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feat: surface YouTube + TikTok top comments alongside Reddit (#260)

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* feat(normalize): pass YouTube top_comments through with Reddit-compatible shape

_normalize_youtube silently dropped top_comments after enrich_with_comments
populated them, so the downstream signals/render/entity layers never saw
YouTube comments. Map likes->score and text->excerpt so the existing
Reddit-compatible readers Just Work.

Shared _remap_comments helper will be reused for TikTok in a later commit.

* feat(tiktok): fetch top comments via ScrapeCreators when opted in

Mirrors the youtube_comments pattern: new env.is_tiktok_comments_available
gate (requires SCRAPECREATORS_API_KEY + tiktok_comments in INCLUDE_SOURCES),
tiktok.enrich_with_comments ranks posts and fetches via
GET /v1/tiktok/video/comments. Vote field is digg_count; text and user.nickname
come across verbatim. Pipeline calls the enricher right after TikTok search
when the gate is open.

Comment-fetch errors never crash the pipeline — the enricher returns an
empty list on 4xx/5xx.

* feat(normalize): pass TikTok top_comments through with digg_count->score mapping

Instagram uses the same shortform normalizer and has no comment fetcher
today, so the key is harmlessly absent there — no Instagram regression.

* feat(signals): add YouTube + TikTok top-comment score to engagement formula

Mirrors Reddit's 10% top-comment slot. Without top_comments present, the
formula reduces to views-dominant weighting; with a high-signal comment,
the item gets a meaningful bump (log1p(10k) ~ 9.2, weighted 0.10 = ~0.92
on the engagement score).

Updated the existing dominant-weight and missing-fields tests to the new
weights (0.45/0.32/0.13 for YT, 0.45/0.27/0.18 for TT). Views still dominate.

* feat(render): source-aware thresholds and vote labels for top comments

10 upvotes on Reddit signals community interest; 10 likes on a viral
TikTok is noise. Introduce per-source minimums (reddit 10, youtube 50,
tiktok 500) and native vote labels ('upvotes' for Reddit, 'likes' for
YT/TT). First-pass numbers — tune after live observation.

* docs: generalize top-comment quoting to YouTube + TikTok, add tiktok_comments opt-in

Synthesis instructions previously called out Reddit top comments only.
Now cover Reddit/YouTube/TikTok uniformly with source-appropriate vote
labels (upvotes vs likes), and explicitly frame YT transcript highlights
and comments as complementary signals. README and setup-wizard copy
document the new tiktok_comments INCLUDE_SOURCES token.

---------

Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
This commit is contained in:
Matt Van Horn
2026-04-15 08:26:06 -04:00
committed by GitHub
parent 242e38ef56
commit 082efe03e3
12 changed files with 678 additions and 29 deletions
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tests/test_normalize_v3.py
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@@ -49,6 +49,165 @@ class NormalizeV3Tests(unittest.TestCase):
)
self.assertEqual([], normalized)
def test_youtube_top_comments_passthrough_with_field_mapping(self):
"""YT comments from enrich_with_comments use likes/text; normalize must
carry them into metadata as the Reddit-compatible {score, excerpt} shape."""
items = [
{
"video_id": "vid-1",
"title": "How to deploy",
"url": "https://youtube.com/watch?v=vid-1",
"channel_name": "Example",
"date": "2026-03-01",
"engagement": {"views": 10000, "likes": 500, "comments": 30},
"top_comments": [
{"author": "Alice", "text": "Best tutorial ever", "likes": 120, "date": "2026-03-02"},
{"author": "Bob", "text": "Helped me ship", "likes": 45, "date": "2026-03-03"},
{"author": "Carol", "text": "Solid walkthrough", "likes": 7, "date": "2026-03-04"},
],
}
]
normalized = normalize.normalize_source_items(
"youtube", items, "2026-02-15", "2026-03-17",
)
self.assertEqual(1, len(normalized))
top = normalized[0].metadata.get("top_comments")
self.assertIsNotNone(top)
self.assertEqual(3, len(top))
# First comment: likes->score, text->excerpt
self.assertEqual(120, top[0]["score"])
self.assertEqual("Best tutorial ever", top[0]["excerpt"])
self.assertEqual("Alice", top[0]["author"])
self.assertEqual("2026-03-02", top[0]["date"])
# Preserves ordering from input (already sorted desc upstream)
self.assertEqual(45, top[1]["score"])
self.assertEqual(7, top[2]["score"])
def test_youtube_top_comments_empty_list_passes_through_cleanly(self):
items = [
{
"video_id": "vid-2",
"title": "Short clip",
"url": "https://youtube.com/watch?v=vid-2",
"channel_name": "Example",
"date": "2026-03-01",
"engagement": {"views": 50, "likes": 2},
"top_comments": [],
}
]
normalized = normalize.normalize_source_items(
"youtube", items, "2026-02-15", "2026-03-17",
)
self.assertEqual(1, len(normalized))
# Empty list is fine; metadata may have empty top_comments or omit it.
top = normalized[0].metadata.get("top_comments", [])
self.assertEqual([], top)
def test_youtube_without_top_comments_key_does_not_crash(self):
items = [
{
"video_id": "vid-3",
"title": "No comments fetched",
"url": "https://youtube.com/watch?v=vid-3",
"channel_name": "Example",
"date": "2026-03-01",
"engagement": {"views": 100, "likes": 5},
}
]
normalized = normalize.normalize_source_items(
"youtube", items, "2026-02-15", "2026-03-17",
)
self.assertEqual(1, len(normalized))
self.assertEqual([], normalized[0].metadata.get("top_comments", []))
def test_youtube_top_comments_feed_top_comment_score_signal(self):
"""Integration: after normalize, signals._top_comment_score should
return log1p(first comment score) for YT, proving the full chain."""
from lib import signals
import math
items = [
{
"video_id": "vid-4",
"title": "Viral comment thread",
"url": "https://youtube.com/watch?v=vid-4",
"channel_name": "Example",
"date": "2026-03-01",
"engagement": {"views": 1000, "likes": 50, "comments": 10},
"top_comments": [
{"author": "A", "text": "Legendary", "likes": 9999, "date": "2026-03-02"},
],
}
]
normalized = normalize.normalize_source_items(
"youtube", items, "2026-02-15", "2026-03-17",
)
self.assertAlmostEqual(math.log1p(9999), signals._top_comment_score(normalized[0]), places=4)
def test_tiktok_top_comments_passthrough_with_digg_count_mapping(self):
"""TikTok comments from enrich_with_comments use digg_count/text;
normalize must map to the shared {score, excerpt} shape."""
items = [
{
"id": "tt-1",
"text": "POV: shipping on Friday",
"url": "https://www.tiktok.com/@u/video/tt-1",
"author_name": "u",
"date": "2026-03-01",
"engagement": {"views": 50000, "likes": 2000, "comments": 300},
"top_comments": [
{"author": "Alice", "text": "dead", "digg_count": 1200, "date": "2026-03-02"},
{"author": "Bob", "text": "so real", "digg_count": 400, "date": "2026-03-03"},
],
}
]
normalized = normalize.normalize_source_items(
"tiktok", items, "2026-02-15", "2026-03-17",
)
self.assertEqual(1, len(normalized))
top = normalized[0].metadata.get("top_comments")
self.assertEqual(2, len(top))
self.assertEqual(1200, top[0]["score"])
self.assertEqual("dead", top[0]["excerpt"])
self.assertEqual("Alice", top[0]["author"])
self.assertEqual(400, top[1]["score"])
def test_tiktok_without_top_comments_does_not_crash(self):
items = [
{
"id": "tt-2",
"text": "plain clip",
"url": "https://www.tiktok.com/@u/video/tt-2",
"author_name": "u",
"date": "2026-03-01",
"engagement": {"views": 1000, "likes": 20},
}
]
normalized = normalize.normalize_source_items(
"tiktok", items, "2026-02-15", "2026-03-17",
)
self.assertEqual([], normalized[0].metadata.get("top_comments", []))
def test_tiktok_top_comments_feed_top_comment_score_signal(self):
from lib import signals
import math
items = [
{
"id": "tt-3",
"text": "viral",
"url": "https://www.tiktok.com/@u/video/tt-3",
"author_name": "u",
"date": "2026-03-01",
"engagement": {"views": 100000, "likes": 5000, "comments": 500},
"top_comments": [
{"author": "A", "text": "this aged well", "digg_count": 50000, "date": "2026-03-02"},
],
}
]
normalized = normalize.normalize_source_items(
"tiktok", items, "2026-02-15", "2026-03-17",
)
self.assertAlmostEqual(math.log1p(50000), signals._top_comment_score(normalized[0]), places=4)
def test_grounding_requires_a_usable_date(self):
items = [
{