# PBS Frontline Transcripts Scraper (`jungle_synthesizer/pbs-frontline-transcripts-scraper`) Actor Scrape full transcripts from PBS Frontline documentary films. Extracts transcript body text, speaker labels, film metadata (air date, synopsis, credits), and topic tags from all Frontline documentaries on pbs.org. - **URL**: https://apify.com/jungle\_synthesizer/pbs-frontline-transcripts-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, News, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## PBS Frontline Transcripts Scraper Scrapes full transcripts from PBS Frontline documentary films. Returns one record per documentary — title, synopsis, air date, speaker-labeled transcript body, and topic metadata. Covers the active Frontline archive (250+ films back to ~1995) using the site's sitemap index for discovery. Frontline is one of the few public-broadcasting sources where transcripts are routinely cited in academic and journalism contexts. Each film runs 60-120 minutes and produces 30-80KB of clean, fact-checked, single-narrative text — a different unit of value from fragmented panel-show or wire-copy corpora. ### What It Scrapes Every record comes from `pbs.org/wgbh/frontline/documentary//`. The transcript lives inline on the main documentary page (the `/transcript/` subpath was retired; transcripts are now embedded directly). Metadata comes from JSON-LD structured data blocks on the same page. ### Output Schema | Field | Type | Description | |-------|------|-------------| | `film_slug` | string | URL slug (e.g. `the-deal-trump-bukele-gangs-el-salvador`) | | `film_title` | string | Documentary title | | `film_url` | string | Canonical PBS URL | | `air_date` | string | Original broadcast date (YYYY-MM-DD) | | `duration_minutes` | number | Runtime in minutes | | `synopsis` | string | Brief description from page metadata | | `producers` | string | Comma-separated producing and directing credits | | `correspondents` | string | Comma-separated correspondent credits | | `related_topics` | string | Comma-separated PBS topic tags | | `body_html` | string | Full transcript HTML with `SPEAKER:` spans | | `body_text` | string | Plain-text transcript with inline speaker labels | | `speakers` | string | Comma-separated unique speaker labels | | `has_viewer_discretion_notice` | boolean | True if the film flags mature content | | `related_film_urls` | string | Comma-separated URLs of cross-linked Frontline films | | `canonical_url` | string | Canonical page URL | | `source` | string | Fixed: `pbs.org/wgbh/frontline` | | `scraped_at` | datetime | ISO 8601 scrape timestamp | Speaker labels follow the Frontline convention: `NARRATOR`, `PRESIDENT DONALD TRUMP`, `NAYIB BUKELE`, etc. They are extracted directly from `LABEL:` spans — no inference, no cleanup required. ### Input Options **`startUrls`** (array, optional) — Specific documentary URLs to scrape. Leave empty to run the full sitemap discovery and scrape all available transcripts. **`maxItems`** (integer, optional) — Cap on total records. Default 0 (no limit). When using sitemap discovery, applies globally across all sitemaps. #### Example: Single film ```json { "startUrls": [ {"url": "https://www.pbs.org/wgbh/frontline/documentary/the-deal-trump-bukele-gangs-el-salvador/"} ] } ```` #### Example: Full archive crawl (all ~250 films) ```json { "maxItems": 0 } ``` #### Example: Recent 50 films ```json { "maxItems": 50 } ``` ### How It Works Discovery uses PBS Frontline's sitemap index at `pbs.org/wgbh/frontline/sitemap.xml`. The nine `sitemap-documentary` sub-sitemaps each hold up to 100 film URLs, ordered newest-first. Films without a transcript (some pre-rebuild older entries) are silently skipped. Metadata is parsed from JSON-LD blocks on each documentary page. The transcript and credits live in two Chakra UI accordion panels — panel 0 is the transcript, panel 1 is the credits. Speaker labels are extracted via a single regex pass on the `LABEL:` pattern Frontline uses consistently across its archive. The site is server-rendered Next.js with aggressive edge caching — no headless browser required, no proxy required. ### Pricing Charged per record scraped. Long-form transcripts (30-80KB each) are priced at a modest premium reflecting per-record research value. Start price applies per actor run regardless of record count. ### Notes - Films without a transcript are skipped gracefully and do not count toward `maxItems`. - Some older archive films have had their transcript pages rebuilt and may appear without speaker-label markup — body text is still returned when a transcript exists. - `body_html` preserves the original `` speaker spans for downstream NLP pipelines that want to distinguish speaker turns programmatically. ### Need Custom Fields or a Different Source? File an issue or get in touch. We can add fields, filter by topic, or build adjacent scrapers in the same broadcast-transcript vertical. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of documentary transcripts to scrape. Set to 0 for no limit. ## `startUrls` (type: `array`): Optional list of specific documentary URLs to scrape. Leave empty to discover all documentaries from the full sitemap index. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startUrls": [] } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startUrls": [] }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/pbs-frontline-transcripts-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startUrls": [], } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/pbs-frontline-transcripts-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "startUrls": [] }' | apify call jungle_synthesizer/pbs-frontline-transcripts-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/pbs-frontline-transcripts-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PBS Frontline Transcripts Scraper", "description": "Scrape full transcripts from PBS Frontline documentary films. Extracts transcript body text, speaker labels, film metadata (air date, synopsis, credits), and topic tags from all Frontline documentaries on pbs.org.", "version": "0.1", "x-build-id": "RCdX6uGkXiR1n9Zry" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~pbs-frontline-transcripts-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-pbs-frontline-transcripts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~pbs-frontline-transcripts-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-pbs-frontline-transcripts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~pbs-frontline-transcripts-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-pbs-frontline-transcripts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of documentary transcripts to scrape. Set to 0 for no limit.", "default": 0 }, "startUrls": { "title": "Start URLs", "type": "array", "description": "Optional list of specific documentary URLs to scrape. Leave empty to discover all documentaries from the full sitemap index.", "default": [], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```