E152: Peeking Behind TikTok’s Curtain - What a Data Reporter Learned from 15M Videos

E152: Peeking Behind TikTok’s Curtain - What a Data Reporter Learned from 15M Videos

Author: MDDC Press Association January 28, 2026 Duration: 52:53

In this episode, Kevin talks with Jeremy Merrill of The Washington Post about an ambitious investigation into how TikTok’s recommendation algorithm really works. Jeremy explains how he and his colleagues analyzed data from more than 1,000 volunteers and 15 million videos to better understand personalization, stickiness, and scale on the platform. They discuss what the data reveals about how feeds are shaped, why some content is harder to escape than others, and what this kind of reporting can—and can’t—tell us about influence, attention, and transparency in algorithm-driven media.

Read, "We teamed up with our audience to measure TikTok’s unmistakable draw..."
View this story's interactive map, "Are you in TikTok’s cat niche? What 121,000 videos reveal."
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Ever wonder how a major local story really came together, or what a reporter was thinking when they asked that tough question? Five Dubs Podcast pulls back the curtain on the newsrooms across Maryland, Delaware, and Washington D.C. Hosts Rebecca Snyder and Kevin Berrier, through the MDDC Press Association, sit down with the journalists themselves to explore the how and why behind the headlines you read each day. This isn't just a recap of the news; it's a conversation about the process, the challenges, and the sometimes surprising human stories that unfold during the pursuit of a story. In each episode, you'll hear firsthand accounts of what it takes to cover communities in this dynamic region, from the initial tip to the published piece. The Five Dubs Podcast offers a unique, insider perspective on the function and future of local media, making the landscape of regional journalism more accessible and understandable. It’s for anyone curious about the mechanics of news gathering and the people dedicated to telling our local stories.
Author: Language: English Episodes: 100

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