How The New York Times is Building Thoughtful Comment Sections in the Trump Era

Over the past few months, Patrick has spoken to several leaders in the world of journalism and for this episode, we’re welcoming back Bassey Etim, community editor at the New York Times. Bassey was originally on Community Signal in December of 2015 and it’s overwhelming to think about how public perception of the media and the Times, in particular, has changed since then. To give you some context, Barack Obama was still in office at the time of that interview and Donald Trump had yet to win a primary.

Patrick brings up an important question during this conversation: How are moderators at the New York Times doing? And perhaps that question can largely be answered by how Bassey manages his 14-person team. He shares how the team blows off steam, what he does to advance people within his team, and how he views AI as a human-powered tool to moderation, not a human-replacing one. Is it Bassey’s emphasis on people and objectivist journalism that powers a positive environment amongst his team and the comment sections at the Times? I think so! Bassey also shares:

  • The impact of the midterm elections and politics in general on moderators at the Times
  • His own career path at the Times and how he elevates others for growth opportunities
  • Getting AI machines to ask humans for help

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Big Quotes

On positively fostering comment sections: “We want to make comment sections [that] people want to read. There’s two elements to that. One is that the comment sections are interesting and good and reflect modern society. They don’t feel like they’re in some weird cloistered bubble. The other is that you feel safe, you don’t feel dirty when you read them.” –@BasseyE

On using automation to create transparency around moderation decisions: “It’s really interesting to see how we could [use automation to] educate people along the process of submitting content to make sure that they have the highest probability of being approved. Everyone loves that automation. Members don’t want to have their content removed after they spend time on it. Moderators don’t want to remove content and have to talk to the member or deal with whatever the fallout of that is and then have to readjust or re-approve it once they try to submit it again. That’s a perfect example of automation done well that everyone would like.” –@patrickokeefe

On not repeating the industry’s outsourcing mistakes: “The really important thing for us in the industry is probably going to be avoiding that old tech problem which is that there’s this piece of technology and it can solve everything. … The technology is really a tool. Just because you have a good tool you can’t just have one person wield it. … You’re going to need a lot of people using the same tool to truly be effective.” –@BasseyE

On machine learning’s biggest problem: “If you let machine learning models operate in the wild without trained human intervention, what you’re going to be doing is perpetuating a cultural cycle of silencing certain people’s voices, filtering out a critical mass of people from certain communities. Wouldn’t it be quite ironic if the thing that’s supposed to save us all, technology, only winds up taking all the human biases and codifying them into code so that, unaccountable executives can say, ‘Oh, well, the model does this. We’ll try to fix the model sometime. We just don’t understand the complexities of the model.'” –@BasseyE

About Bassey Etim

Bassey Etim is the community editor for the New York Times, a novelist, and a musician. He’s currently putting the finishing touches on his second book and planning for his wedding next year. Bassey is a first-generation American with roots in Nigeria. He’s from Milwaukee, Wisconsin and graduated from the University of Wisconsin School of Journalism.

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