Every fall, leading startups, companies, and organizations in NYC pose business challenges to Cornell Tech. In Product Studio, teams develop and present new products, services, and strategies that respond to those challenges.
My team is responding to The New York Times (Community) challenge.
The challenge is as follows:
How might we create a safe place for people of all backgrounds to discuss important local, national, and global issues?
Currently the majority of New York Times commenters are older white men. But that group is just a small slice of the larger population, which means that many readers—people who have valuable experiences and expertise to share—are shut out of the conversation.
As a solution, we want to invite commenters from all backgrounds in, as a way of expanding and enriching the New York Times’s user-generated content. By celebrating different voices, we believe we can create value for both the New York Times and its readers.
We arrived at our current product after a few wrong turns. After the last sprint, we received some critical feedback and we realized that we were too focused on creating value for the New York Times, not its readers.
So we conducted some user interviews with Times readers and we learned that people are demotivated from commenting when they don’t think they have anything informative to contribute; when they don’t think their comments will be seen or valued; and when they don’t care about the issue.
Meet Chumeng, an Asian-American woman (Chumeng is actually Chinese, but for the sake of our user story, we’re saying she’s Asian-American). She’s reading an article about how more Asian Americans are becoming Democrats. Looking at the current layout of the comments section, there’s no space carved out for her opinion. She doesn’t trust that her opinion will be valued here, and she doesn’t immediately have an idea about what information she could share.
With the new Q+A feature, the story is different. Now Chumeng’s opinion is being solicited directly with a question related to her background. She is being prompted to share something specific, so she thinks, Aha! I have something to add here. And she can see the opinions of other Asian-Americans on display, so she knows it’s an environment that will support her opinion. And because the question is tagged “Asian American,” she can see how her background makes her especially qualified to make a comment.
To validate our product, we built wireframes for targeted A/B testing. Of the people we tested, 90% said they were more likely to read the comments with our interface, 80% said they were more likely to comment, and 100% said they were more likely to do one or the other. We also saw a lot of smiles and got a lot of unsolicited positive feedback. And from our alumni crit, we got feedback that ranged from “There’s really something here” to concerns about our feature inviting trolls in as well.
There are two major parts to our system. One is the editor’s view and one is the reader’s view. The editors input the article into the New York Times content management system, and through IBM Watson’s Alchemy API, we can extract key concepts and entities from the article, and use these to generate targeted questions. The editors then formulate the appropriate questions, and these appear on the New York Times commenting platform.
|Delia Casa||Frances Coronel||Chumeng Xu|