A daily digest of new YouTube comments in Slack, without paying for comments you already saw

Description and use

An automation recipe for a daily Slack digest of new YouTube comments, filtering duplicates and channel replies, built with an Apify Actor and YouTube Data API v3.

Problem

Teams need a daily list of new comments from their own or competitor’s YouTube videos, but YouTube Studio only shows comments one at a time and only for the channel owner.

Outcome

The recipe delivers a daily Slack digest of new YouTube comments, grouped by video, ranked by likes, and filtered to exclude the channel’s own replies, while keeping API usage low by paging only until older comments are reached.

Workflow

• 1. Configure Apify Actor input with channel URLs, maxVideosPerChannel, sortBy=time, publishedAfter=2 days, maxComments=0, authorData=omit.
• 2. Run the Actor via ApifyClient in a Python script.
• 3. Load seen_comments.json to track already reported comment IDs.
• 4. Iterate over Actor dataset items, filter out failures, already seen comments, and comments by the video owner.
• 5. Group remaining comments by video title and ID.
• 6. For each video, sort comments by likeCount and select top five.
• 7. Format comments into a Slack message with links and truncate text for Slack markup.
• 8. Send the message to the configured Slack incoming webhook.
• 9. Save the new comment IDs back to seen_comments.json for the next run.

Evidence

Best Damn YouTube Comments Scraper is an Apify Actor that reads comments through the official YouTube Data API v3.

Source

In a test run on the five latest uploads of a large tech channel, two videos had nothing new and returned zero comments, and the whole run delivered 1,177 comments using 19 quota units.

Source

The script runs the Actor, drops comments it has already reported, groups the rest by video and posts the top five per video to a Slack incoming webhook.

Source