Where Food YouTube Ate Last Month: 500 Videos Mapped

The chains win. Burger King was visited in 19 videos and McDonald’s in 18, ahead of Taco Bell and Chick-fil-A with 11 each. Fast-food ranking videos, secret-menu tests and “least ordered item” challenges walk through several chains at once, which is how the same names keep coming back.

• One new restaurant made the chart. DaBaby’s No Knife Chicken and Waffles in Charlotte was visited, reviewed and argued about in 10 videos under that exact name, and in 19 videos counting every way it was written.
• The long tail is the real map. 1,401 of the 1,458 names appear in a single video: independent restaurants, cafes and night-market stands from one creator’s trip.
• Buffets are a genre of their own. Golden Corral appears in 4 videos, next to a long list of all-you-can-eat challenges.

Most videos stay at one or two places (220 of 500). Food tours and chain rankings make up the right-hand side: 119 videos visit five places or more.

All 500 videos, most viewed first. Search a place to find every video that visits it, or a channel to see where it went this month.

1. The videos

YouTube’s own search, filtered to “This month” and sorted by view count, across 22 food queries: restaurant review, food tour, street food tour, Michelin star restaurant, cheap eats, best burger, best sushi, fast food review, all-you-can-eat buffet, NYC, London and LA food tours, and more. We kept videos of 4 to 30 minutes whose title is about eating out, at most five per channel, and checked each video’s spoken language so every one is in English. Where a video has human-made English captions it went first (75 of them); the rest use YouTube’s automatic captions. Then we took the most viewed.

2. The extraction

Each video went through the TubeExtract API with the one-column schema below, 50 videos per request. The schema asks for the proper name of each place the creator visits in person, and leaves out places that are only mentioned or recommended.

3. The names

Every name is exactly as the extraction returned it, with nothing merged, renamed or removed. Captions spell places the way they sound, so the same place can appear as “Popeye’s” in one video and “Popeyes” in another; counts on this page are by exact name. Search the table for part of a name to catch every spelling.

youtube-restaurant-visits-2026-08-29_to_09-28.csv: 500 rows, one per video, with the columns video_url, video_title, channel, views, minutes, captions, places_count and places_visited (names separated by semicolons). Free to use under CC BY 4.0, with a link back to this page.

The whole dataset is one schema and a list of URLs. Add columns for the city, the dish ordered or the creator’s verdict and you have a restaurant guide built from the videos people already watch. You describe the columns, send up to 200 videos per request, and get typed rows back, at 10 credits per started minute of video.

curl https://api.tubeextract.dev/v1 \
  -H "X-API-Key: $TUBEEXTRACT_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "videos": ["https://www.youtube.com/watch?v=...", "... up to 200 URLs"],
    "schema": {
      "multiple": true,
      "columns": [
        { "name": "restaurant", "type": "string",
          "description": "The proper name of a restaurant, cafe, bar or food stall the creator visits in person" },
        { "name": "city", "type": "string",
          "description": "The city the place is in" },
        { "name": "verdict", "type": "enum",
          "options": ["recommends", "mixed", "does not recommend"],
          "description": "What the creator thinks of the place" }
      ]
    }
  }'

Start with the quickstart, or read how to write a schema for the column wording that shapes the rows.