Devil Scrapes

Posted on Sep 9 Originally published at devilscrapes.com

Twitch Chat Scraper and 2 More Live-Stream Chat Archives

#webscraping #apify #socialmedia #python

Originally published on the Devil Scrapes blog.

A Twitch chat scraper, a Kick chat archive, and a YouTube transcript export solve the same underlying problem from three different angles: none of the three platforms hands you a clean, structured record of what was said during a broadcast. Twitch retains a raw replay but no export tool; Kick has no chat history API at all; YouTube captions exist but aren't built for bulk pulls. Here's what each of our three Actors returns, what teams actually build with the output, and what it costs.

The platform gap, briefly

Live-stream chat and captions are some of the richest unstructured data on the internet — real-time audience reaction, sponsor call-outs, moderation events, comprehension-level language samples — and almost none of it is queryable in bulk from the platform itself. Twitch's chat replay lives behind an internal GraphQL endpoint, not the public Helix API. Kick has no chat-history endpoint whatsoever; once a stream ends, the conversation is gone from every Kick surface. YouTube's caption tracks are public but the timedtext endpoint isn't designed for hundreds of sequential pulls. We built one Actor per platform because each one needed a different transport, not because the underlying job differs.

Twitch: VOD chat, after the fact

Twitch Chat Scraper — VOD Chat Archive walks the same VOD-replay endpoint Twitch's own player uses and returns one row per chat message from a published VOD — no login required. Each row carries message_text, structured message_fragments (so emote IDs survive, not just literal emote shortcodes), user_color, badges, and message_offset_seconds, the position within the VOD the message was posted at.

That offset field is the one people underrate. Join it against the VOD timeline and you can locate exactly where chat spiked — a common proxy for "something worth clipping happened here" — without watching the broadcast back.

Pricing: $0.20 actor-start plus $0.001 per message, i.e. $1.20 per 1,000 messages. A 5,000-message stream costs about $5.20.

Kick: real-time, because there's no other option

Kick Chat Scraper & Real-Time Archive connects to the same public Pusher WebSocket Kick's own site uses to render chat, subscribes to up to 20 channel slugs at once, and archives every message live — sender, derived role (moderator, vip, subscriber, etc.), raw badges, chat color, and both the platform's send timestamp and our scrape timestamp.

The limitation that matters here isn't a limitation of the Actor, it's a limitation of Kick: this only captures chat sent while the run is active. There's no upstream to query for last week's stream, because Kick doesn't store it either. If you want a record of a future stream, the run has to be live during it.

Pricing: identical structure to Twitch — $0.20 actor-start plus $0.001 per message, so $1.20 per 1,000 messages archived across all subscribed channels combined.

YouTube: transcripts, not chat, but the same job

YouTube Transcript Scraper & Bulk Downloader is a different shape of data — one row per video, not per message — but it answers the same underlying question: what did the audience actually hear or say. It pulls manual or auto-generated caption tracks in any language and returns the full joined transcript_text, optional timed segments, channel name, title, duration, and every available_languages code on the video, from a list of URLs or bare video IDs. No API key needed.

Pricing: $0.20 actor-start plus $0.004 per transcript, i.e. $4.20 per 1,000 transcripts — a different unit from the chat Actors (per-video, not per-message), so don't compare the two rates directly.

What people actually build with this

Moderation research — pull Twitch or Kick chat across a batch of streams and quantify how often a term, slur list, or brigading pattern appears before deciding on a moderation policy change.
Sentiment and hype detection — bucket message_offset_seconds into time windows and chart message volume against the stream timeline; spikes correlate strongly with in-game or in-broadcast events worth reviewing.
Sponsor-mention auditing — search archived chat and transcript text for a brand or product name to check whether — and how — a sponsored segment actually landed with the audience, independent of what the creator's read reported.
Creator analytics — a talent agency or network archiving chat and transcripts across a roster of streamers to build engagement benchmarks that don't depend on the platform's own (often coarse) analytics dashboard.
NLP and RAG datasets — transcripts feed directly into a vector store or LLM context window for conference-talk or podcast search; chat archives are commonly used as training or evaluation data for toxicity and spam classifiers, with badges and sender_role as useful features.
Post-broadcast review — a streamer or their mod team reviewing an old VOD's chat for context around a moment, a decision, or a dispute, without scrubbing back through hours of stream.

How we handle the platform-side friction

All three Actors sit on the same infrastructure the rest of our fleet uses: we rotate curl-cffi browser TLS fingerprints (Chrome, Firefox, Safari) on every hop that could fingerprint a client, route through Apify Proxy with fresh sessions when a target pushes back, and retry with exponential backoff on 408/429/5xx up to five attempts per request, honoring Retry-After when it's sent. Every row is Pydantic-validated before it lands in your dataset, with ISO-8601 timestamps and stable IDs — so you can pull results as JSON, CSV, or Excel straight from the Apify Console, or via the API, without a cleanup pass first.

Run it on Apify

Every new Apify account starts with free trial credit, no card required, enough to test each Actor's output shape before committing spend.

FAQ

Can I get live Twitch chat, not just VOD chat, with these Actors?

Not with the Twitch Actor described here — it reads the VOD replay endpoint, which only exists once a broadcast has ended and Twitch has processed the recording. Live IRC-over-WebSocket chat is a separate protocol and a different Actor.

Why does Kick chat archiving require the run to be live during the stream?

Because Kick itself doesn't store chat history anywhere — not for us, not for anyone. There's no upstream endpoint to backfill from once a stream ends, so real-time capture is the only way to keep a record.

Do these Actors need a Twitch, Kick, or Google account to run?

No login is required for any of the three. Twitch's VOD replay and Kick's chat WebSocket are both public surfaces every viewer's browser reaches without authentication, and YouTube captions are public metadata attached to public videos.

What happens if a video has captions disabled or a VOD has expired?

Each Actor treats that as a normal, non-error outcome: the row (or VOD) is skipped, logged, and the run finishes SUCCEEDED rather than failing the whole batch over one unavailable item.

Which one should I use for sentiment analysis on a specific game or topic?

If you need audience reaction as it happened during a broadcast, use the Twitch or Kick chat Actor and bucket by message_offset_seconds or sent_at. If you need what the creator said, use the YouTube transcript Actor against their video and run your sentiment model over transcript_text.

Devil Scrapes builds and maintains 200+ pay-per-result web scrapers on the Apify Store. Blocks, retries and proxies handled by us. Browse the full catalog or commission a custom Actor.

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