Thinking. Feeling. And Exploring (Computational Journalism).
Hey there!
Last time I wrote about how teams approach interdisciplinary collaboration, specifically as it pertains to the media and tech space.
Today I want to go in slightly deeper on the individual: On a team, how do we best contribute to the problem-solving process?
This question made me reflect on a class I took this quarter at Stanford University’s d.School. A few weeks ago, Debbie Senesky, an assistant professor in the Department of Aeronautics and Astronautics, spoke to us and introduced herself as “an engineer by training.” It was interesting that she qualified it with a “by training,” as most of us introduce ourselves purely by our role (e.g. “an engineer”). It made me wonder when we all started compartmentalizing ourselves by our role in society, when after all it was simply a “training” we chose to take.
As humans, we don’t think in just one domain — we think about whatever we care about, and it is only natural to have a multitude of interests. So why have we become almost myopic about what conversations we can participate in or how we believe we can contribute to society?
Instead of engineer or journalist, we should think of ourselves as people trying to solve problems for people.
Today, it seems that media is the new tech and tech is the new media. In both spaces, we’re trying to figure out how technologists and journalists can collaborate, who’s doing what, and what needs to be done.
(This actually gives me solace. As a college student, I often get the “what do you want to do?” after graduation question. Now I can tell them: “Well, I care about media and tech and it seems that they don’t even know who’s doing what yet, so I think I have time to figure it out. Maybe it’ll be a role that we don’t even have a name for yet.”)
Confusion over the intersection of media and science is not new. The Printing Press, The Radio, The Television — they were all once “novel” inventions for delivering information to the public that people grappled with at the time.
We really started to grapple with the idea that journalism and tech were no longer separate entities within the past decade or so, when we learned that if we want journalism to stay alive (i.e. make money), we needed to take action. Get a new business model. Spin content in innovative ways. We saw revenue for television and newspapers decline and online information rise (and that spawned “new-media” experiments — did you know that blogs and social media were once considered “new-media” experiments? If you think this is funny, check this out. My favorite terms on this “survival guide”: “Facebook group”, “Google Docs” and “URL” ).
And what did people think at the time? Well, in this one amazing collection of reports that the Nieman Lab produced, starting in 2013, you get the perspectives of technologists, journalists, writers, reporters, television producers and basically anyone who cares about how we communicate information.
And here we are today. With some questions resolved. Some questions revisited. And some questions prompting more questions.
Since my first post, I’ve continued to have the privilege of acting as a fly-on-the-wall in Exploring Computational Journalism, a project-based Stanford course that aims to address emerging issues in media and tech through interdisciplinary student collaboration.
For a big portion of the class, many teams continued to work in rather segregated groups. I approached one team where the journalist and computer scientists were not particularly engaging in conversation as to why that was the case.
The journalist answered that he felt it would be best to leave the technical work to the computer scientists. The computer scientists felt that it would be best to leave the editorial judgment such as fact-checking to the journalist.
To this, I asked the journalist:
“What makes you think [the computer scientists of the team] can code? Because they go to Stanford and study CS?”
And the journalist answered:
“Well, they’ve been doing it for a long time.”
I asked the computer scientist:
“What makes you think that [the journalist of the team] is going to be a better fact-checker than you? Is it the JSK sticker on his laptop?”
And he answered:
“Well, they’ve been doing it for a long time. So it’s likely they’re more knowledgeable and would take a shorter time.”
Precisely.
One student, whom I spoke to at the end of the course, described why his team segregated roles so distinctly between journalists and computer scientists: “It’s just more efficient.”
I’m a big fan of efficiency. I truly am. But the fact of the matter is this: Everyone knew (by their answers) that the only thing that distinguished each member by “expertise” was simply experience. And experience comes with time.
While experience is valuable — we live to experience — it is also something that we can obtain if we put our mind to it.
If the journalist puts in time to understand code, she can. That’s how the computer scientists learned how to code in the first place. If the computer scientist puts in the time to understand editorial judgment, she can. That’s how the journalists learned: time and experience reading and writing news.
So why is it so difficult for us to get over this self-perception that it is not our role, or in our capacity to partake in other fields than those that are familiar to us? Unlike in the corporate world, the class does not “hire” them as “engineers” or “journalists.” Yes, the instructors made it clear that the students would be working in interdisciplinary teams. And yes, they were asked to share what they studied. But as far as this goes, as Professor Debbie Senesky said, what you studied is just a “training” you chose to receive.
The interesting thing to note is that towards the last few weeks of the class, as the students raced to the deadline, I saw less of a segregation between the different roles. As push came to shove, all hands were on deck. Yes, those with more experience programming made the algorithms. Yes, those with more experience with news dug up sources. They conducted interviews. But that did not prevent everyone from talking to one another and grappling with the following question:
How do we solve this challenge that we were given?
How do we make this project better?
How can we, as a team, solve this problem so that it works for the people who will use it?
Well, I had the lovely pleasure of talking to Professor Carol Dweck after being inspired by her research on growth-mindset. I told her about the course and the dynamics aforementioned.
And I told her about how, in the last few weeks, everyone seemed to work together to solve problems for no other reason than they had to. There was a deadline. So they did it. Out of necessity.
And she suggested something along the following:
Well, if that is the case and they will all have to work together in the end, why don’t you tell them that in the first place? If they’re going to end up doing it anyways, they might as well save time and do it earlier.
True. Brilliant and true.
But here’s the one problem that I forgot to raise in the conversation that I will raise now.
We, as humans, have a hard time believing something unless we experience it ourselves. At the beginning of the course, our instructors dedicated time to share insights on how the students could collaborate in interdisciplinary teams. I confess that I forgot about this portion until after my conversation with Professor Dweck, as I too, even as an observer, got too caught up with how they did the projects rather than the importance of how.
Especially for those of us who are incredibly determined (this can, at times, mean that we’re stubborn — I certainly confess to this), we have a hard time taking in suggestions unless we ourselves have experienced the need to accept it.
This is why deadlines are so important. We work out of necessity.
This was the case around 10 years ago.
People went online, so journalists had to share stories online.
Krishna Bharat, the creator of Google News (and an instructor of this course), made his first prototype after 9/11 because there was so much information online and a lack of an aggregator that would collect and clearly communicate a variety of sources.
Chloe Sladden, former head of media at Twitter (and a Stanford alum), mentions something similar that “we went through a US election and that definitely established new best practices” (at Twitter). The election sparked conversations and the public needed a platform to see what’s happening.
And I’m writing this piece. Because I have a deadline.
So here’s the deal:
Whether we like it or not, many people consume information online. Technology is a tool we use to spread information (IT stands for “Information Technology”).
We are more similar than different, because regardless of what “training” we received, we are human beings.
A “journalist” in our class said the following:
“I get too many voices in my head when I interview people.”
A “computer scientist” in our class said this:
“I have nightmares about math problems.”
They’re the same problem. You just can’t stop thinking about interesting ideas.
We all have different strengths and talents that we bring to the table. That’s what makes us unique and that’s worth celebrating.
But at the end of the day, we come together with a shared quality: we’re human beings. And we’re solving problems, for other human beings.
So I’m going to take Professor Dweck’s advice and leave you on this note. You’re not going to believe me unless you experience the need yourself, but I’ll say it anyway:
We’re at a stage where information is consumed online. We have computational tools that can help us spread more information more efficiently. Journalists and technologists must come together, engage in conversation to communicate information. As humans, we feel. We empathize with stories. We also think. We think about data and use tools to help us gather more information.
We must strive to do both.
How?
By asking questions. It’s about the questions, not the answers.
So here’s my question to you:
What can you do to partake in this conversation?
And a question for me:
What can I do?
I don’t know. But it’s worth asking.
Until next time!
Meanwhile, if you’re interested in chatting with me — perhaps we can get some answers, perhaps we can get even more questions: feel free to reach out to me at ichoi@stanford.edu. I’m also on Twitter. And Instagram. And Facebook. More than happy to chat, especially with people who have ideas. On ideas about people.
Special thanks to R.B. Brenner for working with me on this piece, thanks to Krishna and Professor Maneesh Agrawala for the wonderful instruction in the course, thanks to Professor Jay Hamilton (who taught the course last year) for introducing me to R.B. so that I got the opportunity to write these pieces in the first place and thank you to everyone in the course who let me observe. And thank YOU for caring!