# X (Twitter) Viral Tweets Finder - Top Content by Topic (`seemuapps/x-viral-tweets-scraper`) Actor Find viral tweets on any topic - set minimum likes and retweets, get engagement rates and virality scores for every result. No login required. - **URL**: https://apify.com/seemuapps/x-viral-tweets-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Social media, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 viral tweet scrapeds 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## X (Twitter) Viral Tweets Finder - Top Content by Topic Find the viral tweets on any topic — set a minimum likes/retweets threshold, pick a time window, and get every matching tweet with engagement rates and virality scores. No login, no cookies. ### What you get For every viral tweet: - **Tweet fields** — tweet ID, URL, full text, creation date, and language - **Engagement metrics** — likes, retweets, replies, quotes, views, and bookmarks - **Computed scores**: - `totalEngagement` — likes + retweets + replies + quotes + bookmarks - `engagementRatePct` — engagement as a percentage of views (how strongly viewers reacted) - `viralityMultiplier` — views ÷ author's follower count (above 1 means the tweet reached far beyond its author's audience) - **Author info** — username, display name, blue-verified status, follower count, and profile picture - Export to JSON, CSV, Excel, or Google Sheets directly from the Apify console ### Use cases - **Content research** — see exactly what goes viral in your niche and reverse-engineer the hooks - **Trend detection** — catch breaking topics while they're still climbing - **Influencer discovery** — find creators whose content consistently outperforms their follower count - **Social listening** — track high-impact conversations about your brand, product, or market - **Newsletter & curation** — pull the week's top tweets on a topic in one run ### How to use 1. Enter a **Topic** — a keyword, phrase, `#hashtag`, or `$cashtag` 2. Set **Minimum Likes** (default 500) and optionally **Minimum Retweets** 3. Choose **Days Back** (default 7 — last week) and an optional **Language** 4. Run the actor — results appear in the **Dataset** tab, highest-engagement first #### Fetching more (pagination) After a run finishes, open the **Key-value store** tab → copy the `NEXT_PAGE_ID` value → paste it into **Page ID** on your next run. If `NEXT_PAGE_ID` is `null`, you've fetched everything. ### Output format Each dataset record: ```json { "topic": "artificial intelligence", "tweetId": "1866332309399781537", "url": "https://x.com/user/status/1866332309399781537", "text": "AI just did something wild…", "createdAt": "Tue Dec 10 03:14:59 +0000 2024", "lang": "en", "likeCount": 15230, "retweetCount": 2140, "replyCount": 890, "quoteCount": 312, "viewCount": 1250000, "bookmarkCount": 4100, "totalEngagement": 22672, "engagementRatePct": 1.81, "viralityMultiplier": 27.5, "authorUsername": "example", "authorName": "Example User", "authorIsBlueVerified": true, "authorFollowersCount": 45400, "authorProfilePicture": "https://pbs.twimg.com/profile_images/…" } ```` ### FAQ **Do I need X login credentials?** No. Enter a topic and run. **How is this different from a normal tweet search?** It pre-filters by engagement thresholds and time window, sorts by engagement, and adds computed virality metrics — so you get only the tweets that actually performed, ranked and scored. **Can I use advanced query syntax in the Topic field?** Yes — exact phrases in quotes, `OR` between alternatives, and `#hashtags`/`$cashtags` all work. The engagement, date, and language filters are added automatically. # Actor input Schema ## `topic` (type: `string`): Keyword, phrase, #hashtag, or $cashtag to find viral tweets about. Supports quotes for exact phrases and OR for alternatives. ## `minLikes` (type: `integer`): Only return tweets with at least this many likes. Set 0 to disable the likes filter. ## `minRetweets` (type: `integer`): Only return tweets with at least this many retweets. Set 0 to disable the retweets filter. ## `daysBack` (type: `integer`): Only return tweets posted within the last N days. Set 0 for all time. ## `language` (type: `string`): Optional ISO language code filter, e.g. en, es, ja. Leave empty for all languages. ## `excludeReplies` (type: `boolean`): Skip reply tweets and only return original posts. ## `onlyWithMedia` (type: `boolean`): Only return tweets containing an image or video. ## `sortBy` (type: `string`): Top returns the highest-engagement tweets first. Latest returns the most recent matching tweets first. ## `maxItems` (type: `integer`): Maximum tweets to return. Set 0 for unlimited (auto-paginates). ## `pageId` (type: `string`): Optional. Paste NEXT\_PAGE\_ID from a previous run's Key-value store to fetch the next page. ## Actor input object example ```json { "topic": "artificial intelligence", "minLikes": 500, "minRetweets": 0, "daysBack": 7, "excludeReplies": true, "onlyWithMedia": false, "sortBy": "Top", "maxItems": 100 } ``` # Actor output Schema ## `results` (type: `string`): One row per tweet: text, url, createdAt, likes/retweets/replies/quotes/views/bookmarks, totalEngagement, engagementRatePct, viralityMultiplier, and author info. ## `nextPageId` (type: `string`): NEXT\_PAGE\_ID record in the default key-value store. Paste into Page ID on the next run to resume; null when results are exhausted. # 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 = { "topic": "artificial intelligence" }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/x-viral-tweets-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 = { "topic": "artificial intelligence" } # Run the Actor and wait for it to finish run = client.actor("seemuapps/x-viral-tweets-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 '{ "topic": "artificial intelligence" }' | apify call seemuapps/x-viral-tweets-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/x-viral-tweets-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X (Twitter) Viral Tweets Finder - Top Content by Topic", "description": "Find viral tweets on any topic - set minimum likes and retweets, get engagement rates and virality scores for every result. No login required.", "version": "1.0", "x-build-id": "sjXvfJqjvF7n5eBc4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~x-viral-tweets-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-x-viral-tweets-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~x-viral-tweets-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-x-viral-tweets-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~x-viral-tweets-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-x-viral-tweets-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "topic" ], "properties": { "topic": { "title": "Topic", "type": "string", "description": "Keyword, phrase, #hashtag, or $cashtag to find viral tweets about. Supports quotes for exact phrases and OR for alternatives." }, "minLikes": { "title": "Minimum Likes", "minimum": 0, "type": "integer", "description": "Only return tweets with at least this many likes. Set 0 to disable the likes filter.", "default": 500 }, "minRetweets": { "title": "Minimum Retweets", "minimum": 0, "type": "integer", "description": "Only return tweets with at least this many retweets. Set 0 to disable the retweets filter.", "default": 0 }, "daysBack": { "title": "Days Back", "minimum": 0, "type": "integer", "description": "Only return tweets posted within the last N days. Set 0 for all time.", "default": 7 }, "language": { "title": "Language", "type": "string", "description": "Optional ISO language code filter, e.g. en, es, ja. Leave empty for all languages." }, "excludeReplies": { "title": "Exclude Replies", "type": "boolean", "description": "Skip reply tweets and only return original posts.", "default": true }, "onlyWithMedia": { "title": "Only Tweets with Media", "type": "boolean", "description": "Only return tweets containing an image or video.", "default": false }, "sortBy": { "title": "Sort Order", "enum": [ "Top", "Latest" ], "type": "string", "description": "Top returns the highest-engagement tweets first. Latest returns the most recent matching tweets first.", "default": "Top" }, "maxItems": { "title": "Max Tweets", "minimum": 0, "type": "integer", "description": "Maximum tweets to return. Set 0 for unlimited (auto-paginates).", "default": 100 }, "pageId": { "title": "Page ID (pagination)", "type": "string", "description": "Optional. Paste NEXT_PAGE_ID from a previous run's Key-value store to fetch the next page." } } }, "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 } } } } } } } } } } ```