Data journalism in the post-truth world: AMA with First Draft

Conversations with Data: #21

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Almost three years on from the declaration of ‘Post-Truth’ as the 2016 Word of the Year, it looks like its close cousins, mis- and dis-information, are here to stay.

And, unfortunately for data journalists, it’s easy for our arsenal of numbers and statistics to aid various untruths. So, what’s a reporter to do?

In this edition of Conversations with Data, we gave you the opportunity to ask the team at First Draft. With a firm focus on tackling information disorder, First Draft is an international nonprofit that uses fieldwork, research, and education, to promote trust and truth in this digital age.


Image: From First Draft’s report, Information Disorder: An interdisciplinary framework.

What you asked

To make sure we’re all on the same page, let’s start with a question on the various terminologies used in the information disorder space:

What are the differences between fake news, disinformation, and misinformation?

'At First Draft, we don’t use the phrase fake news whenever possible. This is because it’s actually a terrible phrase for understanding this space -- much of the most problematic content isn’t fake, it’s genuine but used out of context. Similarly, a lot of it could never be described as news -- it’s memes, social posts, and fabricated videos. More importantly the term has been weaponised by politicians around the world to describe journalism they don’t like. As a result, research shows that when audiences are asked about fake news, they think it describes the professional media. So when journalists use the phrase, it’s harming journalism itself.

We prefer misinformation and disinformation. Misinformation is false or misleading information that is shared by people who don’t realise it’s not false and they mean no harm.

Disinformation is false or misleading information that is shared by people who know that it’s false or misleading and hope to cause harm by creating or spreading it.'


Image: Types of Information Disorder. Credit: Claire Wardle and Hossein Derakshan, 2017 (CC BY-NC-ND 3.0).

Because audiences tend to inherently trust numbers, do you see data journalists as playing a distinct role in the fight against mis- and dis-information?

“The biggest challenge in this space is that audiences gravitate to information that supports their existing world view. There is some good research by Dan Kahan at Yale that showed highly intelligent people doing mathematical somersaults with data in order to make it support their pre-existing views on gun control. But it’s true that in order to convince people about the importance of evidence in society, data journalists play an important role. We need deeper investigations, both traditional investigative journalism as well as data journalism to build trust with audiences.”

What solutions are realistic for society to become better at understanding statistics?

“We need stronger education to teach people the skills they need to be able to read data and statistics. This should be in schools as well as part of life-long learning initiatives. Many journalism schools still don’t teach this enough, which means too many journalists can’t critically assess data, let alone their audiences. It’s definitely a required skill that too few people have.”

What is the most successful data driven initiative you’ve seen to date?

“We focus on misinformation, so we’ve been very impressed by journalists like Craig Silverman or Julia Angwin who have used publicly available data to put pressure on the technology companies. For example, Craig looked at the most shared viral misinformation stories on Facebook. Julia found that advertisers could target ads on Facebook to people based on their religion or ethnicity in ways that were highly problematic.”

If you could choose one thing for all data journalists to implement in their work, in order to curb mis- and dis-information, what would it be?

“We need more data journalists to investigate potential algorithmic bias. We can only see how untended bias plays out when people search different queries and compare results. Most bias is not intentional but with the power algorithms have on almost all aspects of our life now, we need to continuous research and oversight from independent journalists and researchers.”

You can get started in algorithmic accountability reporting with our how-to chapters in the Data Journalism Handbook 2, or expand your verification skills with Craig Silverman’s LEARNO course.

Our next conversation

While most journalists produce stories by exploring databases, a new form of journalism is emerging where databases are the end product. Remember ProPublica’s Dollars for Docs project (or its German equivalent, Euros for Doctors by Correctiv)? These types of journalistic databases allow readers to explore deeper into the aspects of an issue that immediately interest them. In our next edition, we need your help to share other great examples of searchable databases as end-products.

As always, don’t forget to let us know what who you’d like us to feature in our future editions.

Until next time,

Madolyn from the EJC Data team

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