Flattened Conversations: Reply-Structure Loss in YouTube Comment Data

If you study replies on YouTube, your choice of collection tool decides who answered whom. This report measures by how much, on identical comments, and says which tool to use for what. Two videos were collected both through the official YouTube Data API and through interface-level extraction (yt-dlp). The two methods assign different parents to 48.5% and 42.3% of replies. Every divergent case has the same shape: where the interface records that C answered B, the API records that C answered the top-level comment A. This is the API working as documented — across 43,896 replies its parentId points to a top-level comment in 100.0% of cases — but its size had not been measured. Across four corpora (n = 267,505), 10.8% to 65.5% of replies answer another reply, most in exactly the conversational material interaction research selects for. The interface method recovers that structure only up to a platform nesting cap at depth 3, so the usable gain over the API is 8.6–24.3% of replies, not 42–49%. Below the cap the recovered edges are the best-supported in the data: across all four corpora, the platform-inserted address marker agrees with the depth-2 parent in all but 3 of 18,114 resolved cases. At the cap, agreement falls to 48–82%. The two exports also differ in text. For the same comments, the API omits the space after an inserted address marker in 15.7% of marked replies, where the interface text keeps it — a trap for any tool that resolves mentions from API text. Practical rule: take time from the API, structure from interface-level extraction below the cap, record depth on every edge, and keep the two parent fields apart.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-21
DOI
https://doi.org/10.5281/zenodo.22882812
Primary Topic
Topic Modeling
Type
preprint
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Flattened Conversations: Reply-Structure Loss in YouTube Comment Data

Max Sedlmair
Zenodo (CERN European Organization for Nuclear Research)
Topic Modeling
preprint

Flattened Conversations: Reply-Structure Loss in YouTube Comment Data

Max Sedlmair
preprint en

Abstract

If you study replies on YouTube, your choice of collection tool decides who answered whom. This report measures by how much, on identical comments, and says which tool to use for what. Two videos were collected both through the official YouTube Data API and through interface-level extraction (yt-dlp). The two methods assign different parents to 48.5% and 42.3% of replies. Every divergent case has the same shape: where the interface records that C answered B, the API records that C answered the top-level comment A. This is the API working as documented — across 43,896 replies its parentId points to a top-level comment in 100.0% of cases — but its size had not been measured. Across four corpora (n = 267,505), 10.8% to 65.5% of replies answer another reply, most in exactly the conversational material interaction research selects for. The interface method recovers that structure only up to a platform nesting cap at depth 3, so the usable gain over the API is 8.6–24.3% of replies, not 42–49%. Below the cap the recovered edges are the best-supported in the data: across all four corpora, the platform-inserted address marker agrees with the depth-2 parent in all but 3 of 18,114 resolved cases. At the cap, agreement falls to 48–82%. The two exports also differ in text. For the same comments, the API omits the space after an inserted address marker in 15.7% of marked replies, where the interface text keeps it — a trap for any tool that resolves mentions from API text. Practical rule: take time from the API, structure from interface-level extraction below the cap, record depth on every edge, and keep the two parent fields apart.

Zenodo (CERN European Organization for Nuclear Research)
Topic Modeling
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Flattened Conversations: Reply-Structure Loss in YouTube Comment Data — Max Sedlmair · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS