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- Our press secretary gave alternative facts to that.
- My goal in this deposition was to be truthful, but not particularly helpful.
- Welcome to Unspun, the podcast that makes you better at finding the truth.
- The way people get news is changing.
- It used to be that there were many reporters who would research stories and write articles,
- but now politicians and famous people share information directly with you on social media and the internet.
- That means you find out things fast, but it's up to you to make sure the information is actually accurate.
- And newsmakers don't always do their part.
- The temptation to manipulate information is strong.
- They bend the truth to deceive so that they can avoid accountability, so that they can advance their agendas.
- When you recognize these agendas, you can sometimes find out what's real.
- And we're at a crossroads where anyone can share anything online,
- so it's important to sharpen your critical thinking skills.
- Finding that deception before it goes viral is pretty much a survival skill now,
- and we're going to do it together.
- Let's get Unspun.
- Hello, everyone, and welcome to this week's Unspun.
- In this episode, you'll learn to recognize some ways that newsmakers use distraction to shape the news you see.
- Then when interviews and press conferences are not enough,
- journalists can use databases to find questions and answers that help hold the powerful to account.
- So a lot of data journalist Ben Welsh will join me for the interview.
- Sound good? Let's get Unspun.
- Imagine this. Your child has maybe been taking it easy and they're not doing so well in school.
- Education is important to you, and you know he's pretty capable,
- so you expect pretty good marks on the next report card.
- But your child knows that the next one you see is going to be really disappointing.
- For several days before that report arrives home,
- he pays extra attention to his contributions in keeping the home clean.
- He cleans his room maybe, he remembers to take his shoes off at the door.
- Somehow his dishes manage to make it from the table into the dishwasher without you even having to remind him.
- The day comes and you open up that mail from the school.
- You look up from that disappointing lineup and you start to say something,
- and he interrupts to say, "Hey, have you seen my room? It's very clean."
- Your child is hoping you will fall for distraction.
- Distraction tactics are powerful.
- They can change what people think and they hide important problems.
- By understanding these tactics, we can see the difference between real talk and lies.
- Distraction works by taking advantage of how our brains work.
- It's hard to focus on many things at once.
- When something new or exciting pops up, we pay attention to that instead.
- And this lets people hide things they don't want us to pay attention to.
- The news and social media make this worse.
- News needs to be new and exciting to get our attention,
- and that means big flashy stories, even if they're not important, they get shown more.
- And social media doesn't help, because it learns what we like and it shows us more of that.
- Which means we might only see things we already agree with, even if they're not true.
- This is bad for everyone.
- When we don't talk about real problems, they don't get fixed.
- Distraction stops us from making good choices because we don't have all the facts.
- We need to be careful and look for what's hidden when things feel like they're too simple,
- or even just too exciting.
- So let's unpack this in the political world.
- We'll listen to a few examples for this week's warm-up.
- Have a listen and see if you can identify the distraction.
- Around the globe, the president realized that our current approach to testing
- was inadequate to meet the needs of the American public.
- He asked for an entire overhaul of the testing approach.
- He immediately called the private sector laboratories to the White House, as noted,
- and charged them with developing a high-throughput quality platform
- that can meet the needs of the American public.
- We are grateful to LabCorp and Quest for taking up the charge immediately after the meeting,
- and within 72 hours bringing additional testing access,
- particularly to the outbreak areas of Washington State and California and now across the country.
- Ah, 2020, the early days of COVID.
- Maybe you remember the uncertainty, the fear,
- if you live in the United States, the daily press conferences.
- This clip beautifully illustrates a classic logical fallacy, the fallacy of distraction.
- So here's how it works.
- Instead of addressing the actual issue at hand,
- in this case, a lack of COVID testing, a shiny, irrelevant fact shows up.
- Hey, companies are helping us.
- All the things that are being described here are plans that have just started,
- but the situation was not good at the time.
- In fact, this is the same speech in which the president declared a national health emergency.
- Now, imagine this playing out in the whirlwind of the 24/7 news cycle.
- Headlines are changing at lightning speed and there's a constant influx of information,
- and it makes it difficult to focus on any one thing for too long.
- This environment is fertile ground for the distraction fallacy to thrive.
- Before we even have time to process the lack of testing,
- we're already swept away by the promise of a great new website from Google.
- And then enter social media algorithms designed to feed us more of what we already engage with,
- you know, creating those echo chambers and those filter bubbles.
- So when a politician or an organization successfully deploys a distraction
- that aligns with our existing biases, the algorithm amplifies it.
- But, you know, this strategy, this well-timed distraction is nothing new.
- So here's another example.
- Picture this, it's 1998, the Bill Clinton era,
- and the House is getting ready to vote on impeaching the president.
- A member of the House of Representatives gets up and says this.
- Where do we go from here?
- We will go to the floor of the House of Representatives to debate this issue.
- It is going to be a nasty debate.
- It will be a debate about sex.
- It will be a debate about the private affair of the president.
- It will be a debate about the extramarital affair.
- It will be a debate that the American public may not necessarily want to hear.
- But they have been thrust into this debate by members of this House
- and the Republican Party who are intent and who will stop at nothing
- to bring down the president of the United States of America.
- So, you know, at this time, the Monica Lewinsky scandal is exploding.
- And impeachment hearings are on everybody's mind.
- The actual grounds for the impeachment were perjury.
- Clinton, when he was being deposed for a sexual harassment lawsuit,
- denied having sex with Monica Lewinsky.
- And later, evidence emerged proving that he lied
- and this led to those charges of perjury, you know, that lying under oath.
- But when Representative Waters says what she says,
- she's implying that the Republicans are trying to impeach Clinton
- for a consensual affair rather than for the perjury itself.
- And this framing, whether it's accurate or not,
- shifted the focus away from the legal charge of perjury
- and toward a debate about the appropriateness of the relationship.
- Distraction achieved.
- As a little side note, this also kind of showed up
- when President Trump was being tried in New York City
- and Stormy Daniels was on the stand.
- You heard some people commenting saying that people just wanted the,
- you know, salacious details of the encounter
- between Stormy Daniels and the president.
- There was a second distraction tactic that was related to Clinton.
- Here's Senator Dianne Feinstein.
- Well, I've read most of the press from other countries.
- And as you know, it's universal that no one can understand what is happening
- and that most of the leaders of these other countries
- really believe that we're tearing at the fabric of the presidency
- at a time when the United States is really important in the world
- in terms of peace and stability.
- The world is a more uncertain place now than it has been.
- The world is a more uncertain place now than it has been in the last ten years.
- And having a strong American president out there is important.
- And all of this goes to diminish that.
- So I'm one that believes it should be brought to conclusion as quickly as possible.
- And then let's get on with it.
- Did you hear the distraction here?
- The rest of the world will think less of the United States.
- So if you're debating this, what are you not debating?
- Whether or not the president committed perjury.
- So what's the takeaway?
- Well, we live in a world of carefully curated narratives
- where bad news is eclipsed by shiny distractions
- and it's amplified by the actual technology we use to consume the information.
- So whether it's a press conference or a news feed
- or even just a casual conversation, remember,
- you can always look below the surface.
- And one thing that helps journalists look below the surface
- is to get the data behind the story so that they can evaluate it themselves.
- I need to take a quick break, but when I come back,
- Ben Welsh from Reuters and I talk about the world of data journalism.
- I'll be right back.
- While we're on a break, I wanted to remind you that I have a lot of material
- like what we have in warm up in my book, Detecting Deception,
- Tools to Fight Fake News.
- I break down more than 20 ways that newsmakers use facts and language
- to be deceptive, and for each, I give you real world examples to explain
- and then some historical examples that let you put yourself
- in the role of a journalist trying to report on these newsmakers.
- There's even answers in the back.
- It's an inexpensive, helpful little book that you might enjoy
- but in between unspun episodes.
- You can find out more at bit.ly/detectingdeceptionbook, all one word.
- That's bit.ly/detectingdeceptionbook, all one word.
- And now just a quick word from my sponsors
- and then my uninterrupted interview with Ben Welsh.
- Welcome back.
- All right, so I'm very excited to have you with us this week on the podcast.
- And one of the things I first wanted to ask you is it seems like your job
- is really different from the job you might like see a journalist on TV doing,
- right, you know, like in a movie or something.
- So it's different from sitting in a press briefing
- and asking questions or talking to someone on the phone.
- So first of all, can you just explain what data reporting is?
- What is data reporting? Great question.
- I think it actually is actually more than one answer.
- And the way to summarize it, like a lot of things in life, is it depends.
- It kind of depends what you do with it.
- I mean, the most famous example of a data journalist people might encounter
- on cable news would be John King or Steve Kranacki
- or these folks that stand in front of the magic maps on election night
- and help to decode what the data is telling us about how voters,
- you know, how voters are behaving.
- But data journalism is a lot more than that, too.
- It's it really is, you know, reporters and editors
- who treat structured information, spreadsheets, maps,
- big piles of documents as sources in the same way
- that we've traditionally all treated people.
- We can talk to it on the telephone or shoot questions out of the press
- conference as sources, you know, structured information.
- Data is is a source that in the same ways.
- And we're we're using as data journalists
- just kind of different tools to ask questions and try to get answers
- out of, you know, the data that surrounds us all every day.
- Yeah. So that sounds like it would have a kind of different set of challenges
- from doing live interviews.
- Would you be willing to give maybe an example of a story
- that you've worked on that you're particularly proud of? Hmm.
- Well, it's always interesting which one pops into your head.
- Well, as a few years ago, I was working as a reporter at the Los Angeles Times
- and a thing happened in the news.
- A candidate for mayor made an allegation.
- He said that, hey, the fire department here in our city is in big trouble
- and you better elect me to fix it.
- Now, obviously, that's a bit of a caricature on my part,
- but he said something along those lines.
- He actually said it in more vivid terms, made some pretty strong claims
- about what was happening in our local 911 system.
- And that kind of raised a question in our public sphere in Los Angeles about,
- hey, is the fire department doing a bad job?
- What's going on over there? Right.
- And that's a question that, you know, with traditional techniques,
- you can answer by just interviewing people,
- you know, going to a fire station and riding along and seeing what's going on.
- And all that is supremely valuable.
- But it's also a question that there's a measurable answer to.
- In some cases, you know, it's claims were and as a data journalist,
- that's often what we're looking to do.
- We have we have questions for the world.
- But can we can we measure the answer to help us come up with something
- that's a little maybe more stronger or broader or more contextualized
- than what you could do without it?
- And so in that case, the claim really was that the response times,
- how long it was taking for the fire department to arrive when you call 911
- was getting slower was the claim of the candidate.
- And hey, that's measurable.
- And so I set out to file a Freedom of Information Act request
- seeking not public documents, not emails or memos, but a database.
- There's a database at the core of the 911 system.
- I learned that by interviewing people as a traditional reporter would.
- I filed the request is a longer story, but, you know, I eventually received
- a CD in the mail that had the government database of every 911 call
- for a long period of time.
- And then I set out to to write the computer code necessary
- to sort of get my hands on and interview that database
- to ask you questions like, hey, how long does it take, say, on average
- for a 911 response to arrive?
- Hey, is it faster in one part of town versus another?
- Hey, is it getting slower?
- Is it getting faster over time? Right.
- And and I also sought out outside standards for what might be good or bad.
- Hey, the fire department has said their goal is to respond in X amount of time.
- Are they doing that? Are they not, et cetera, et cetera?
- And so those are all kind of questions that were measurable.
- And then you write the code that kind of spits out the answer,
- you know, not unlike how a social scientist or someone who works
- at a university might for an academic study, just maybe in a simpler way.
- And then the answers that we got back from those provided the foundation
- for a whole series of stories we did about problems in the 911 system,
- which led to some pretty significant reforms and how it was managed,
- including changes to how calls were handled, the replacement of the fire chief.
- Ultimately, and a number of changes.
- And one fun fact is all the things that we discover and wrote about
- were not what the candidate was complaining about.
- His complaints were actually inaccurate.
- But by getting the data and figuring it out ourselves with, you know,
- with our technical skills and a more scientific approach,
- we actually got a lot closer to the truth. Right.
- Fair enough.
- So one thing that makes me think of is that I know that in journalism,
- a lot of reporters have sort of standard coverage areas that they work in.
- Right. So that they get to know the sources and they get expertise in that area.
- You know, we might call that like beat reporting as a data reporter.
- Do you have particular areas of specialization
- or is it more that you kind of go out and work with other people?
- Some do, some don't.
- You know, I think it really, again, come back to my unsatisfying answer.
- It depends.
- And so there are data journalists who have areas of really strong expertise.
- I think elections is an example of that and political science and polling.
- There's a lot of great data journalists who that's pretty much all they do.
- And they do it very, very well. Right.
- And then there's other data journalists who are maybe a little more
- technical specialists or sort of, you know,
- social scientists for hire in the newsroom, you know,
- who will range and partner with reporters who have expertise
- in this topic or that topic. Right.
- And juggle a lot of different things over time.
- I think in the career of a data journalist, you can end up in either of those situations.
- And it often depends on the personality of the person.
- But I think either model can work really very well.
- You do end up developing as this is true of all reporters,
- is you do end up kind of developing expertise in the areas you cover over time.
- And that isn't always planned out. It just kind of happens.
- OK, it sounds like this kind of projects take a lot of time, too.
- Would that be fair to say?
- They can. Again, it depends.
- You know, I think traditionally data analysis and journalism was developed
- a lot earlier than I think people realize there.
- I mean, there were data stories that were quite scientific in the 19th century,
- including one you can read about, about how a young congressman named
- Abraham Lincoln was submitting the largest expense reports in Congress
- at the time for travel.
- Those were, of course, all done with, you know,
- very traditional analog computational techniques,
- probably pen and paper. And but, you know, even computerized data analysis
- is older than people realize it in the 1960s and 70s.
- You already had a small group of data journalists doing very ambitious
- survey stories and other other works of analysis
- using punch cards and early computers.
- And those projects created kind of an early investigative tradition
- in data journalism, where a lot of the specialists were really focused
- on long term investigative pieces that took quite a bit of time to get done. Right.
- And so that was kind of the origin of a lot of the computational approaches
- to data in news. However, over time, as these tools have gotten easier to use,
- as data has become much more commonplace in our society,
- you see a lot of data work happening very, very quickly.
- And so you can see data stories that happen on a one or two day turnaround.
- You know, when there's an unexpected event where data can help cover it.
- And then there's also cases now where, you know, data has become a flowing
- river of news where every day when a new macro economic number comes across
- the wire from the Bureau of Labor Statistics that can feed into
- not just traditional stories that a data journalist would report and put out,
- but also maybe into software applications you're making to automatically publish new stories.
- So you're saying you actually do write software as a part of your job?
- I do not all data journalists do, but I definitely do.
- An example that I think almost all of your listeners will be familiar with.
- There's two. One is election night.
- So, you know, the most popular piece of journalism on election night now
- and pretty much since about 2008 have been the live updating election maps
- that give you the results as they come in.
- Almost all of those systems were invented and developed by people who started
- their careers as news reporters and editors and kind of drifted into software
- development or sought it out because it does take quite a bit of editorial
- kind of knowledge to be able to make something like that correctly.
- Another example that's more recent is a coronavirus tracking.
- So I'm sure almost everyone who's listening looked at, you know, a daily
- updating map or chart, at least with some frequency of the latest
- information on coronavirus where they live.
- Those were also cases where data journalists were kind of filling the void
- of where the government information wasn't satisfactory and developing
- software to kind of put news out automatically.
- Yeah. So you bring up, I think, an important thing here, which is
- it sounds like this kind of reporting is pretty resource intensive
- in terms of sort of the human resources, the time, the expertise.
- People have to build up those kind of things.
- So I guess what I'm getting at is what makes it worth it.
- So you've brought up kind of one thing, which is that you can kind of fill in
- gaps, right, telling stories that are not going to come out otherwise.
- Are there other things that kind of make the investment worth it?
- You're right. It is higher skill work.
- And we've seen this in both data and graphics journalism over the last two
- decades is that they've gone from jobs that were often about creating
- small accessories to go with stories to making these much more sophisticated
- feature stories, large scale investigations and these data applications.
- So it's definitely higher skill work.
- You hit on one of the major payoffs is like, yeah, it lets you do the big
- story you couldn't have got otherwise.
- Right. And that's been the reason this field really started to develop
- in the first place decades ago.
- But you have to remember is that once you automate something
- and put it on the Internet, you can have quite a bit of scale. Right.
- And so, for instance,
- when I was an editor at the Los Angeles Times, we created a system
- to automatically draft blog posts every time there was an earthquake.
- The government data would come across and we would have within seconds
- a post with a map that had the bare facts about the earthquake
- that was reviewed by a human editor, of course, but was ready to go out
- before everyone else for this, an event that would draw a lot of attention.
- Well, yeah, it did take us a few months to invent that application,
- finish it, vet it and prepare it.
- But once it was running, it was close to zero labor whatsoever. Right.
- And it then every time there was an earthquake
- yielded a piece of content that was oftentimes of very high value. Right.
- And so you have a large investment at the outset. Right.
- But once it's operational, it can have quite a bit of long running value
- and a really wide amount of scale.
- The third thing is is a lot of these data features are actually significantly
- sometimes by an order of magnitude or more
- more popular than a traditional text and photo story. Right.
- The coronavirus trackers, for instance, that were published by news
- organizations were in almost every case, the most popular piece of content
- ever published in the history of the news organizations that published them.
- That's these are like at the Los Angeles Times, our coronavirus tracker
- was the most read thing ever published by the L.A. Times in 130 years. Right.
- Of thousands of people spending how many work hours creating product.
- And so, yes, it is higher skill work.
- And yes, it does require more time than writing an individual story.
- But it also can be significantly more valuable.
- OK, so that makes a good business case, I guess, for my investment.
- This is why this is me to my boss saying, hey, let me hire some people.
- OK, that's fair enough.
- It does bring up another question, though, because I think about, you know,
- a larger resource publication or TV station like a CNN or L.A.
- Times versus maybe like your local news or your local newspaper.
- Do you think that the ability to produce these kinds of stories
- sort of creates an information divide between national and local?
- It definitely does, yes, because it requires this higher amount of skill
- to publish it. It can be hard for a small newsroom to put it together.
- So the attempts to solve that in the marketplace have been, of course,
- like everything else with local news, chain ownership, you know what I mean?
- And so the Gannett Company and these sort of large chains that own dozens
- and dozens, if not hundreds of local newsrooms have experimented
- with having sort of centralized teams that, you know, provide this type
- of work for the entire chain.
- You see that also in the television marketplace.
- So CBS News here in New York has a growing data team they're hiring
- for right now, and they'll perform data analysis and work for all
- of the owned and operated CBS stations across the country.
- So centralization is definitely one solution.
- Obviously, I should pump here a little bit for the wire services.
- I work at Reuters News, which is one of the world's largest agency
- and wire services, and so subscribers, you know, to our wire service,
- any outlet are able to access a lot of these graphics that we make automatically
- using some of these things I've talked about.
- And our live election results will publish this November
- with the national election pool are available to subscribers to our wire.
- So if you're if you're a member of Reuters News, you'll be able to take
- at a bare minimum the sort of finished maps and tables that we create
- and put them on your site or on your TV station.
- So those are two ways it works.
- There are some nonprofit academic alternatives to this.
- I spent a year at Stanford University recently working
- on a project called Big Local News, and the intention of this project is
- to create sort of a a similar model, a similar
- centralized kind of model for providing data services and skills
- and support to newsrooms in the nonprofit sector.
- So a lot of, you know, a lot of the small newsrooms that are coming up
- on the nonprofit side who don't have access to the things
- these these larger commercial chains do.
- OK, so I want to switch gears a little bit here
- and ask you about the particular data sets and things that you access.
- And can you talk a little bit about how you try to verify
- sort of the reliability of the data that you get access to?
- Sure. I mean, it can depend because there's there's different types of data sets,
- you know, and there's different ways they're kind of created.
- But to use the 911 data set as an example, we started with.
- So I have via a Freedom of Information Act quest,
- the official database from the primary source. Right.
- So in a way, that's the same as interviewing the buyer chief.
- There's no there's no intermediary who might introduce error. Right.
- Now, there are cases where you might get a data set from an intermediary
- and that could introduce some question about the kind of chain of custody.
- But in this case, it was what I would call a primary source.
- But it's, you know, 15 database tables that are largely undocumented.
- And not a lot of people at the agency are all that willing to explain to me
- how they fit together and how they work and what the caveats are of.
- Oh, gee, you need to filter and exclude all these and oh, gee,
- be careful when you merge these two things and all the crazy stuff
- that can happen with databases. Right.
- And what do these fields and columns even mean?
- They have these cryptic names.
- And so there often is in a circumstance like that in data journalism,
- a bit of forensic analysis you do to just sort of try to gradually
- test and deduce what the heck is in there.
- That can include interviewing people who have worked on the system,
- which I did in that case.
- We found an ex employee who had worked on the database
- and someone who had done data entry on the database.
- And we kind of were able to decode what was in there
- and understand better how to interpret it.
- Oftentimes, just finding the form that's used to create a database
- can really help you understand what's in there
- because, you know, that has a little more of a human sort of base to it.
- And then to me, there's sort of, I guess, lacking a better term,
- I would say kind of external validation or checking, checking, checking my work.
- And so in the case of the fire system, I had some questions
- I wanted to answer that were really specifically about our story
- and like, hey, are the response times meeting this national standard?
- Are are things getting faster, things getting slow?
- That's what I wanted to know at the end of the day.
- But those those didn't help me verify whether I was right or not. Right.
- And so what I did is I sought out
- data analysis that had been published by the department itself
- in its annual reports and presented at hearings before the city council.
- So in the past, the fire department had added up.
- We did this many total calls over the whole year.
- And these sort of general aggregate statistics have been published.
- And I sort of hypothesized or thought, hey, if I'm analyzing this database correctly,
- right, I should be able to hit those numbers, right?
- Using whatever sort of queries or formulas I'm going to use to analyze the data set.
- And that gave me some confidence that, hey, I'm able to interpret this data
- without making a lot of it, you know, without making a mistake in how I'm working with it.
- And then, you know, in a case like this, an investigative report,
- all of our findings were presented
- to the agency prior to being published.
- And so I'm a big fan of when publishing
- sort of claims that are that are going to that are going to get out on a limb
- a little bit or challenge a source or subject,
- is that they have the opportunity to review, comment, critique on the method
- and the numbers prior to publication, which I did in that.
- There's other stories I've been down that road on it to me.
- That's a really important step.
- And we did that.
- And but, you know, even at the end of the day, there sometimes can be a little
- you might not get a hundred percent answer
- from your source about whether or not you've done it right,
- because you may be breaking new ground in terms of, you know, your findings.
- And so a really important piece
- that I think is often overlooked in a lot of the data journalism today
- that's done very quickly or by people who don't have a journalism background
- is shoe leather ground truthing the data, which is basically saying, OK,
- so we did this analysis that was like these this this part of the call system
- is really slow and it's causing problems.
- Well, if that's right, I should be able to sort and list the calls
- that are the worst, the outliers that were that are going to be inside
- this kind of claim that we're making.
- And we should be able to call up the people on the phone or knock on the door
- of the people who are listed there and be able to verify that.
- Yeah, it was slow. Right.
- And so to me, it was taking the data out into the real world. Right.
- To verify that, hey, our assumption is that this is pointing to a thing
- that happened in the world. Did it? Right.
- And that requires getting, you know, unplugging your laptop, closing the lid,
- you know, getting out onto the street, knocking on doors, talking to people
- and really checking whether things you match up out there in the real world.
- And I think that that is an underrated and essential piece
- of doing quality data journalism.
- It also helps you find the stories and real people who are affected
- by the statistical stuff that you're writing about.
- And you end up with a much, much better piece of journalism.
- And you also can have an even greater degree of confidence
- that what you're writing about matters, whether or not it, quote, adds up.
- Yeah. So just to go back to a term that you used,
- you use the term chain of custody.
- You got it directly from the horse's mouth. Right.
- So there's cases where this happens frequently with document dumps
- and other types of data stories where you get the information
- from an intermediary, a leaker, right, or there's been a hack.
- And you may remember this from the coverage of the WikiLeaks documents
- and things about a decade ago.
- Questions were raised about whether the information had been tampered with.
- Right. And can we really believe that this email that says is from Hillary Clinton
- is really from Hillary Clinton? Right.
- And those are cases where you have to make extra steps
- to verify that what you have is is is authentic.
- But in the case where you file a Freedom of Information Act
- directly to a government agency and they give it to you directly,
- you don't have that intermediary.
- The human equivalent of this, and this is true in data journalism,
- almost anything we would talk about in this wonky, techie data way.
- There almost always is a shoe leather on the street,
- old school reporter version of is, hey, you went to the protest
- where things really got crazy and did you see the crazy thing that happened?
- Did you talk to the person who the crazy thing happened to
- or did you talk to the person who heard from the person
- the crazy thing happened to? Right.
- And it's the same thing.
- And this is true in all different aspects of journalism,
- is how close are you really to the to the real thing?
- Right. To what actually happened and this and in data that's no different.
- How do you balance all of those technical wonky things you just talked about
- with the need to tell a really interesting story
- maybe to a public who's often number phobic?
- I mean, connecting it to real people and real things that happened,
- I think is is essential.
- I think it is missing from a lot of data reporting.
- However, I think there's more appetite for data than I think is appreciated.
- You know, I think that properly balanced reported and presented
- data stories are really very popular.
- And I think that not to not to hit this nail too many times in our talk here today,
- but I think that that is underappreciated.
- I think there's oftentimes a sense that these are vegetable stories
- when in fact, when we look at the look at the numbers and the metrics,
- we see that these are actually quite popular stories when done well.
- Right. The public wants information that's more trustworthy and reliable
- and data and science is a way that we bring that to our journalism.
- Right. To me, there's a lot of it comes down to the editing.
- And this is true in traditional stories as well.
- Is data stories can sometimes lack that ground truth thing
- and that connection to what's really out there. Right.
- They can sometimes be what in a in a traditional story
- would be called a notebook dump.
- You know, so, hey, I did a data analysis.
- Here's 15 things I found from looking at the data.
- And it's just like a bunch of stuff.
- And it's not clearly focused enough that it connects directly
- with a busy person who who is just trying to go through the headlines and read the news.
- And so to me, the best data stories, not always,
- but oftentimes are about one finding or one very simple statistic.
- And then they don't overload all the numbers and stuff in the way
- that there's there's a good reason that academic papers do that. Right.
- And I do think a well presented data story will offer, you know, below the fold,
- so to speak, in the news or on a link that goes off to a methodology page
- or some code and data that you posted online.
- It will provide that extra stuff for the reader who wants more of it.
- And to verify it's kind of it's it's reproducibility.
- But the headline, what we call news, the nut graph,
- the central finding of any piece needs to be almost always like one thing
- with maybe just one or two numbers that really connect
- directly as possible to the reader. Why is this news, you know?
- Yeah. And so I guess that would explain then why there's often
- a big series around a large data story, right?
- Each one is telling a different piece of it.
- It can be, yes. Yeah. All right.
- What are some of the biggest challenges that you face in doing this kind of work?
- Gee, I mean, it does require more technical skills
- than I think a lot of people receive in their journalism training.
- You know, over time, it's something that any person could learn.
- Being a data journalist is not like being an opera singer or the piano player
- at the orchestra. It's not something that requires you're born
- with some incredible innate talent.
- It just requires that you learn
- numeracy and kind of set of kind of spreadsheet like skills
- for how to get your hooks into clean up, deal with and interview a data source.
- And so I think that that is its own little educational ladder
- that you have to climb to to do.
- But I think it's really attainable to anyone.
- But I think starting out, that can be a challenge, you know,
- in the same way that starting out at any professional job
- requires a little bit of of learning.
- The data that is often most newsworthy is often difficult to acquire.
- So it's there's cases where, you know, the government has a data set
- that you want to do a story about, but they're not giving it to you.
- And so you got to figure out how to get it.
- And so that kind of struggle to get the data that will make the news
- is a challenge. Cleaning data, as anyone who works in what is now
- known as data science and a lot of kind of these more practical applications
- of statistics will tell you, cleaning the data can end up being
- about 95 percent of the work.
- So the actual analysis that you do can be quite simple,
- especially for a journalism story.
- But once you even have acquired the data, just getting it into shape
- where it's ready to analyze can be a ton of work
- just due to errors in how it's been structured or formatted.
- That the need to to kind of clean up values, all the different ways
- that you can spell the street names of New York or L.A.
- If you're a police officer filling out a citation form, right,
- can lead to quite a lot of variations that need to be mopped up.
- So cleaning is in and learning the skills of cleaning
- and how to do it correctly and all that can be a bit of a challenge.
- And it's oftentimes most of the job.
- All right. And I appreciate your time and thank you so much for stopping by.
- Thanks for getting Unspun with me this week.
- Unspun is a production of me, Amanda Sturgill,
- and is a proud member of the MSW Media family of podcasts.
- Some of your thoughts and ideas about trickery in the news on Gmail
- at theunspunpodcast@gmail.com.
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- at theunspunpodcast.substack.com.
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Episode 20: Data journalism (feat. Ben Welsh)
By Ben Welsh • • Unspun in Pocket Casts
Recording
Listen on the original podcast site