 May 2018, in St Paulo, Brazil, at Google’s offices. There, ten teams will have to reach their full potential, launch and run their own businesses.

What we want to do is to have a very solid structure in terms of investigative media training. This is our main core and it will always be.

We created a network that gathers people from 11 countries in Latin America, and supports women. 

 

Finally, what are your favourite programmes to create graphics with?

 

I love making illustrations with Illustrator and animating everything in After Effects. Sometimes I like using stop-motion as well but that’s a little bit more crafty and handmade: you draw, you keep the paper, you lay it on any surface you have and take a picture of every movement you want to create. That makes a really crafty animation. It’s very time consuming but, when you don’t give me any time limitation, that’s what I love doing the most.

 



Michaela Gruber is a journalism and media management student, based in Vienna, Austria. During her studies she spent a semester abroad in France, where she started working for HEI-DA.

As the company’s communication officer, she is in charge of the Data Journalism Blog and several social media activities. This year, Michaela will also be HEI-DA’s editor covering the Data Journalism Awards in Lisbon, Portugal.

The future of news is not what you think and no, you might not be getting ready for it the right way

This article was originally published on the Data Journalism Awards Medium Publication managed by the Global Editors Network. You can find the original version right here.

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Editors, reporters and, anyone in news today: how prepared are you for what is coming? Really. There is a lot of talk right now on new practices and new technologies that may or may not shape the future of journalism but are we all really properly getting ready? Esra Dogramaci, member of the Data Journalism Awards 2017 jury and now working as Senior Editor on Digital Initiatives at DW in Berlin, Germany, thinks we are not. The Data Journalism Awards 2017 submission deadline is on 10 April.

 

Esra Dogramaci, Senior Editor on Digital Initiatives at DW, Photo: Krisztian Juhasz

 

Before joining DW, Esra Dogramaci worked at the BBC in London and Al Jazeera English, amongst others. She discusses here the preconceived ideas people have about the future of journalism and how we might be getting it all wrong. She also shares some good tips on how to better prepare for the journalism practices of the future as well as share with us her vision of how the world of news could learn from the realm of television entertainment.

 

What do you think most people get wrong when describing the future of journalism?

 

There are plenty of people happy to ruminate on the future of journalism — some highly qualified such as the Reuters Institute and the Tow Center who make annual predictions and reports based on data and patterns while others go with much less than that. Inevitably, people get giddy about technology — what can we do with virtual reality (VR), augmented reality (AR), artificial intelligence (AI), personalisation (not being talked about so much anymore), chatbots, the future of mobile and so on. However with all this looking forward to where journalism is headed (or rather how technology is evolving and, how can journalism keep pace with it), are we actually setting ourselves and journalism students up with all that is needed for this digital future? I think the answer is no.

 

What is, according to you, a more adequate description (or prediction) of the future of news?

 

If we’re talking about a digital future, the journalists of tomorrow are not equipped with the digital currency they will need.

Technology definitely matters but it’s not so useful when you don’t have people who understand it or can build and implement appropriate strategy to bridge journalism in a digital age. Middle or senior management types for instance, are less likely to know how to approach Snapchat, which they would be less likely to use, than a high school teenager who is using it as a social sharing tool or their primary source of news.

So if we aren’t actually:

1. Listening to our audience and knowing who they are and how they use these technologies, and

2. Bringing in people who know how to use these tools that speak to and with the audience,

…the efforts are going to be laughable at worst and dismissed at best.

In essence, technology and those who know how to use, develop and iterate it go together. That’s the future of news. We should be looking forward with technology, but we’ve also got to look back at the people coming through the system that will inherit and step into the – hopefully relevant – foundations we’re building now.

 

“Are we actually setting ourselves and journalism students up with all that is needed for this digital future?”

 

When looking at the evolution of journalism practices over the past few years, which ones fascinate you the most?

 

There are two things that stand out. The first is analytics and the second is the devolution of power, both points are interrelated.

Data analytics have really transformed non-linear journalism. Its instantly measurable, helping people make editorial decisions but also question and understand why content you thought would perform doesn’t. Data allows us to really understand our audience, and come up with content that not just resonates with them but how to package content that they will engage with. For instance a website audience is not going to be the same as your TV audience (TV is typically older and watches longer content but again the data will tell specifics), so clipping a TV package and sticking it on Facebook or YouTube isn’t optimal and suggests to your audience that you don’t understand these platforms and more importantly, them. They will go to another news provider that does.

An example of this was a project where it was traditionally assumed [in one of my previous teams] that the audience was very interested in Palestinian-Israeli conflict and so a lot of stories were delivered about it. However, we discovered through the numbers, on a consistent basis, that the audience wasn’t as interested as assumed, rather people were more into the conflicts in Syria, Yemen as well as Morocco and Algeria stories. These stories and audiences may not have traditionally registered on top of the editorial agenda because of what was historically thought to be in the audiences interest, but our data was suggesting we needed to pay more attention to the coverage in these areas.

Now, that being said, it’s still stunning to see how little analytics are used day to day. There still seems to be a monopoly on the numbers rather than integration into newsrooms. There are a plethora of tools available in making informed editorial or data decisions but generally editors don’t understand them or follow metrics that are not useful because they don’t know how to interrogate the data, or we hear things like ‘I’m an editor, I’ve been doing this for x years, I know better.’

Fortunately though, about 80–90% of editors I find are keen to understand this data-driven decision-making world and once you sit down and explain things, they become great advocates. Ian Katz at BBC Newsnight, Carey Clark at BBC HardTalk are two editors who embody this.

The second area is devolving power. The best performing digital teams are when not all decision-making is consolidated at the top, and you really give people time and space to figure out problems, test new ideas without the pressure always to publish. That’s a very different model to traditional hierarchical or vertical journalism structures. Its an area of change and letting go of power. But empowering the team empowers leaders as well.

An example of this is a team I worked with where all decisions and initiatives went through a social media editor. As a result, there was a bottleneck, and frustration for things not being done and generally being late to the mark on delivering stories and being relevant on platform as competitors were overtaking. What we did is decentralise control — we asked the team what platforms they’d like to take responsibility for (in addition to day to day tasks) and together came up with objectives and a proposition to deliver on those. The result? Significant growth across the board, increase in engagement but perhaps most importantly, a happier team. That’s what most people are looking for: recognition, responsibility, autonomy. If you can keep your team happy, they are going to be motivated and the results will follow.

 

Global Headaches: the 10 biggest issues facing Donald Trump, by CNN

 

 

Do you have any stories in mind that represent best what you think the future of newsmaking will look like?

 

CNN digital did this great Global Headaches project ahead of the US elections last year.

The project was on site (meaning that traffic was coming to the site and not a third party platform), made for mobile which would presumably reflect an audience coming mainly from mobile, used broadcast journalists and personalities as well as regular newsgathering, with an element of gamification. Each scenario had an onward journey which then takes your reader out of the game element and into the story.

 

Example from the “onward journey” with the CNN “Global Headaches” project

 

This isn’t a crazy high tech innovation but it is something that would have been much harder to pull off say 5 years ago. This example is multifaceted and making use of the tools we have available today in a smart way. It demonstrates that CNN can speak to the way their audience is consuming content while fulfilling its journalistic remit.

Examples like this doesn’t mean we should be abandoning long form text for instance and going purely for video driven or interactive stories. The Reuters Institute found last year (in their report The Future of Online News Video) that there is oversaturation of video in publishing and that text is still relevant. So, I would caution against throwing the text baby out with the bathwater, which then comes down to two things:

  1. Know your audience and do so by bringing analytics into the newsroom (it’s still slightly mind boggling the number of newsrooms who do not have any analytics in the editorial process)
  2. Come up with a product that you love and that works. The best of these innovations are multidisciplinary and do something simple using the relevant tools we have, that are accessible today. There’s no use investing in a VR project if the majority of your audiences lack the headsets to experience it.

 

Do you think news organisations are well equipped for this digital future?

 

Yes and no. There are the speedboats like Quartz, AJ+, NowThis, Vox, who can pivot quickly and innovate versus the bigger media tankers that turn very slowly. One question I get asked quite a bit is “what’s the most important element in digital change”. The answer is leadership. There needs to be someone(s) who understands, supports and pushes change, otherwise everyone down the ranks will continue to struggle and face resistance.

I truly believe in looking at the people who are on the ground, rolling up their sleeves and getting the work done, trying, failing, succeeding, and who keep persevering — versus always deferring to editors who have been in place for say 10 years to lead the way. Those people in the trenches are the ones we should be shining the light on and listening to. They are much closer to the audience and can give you usable insights that also go beyond numbers.

If I could name a few, people like Carol Olona, Maryam Ghanbarzadeh at the BBC, Alaa Batayneh or Fatma Naib, at Al Jazeera, Jacqui Maher at Conde Nast, need to be paid attention to. You may not see them at conferences or showcased much but by having people like them in place, news organisations are well equipped for a digital future.

 

Do you see some places in the world (some specific organisations maybe?) that are actually doing better than others on that front?

 

The World Economic Forum wouldn’t traditionally be associated as being a digital media organisation, but a few years ago they started to invest in social media and develop an audience that normally would not be interested in them. They take data and make it relevant and accessible for low cost, bite size social consumption.

Take this recent video for example:

 

Your brain without exercise, a video by the World Economic Forum
And also this related one:

 

Best of 2016 social video by the World Economic Forum

 

There is also this NYT video of Simone Biles made ahead of the 2016 summer Olympics which then has the option of taking you to an onward site journey.

The Financial Times hasn’t been afraid of digital either. You see them taking interesting risks which might go over a lot of people’s heads but the point is they’re trying. Like in their project “Build your own Kraft Heinz takeover”.

 

 

Then there are the regular suspects — AJ+ isn’t trying to do everything, they’re trying to be relevant for a defined audience on the platforms that audience uses. Similarly, Channel 4 News isn’t pumping out every story they do on social, but deliberately going for emotionally charged stories rather than straight reporting as well as some play with visualising data.

 

What would you like to see more of in newsrooms today which would actually prepare staff better for what’s coming?

 

When you’re hiring new staff, assign them digital functions and projects rather than putting them on the traditional newsroom treadmill. A lot of organisations have entry level schemes and this could easily be incorporated into that model. That demonstrates that digital is a priority from the outset. You could also create in house lightning attachments, say a six-week rotation at the end of which you’re expected to deliver something ready for publishing, driven by digital. My City University students were able to come up with a data visualization in less than an hour, and put together a social video made on mobile in 45 minutes (social or mobile video wasn’t even on the course but I snuck it in). Six weeks in a newsroom is plenty of time for something substantial.

Also, have the right tools in place and ensure that everyone is educated on the numbers. Reach and views for instance get thrown around a lot- they are big easy numbers to capture and comprehend, but we need to make a distinction between what is good for PR versus actionable metrics in the newsroom. As more people clue into what matters, I do think (and we see in certain places like Newswhip for instance) where success is based on engagement, interactions and watchtime rather than views, impressions or reach.

Finally and obviously, its devolution of power and more risk taking. Make people better by empowering them — that means carve out the time and space to experiment without the pressure to deliver or publish. When you are continually driving staff against deadlines, creativity suffers. Fortunately there are so many third party tools and analytics that will very quickly tell you what’s working and what’s not, contributing to a much more efficient newsroom freeing up valuable time to think and experiment. Building multi disciplinary teams is a good step in this direction. DW is experimenting with a “lab like” concept bringing together editorial, technical and digital folks in an effort to bring the best of all worlds together and see what magic they come up with.

 

From your experience teaching social and digital journalism at City University London, what can you say about the way the younger generation of journalists is being trained for the future? Do they realise what’s at stake?

 

At the beginning of term, I heard quite a few students say that digital didn’t matter, it wasn’t “real journalism” and that they were taking the class merely because it was perceived as an “easy pass”. That’s because the overall coursework, emphasized magazine and newspaper journalism. At the end of the term, and almost on a weekly basis since, my former students write to me about either digital projects they have done, digital jobs they are going for or how something we went over in the class has led to another opportunity.

There remains a major emphasis on traditional broadcast journalism — TV, radio, print, but very little for digital. That’s not something to fault students on. Digital is changing constantly but teaching staff mainly reflect the expertise of the industry, and that expertise is traditional. While there are a lot of digital professionals, it does not come close to the level of expertise and experience currently on offer at institutions training the next journalist generation. That being said organisations like Axel Springer have journalism academies where all of their instructors, are working full time in media and can translate the day to day relevance into the classroom. That’s more of the kind of thing we need to have.

The students I think do realise what’s at stake because a lot of those journalism jobs they’re applying for all require some level of digital literacy. Sure everyone might watch a YouTube video but what happens when an Editor asks you why a news video has been uploaded and monetised by other users elsewhere. Would you know what to do?

 

What could be done to improve the educational system in the UK and beyond? Simply make journalism courses more digitally focussed?

 

There is nothing that will compel places to change but reputation. If students are leaving institutions because what they are learning is not preparing them to meet the demands of the industry they’re choosing to go into, word will spread sooner than later. There will surely be visionary institutions who ‘get it’ and adapt, some are there already.

‘Smart’ places will build in digital basics so students can have the confidence to hit the ground running. I see this in a lot of digital job requirements. It’s a given that anyone starting in journalism in 2017 has basic social media literacy. Beyond that everything is a bonus — how can you file from a mobile phone, can you interpret complex data and tell a story with it. Then, are you paying attention to analytics?

As Chris Moran (Guardian) had pointed out:

 

“staff blame the stupid internet for low page views on a piece…but credit the quality of the journalism when one hits the jackpot.”

We need a much more sophisticated understanding beyond yes/no answers to points like these.

A lot of media houses have academies or training centres expected also to bridge digital gaps. The caution there is that the trainings they offer when it comes to things beyond CMS, uploading video, etc., is that other digital knowledge seem to fall in the “nice to know” rather than “you need this” category. The best thing is to find the in-house talents who know what they’re talking about and get them to lead the way.

 

Another recurrent question when talking about our digital future is the question of business models for news organisations. As the latter are under continual financial strain, you actually think we should get inspiration from the entertainment industry. Can you elaborate on this idea?

 

Yes. The entertainment industry always has a much larger creative capacity and funding so they are able to take more risks with less at stake. That’s where we should be looking and seeing what the obvious news applications could be rather than trying to build our own innovations all the time. Most news houses just cannot compete with entertainment budgets. Jimmy Fallon showcased Google Tilt brush in January 2016:

 

 

https://www.youtube.com/watch?time_continue=2&v=Dzy7ydbEyIk

 

 

I then saw it in November 2016 at a Google News event but have yet to see anyone use it in a meaningful news application. It doesn’t necessarily mean that all these things will be picked up on, but it does mean we should keep a finger on the pulse of what’s possible. Matt Danzico, now setting up a Digital News Studio at NBC is in a unique position. He’s in the same building as Late Night, SNL, and others. That means he has access to all the funky things entertainment is coming up with and can think about news applications for it.

Similarly, how can news organisations think about teaming up with Amazon or Netflix for instance and start to make their content more accessible? These media giants have the capacity to push creative boundaries and invest, and news organisations have their journalistic expertise to offer in that relationship. That’s very relevant in this time of “fake news”.

 

You have recently been appointed Senior Editor of Digital at DW in Berlin. Can you tell us more about what this position entails and the type of projects you’ll be doing? How different is it from what you’ve done in the past at the BBC and Al Jazeera for example?

 

DW is in a position familiar to many broadcasters, and that is a slight shift away from linear broadcasting to a considerable foray into digital. The difference is that DW is not starting from zero, with plenty of good (and bad) examples around to learn from. The first thing is to set a good digital foundation — getting the right tools in house and bringing people along on the digital journey — in a nutshell increasing literacy and comfort with digital. Once that is done I think you’ll see a very sharp learning curve and a lot more ambitious digital projects and initiatives coming from DW.

We’re very lucky that we have a new Editor in Chief, Ines Pohl and new head of news, Richard Walker, both infused with ideas and energy of making a great digital leap. Complementary to that we have a new digital strategy coming from the DG’s office which I’ve been involved with in addition to a new DW “lab like” concept, as I mentioned before. A lot of people might not know how big DW is — there are 30 language services and English is the largest of those, so getting all systems firing digitally is no small task.

Compared to BBC or AJ, the scope and scale of the task is of course much bigger. At AJ we had a lot of free range in the beginning because no one was doing what we did, at the BBC, there was much more process involved, less risk taking. Based on those experiences, DW is somewhere in the middle, a good balance. 2017 could be the year where stars align for DW. There are approximately 12 parliamentary or national elections in Europe and DW knows this landscape well. So bringing together the news opportunities, a willingness to evolve and invest in something new along with leadership that can really drive it, I think DW will be turning heads soon.

 


marianne-bouchart

Marianne Bouchart is the founder and director of HEI-DA, a nonprofit organisation promoting news innovation, the future of data journalism and open data. She runs data journalism programmes in various regions around the world as well as HEI-DA’s Sensor Journalism Toolkit project and manages the Data Journalism Awards competition.

Before launching HEI-DA, Marianne spent 10 years in London where she worked as a web producer, data journalism and graphics editor for Bloomberg News, amongst others. She created the Data Journalism Blog in 2011 and gives lectures at journalism schools, in the UK and in France.