# Reddit User Post Scraper (`saswave/reddit-user-post-scraper`) Actor $0.8 / 1000 results. High-performance data extraction tool designed to archive and analyze the historical activity of specific Reddit users. Track sentiment or conduct academic research on social behavior. Provide a user's digital footprint. Handles Posts or Comments - **URL**: https://apify.com/saswave/reddit-user-post-scraper.md - **Developed by:** [SASWAVE](https://apify.com/saswave) (community) - **Categories:** Automation, News, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $0.80 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ### Reddit User Post Comment Scraper High-performance data extraction tool designed to archive and analyze the historical activity of specific Reddit users. Whether you are tracking a financial influencer's sentiment or conducting academic research on social behavior, this scraper provides a complete forensic look at a user's digital footprint. Handles Posts or Comments ### FEATURES Bilingual Content Extraction: Seamlessly pulls all fields in both English and Arabic, including SEO titles, meta descriptions, and product highlights. Deep Ingredient Mapping: Captures complex ingredient lists (e.g., Ceramosides™, NAC, CoQ10) and identifies "Key Ingredients" vs. full rich-text breakdowns. Subscription Intelligence: Extracts specific discount tiers for recurring orders (e.g., 20% off for 1, 2, or 3-month cycles). Clinical & FAQ Data: Pulls the complete list of clinical FAQs, providing technical answers about product efficacy and safety. Rich Media Support: Scrapes high-resolution product images, promotional videos/reels, and full galleries. Inventory & Pricing: Monitors availableQuantity, retailPrice, finalPrice, and cost-per-capsule metrics. Operational Details: Extracts delivery estimates and pharmacy provider licensing information (e.g., DHA licenses). ### USE CASES Competitive Landscape Analysis: Monitor UAE health market trends by tracking Feelvaleo’s "Bestsellers" and new supplement launches. E-commerce Localization: Use the bilingual data to build localized catalogs for regional distributors in Dubai, Abu Dhabi, and beyond. Price Monitoring: Track real-time shifts in retail prices and subscription-based discounting strategies. Ingredient Research: Build a database of advanced nutraceutical ingredients and their marketed benefits in the Middle Eastern market. SEO & Content Strategy: Analyze meta-tags and H2 headers to understand how health products are optimized for search engines in the GCC region. ### OUTPUT Post ```json { "approved_at_utc": null, "subreddit": "TLRY", "selftext": "Rate to borrow continues down from 41.97% to 15.14% currently. Number of shares available to short is ticking up and is currently at 122,637. There are a ton of shorts added on the day of the announcement. They will be headed for the exit in huge numbers. I suspect there is currently over 30 million shares short. Those are squeeze numbers. The spark will be Rescheduling and Earnings/Projections on US opportunity on January 8th after the close. $30 is nearer than most think.", "author_fullname": "t2_a22naokb", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "Covering has begun...", "link_flair_richtext": [], "subreddit_name_prefixed": "r/TLRY", "hidden": false, "pwls": 7, "link_flair_css_class": "", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_1pzpqdr", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.78, "author_flair_background_color": null, "subreddit_type": "public", "ups": 39, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Bullish", "can_mod_post": false, "score": 39, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1767119382.0, "link_flair_type": "text", "wls": 7, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.TLRY", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Rate to borrow continues down from 41.97% to 15.14% currently. Number of shares available to short is ticking up and is currently at 122,637. There are a ton of shorts added on the day of the announcement. They will be headed for the exit in huge numbers. I suspect there is currently over 30 million shares short. Those are squeeze numbers. The spark will be Rescheduling and Earnings/Projections on US opportunity on January 8th after the close. $30 is nearer than most think.</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": false, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "link_flair_template_id": "b84a16b6-6cb8-11eb-add0-0e88b6d0e62d", "can_gild": false, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_3o7h8c", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "#46d160", "id": "1pzpqdr", "is_robot_indexable": true, "report_reasons": null, "author": "Anxious_Marzipan_881", "discussion_type": null, "num_comments": 44, "send_replies": true, "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/TLRY/comments/1pzpqdr/covering_has_begun/", "stickied": false, "url": "https://www.reddit.com/r/TLRY/comments/1pzpqdr/covering_has_begun/", "subreddit_subscribers": 22705, "created_utc": 1767119382.0, "num_crossposts": 0, "media": null, "is_video": false } ```` Comment ```json { "subreddit_id": "t5_3o7h8c", "approved_at_utc": null, "author_is_blocked": false, "comment_type": null, "link_title": "TLRY value", "mod_reason_by": null, "banned_by": null, "ups": 2, "num_reports": null, "author_flair_type": "text", "total_awards_received": 0, "subreddit": "TLRY", "link_author": "Anxious_Marzipan_881", "likes": null, "replies": "", "user_reports": [], "saved": false, "id": "oj8vstp", "banned_at_utc": null, "mod_reason_title": null, "gilded": 0, "archived": false, "collapsed_reason_code": null, "no_follow": true, "author": "Anxious_Marzipan_881", "num_comments": 11, "can_mod_post": false, "send_replies": true, "parent_id": "t1_oj8vloh", "score": 2, "author_fullname": "t2_a22naokb", "over_18": false, "report_reasons": null, "removal_reason": null, "approved_by": null, "controversiality": 0, "body": "They have not added.", "edited": false, "top_awarded_type": null, "downs": 0, "author_flair_css_class": null, "is_submitter": true, "collapsed": false, "author_flair_richtext": [], "author_patreon_flair": false, "body_html": "<div class=\"md\"><p>They have not added.</p>\n</div>", "gildings": {}, "collapsed_reason": null, "distinguished": null, "associated_award": null, "stickied": false, "author_premium": false, "can_gild": false, "link_id": "t3_1t0d4cv", "unrepliable_reason": null, "author_flair_text_color": null, "score_hidden": false, "permalink": "/r/TLRY/comments/1t0d4cv/tlry_value/oj8vstp/", "subreddit_type": "public", "link_permalink": "https://www.reddit.com/r/TLRY/comments/1t0d4cv/tlry_value/", "name": "t1_oj8vstp", "author_flair_template_id": null, "subreddit_name_prefixed": "r/TLRY", "author_flair_text": null, "treatment_tags": [], "created": 1777602106.0, "created_utc": 1777602106.0, "awarders": [], "all_awardings": [], "locked": false, "author_flair_background_color": null, "collapsed_because_crowd_control": null, "mod_reports": [], "quarantine": false, "mod_note": null, "link_url": "https://www.reddit.com/r/TLRY/comments/1t0d4cv/tlry_value/" } ``` ### 🛟 SUPPORT Share your runs with the developer team and create issues on error to help us improve actor quality. You might discover edge case we didn't test yet We stay available anytime # Actor input Schema ## `users` (type: `array`): Url contains /user/ ## `sort` (type: `string`): Select feature when providing users ## `feature` (type: `string`): Select feature when providing users ## Actor input object example ```json { "users": [ "Anxious_Marzipan_881" ], "sort": "new", "feature": "posts" } ``` # 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 = { "users": [ "Anxious_Marzipan_881" ] }; // Run the Actor and wait for it to finish const run = await client.actor("saswave/reddit-user-post-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 = { "users": ["Anxious_Marzipan_881"] } # Run the Actor and wait for it to finish run = client.actor("saswave/reddit-user-post-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 '{ "users": [ "Anxious_Marzipan_881" ] }' | apify call saswave/reddit-user-post-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=saswave/reddit-user-post-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit User Post Scraper", "description": "$0.8 / 1000 results. High-performance data extraction tool designed to archive and analyze the historical activity of specific Reddit users. Track sentiment or conduct academic research on social behavior. Provide a user's digital footprint. Handles Posts or Comments", "version": "0.0", "x-build-id": "5tWEfDvZOmxB9ScCj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/saswave~reddit-user-post-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-saswave-reddit-user-post-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/saswave~reddit-user-post-scraper/runs": { "post": { "operationId": "runs-sync-saswave-reddit-user-post-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/saswave~reddit-user-post-scraper/run-sync": { "post": { "operationId": "run-sync-saswave-reddit-user-post-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", "required": [ "users" ], "properties": { "users": { "title": "Users /user/", "type": "array", "description": "Url contains /user/", "items": { "type": "string" } }, "sort": { "title": "Posts Sort", "enum": [ "hot", "new", "top&t=day", "top&t=week", "top&t=month", "top&t=year", "top&t=all" ], "type": "string", "description": "Select feature when providing users", "default": "new" }, "feature": { "title": "Select Feature", "enum": [ "posts", "comments" ], "type": "string", "description": "Select feature when providing users", "default": "posts" } } }, "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 } } } } } } } } } } ```