# Instagram Hashtag Discovery (`enezli/instagram-hashtag-discovery`) Actor Turn your Instagram posts (your own data or a connected IG scraper dataset) into related-hashtag discovery (co-occurrence), a competition tier (nano→mega) and a content strategy. Not a scraper — an intelligence layer, no anti-bot. - **URL**: https://apify.com/enezli/instagram-hashtag-discovery.md - **Developed by:** [Turgay NANTA](https://apify.com/enezli) (community) - **Categories:** Social media, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 hashtag analyzeds 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 ## Instagram Hashtag Discovery — Co-occurrence Discovery + Competition Tier Turns your Instagram posts (your own data or a connected IG scraper dataset) into **plannable hashtag intelligence**: the hashtags that are actually used together (related tags), a **competition tier** (nano→mega), and a concrete content strategy. **It doesn't scrape — it's an intelligence layer:** no anti-bot, no proxy, no blocking headaches. ### How it works (BYOD) This actor does not scrape Instagram. You supply the post data — paste a list of posts with captions **OR connect the dataset of any Instagram scraper actor to this input**. We do the valuable part: from those captions we **compute**, via **co-occurrence**, which hashtags work together. (Same pattern as a Maps/Lead Enricher: a post-scraper intelligence layer.) ### Why this actor 1. **Real discovery (co-occurrence)** — most tools just dump posts; we surface which tags are used together, plus a strength ratio. 2. **Competition tier + action** — provide `postCounts` and you get nano/micro/mid/macro/mega plus a concrete strategy per tier (USE / MIX / SPRINKLE …) and a relevance-ranked "add these tags first" list. 3. **No anti-bot** — because we don't scrape, we don't break when Instagram changes its layout; unbreakable + high margin. 4. **Robust normalization + dedup** — Unicode-safe (including non-Latin scripts); hidden likes handled safely; multi-seed pool merged. 5. **Honest billing** — you pay per run plus per seed hashtag actually analyzed. ### Input | Field | Description | |---|---| | `posts` | Instagram posts (with captions). Your own data or a connected IG scraper dataset. **Required.** | | `seedHashtags` | The seeds to run related-tag discovery for. If empty, the most frequent tags in the posts are auto-selected. | | `postCounts` | Seed → total post count on Instagram (optional) → the competition tier is computed from this. | | `relatedLimit` | Maximum number of related hashtags per seed. | ### Output **Dataset (one row per seed):** `hashtag`, `post_count`, `competition_tier`, `strategy {action, reason}`, `recommended_hashtags [{rank, tag, relevance, co_occurrence_ratio}]`, `related_hashtags [{tag, co_occurrence, co_occurrence_ratio}]`, `engagement {sample_size, avg_likes, avg_comments, avg_engagement}`. **Key-value store:** - `RELATED_POOL` — the merged, deduplicated related-hashtag pool across all seeds. - `RUN_SUMMARY` — `seeds_analyzed`, `input_post_count`, `seeds`, `post_counts_provided`. ### How to use 1. Collect your Instagram posts with a scraper (or paste in your own list of posts with captions). 2. Connect the output to this actor as `posts`. 3. (Optional) Set `seedHashtags` and `postCounts` → **Start**. 4. Grab the related hashtags, competition tiers, and recommendations from the Dataset tab. ### Pricing (Pay-Per-Event) You only pay for what you use: per run + per seed hashtag analyzed. No live scraping or proxy cost, so the margin is high — and there's no monthly subscription. # Actor input Schema ## `posts` (type: `array`): The Instagram posts to analyze. Each post must contain at least a 'caption' (likesCount/commentsCount are optional). Paste your own data OR connect the dataset of any Instagram scraper actor to this input. Related hashtags are COMPUTED from these captions via co-occurrence. ## `seedHashtags` (type: `array`): Which hashtags to run related-tag discovery for (with or without '#'). If left EMPTY, the most frequent tags in the posts are auto-selected. ## `postCounts` (type: `object`): Seed hashtag → total post count on Instagram (if known, e.g. {"filtercoffee": 85000}). The competition tier (nano→mega) is computed from this. If omitted, the tier is 'unknown'. ## `relatedLimit` (type: `integer`): Maximum number of related hashtags to return per seed (ranked by co-occurrence). ## Actor input object example ```json { "posts": [ { "caption": "Morning brew #filtercoffee #specialtycoffee #coffeelover #v60", "likesCount": 420 }, { "caption": "New Ethiopia roast #filtercoffee #specialtycoffee #v60", "likesCount": 680 }, { "caption": "Chemex pour-over #filtercoffee #chemex #specialtycoffee", "likesCount": 540 } ], "seedHashtags": [ "filtercoffee" ], "relatedLimit": 30 } ```` # 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 = { "posts": [ { "caption": "Morning brew #filtercoffee #specialtycoffee #coffeelover #v60", "likesCount": 420 }, { "caption": "New Ethiopia roast #filtercoffee #specialtycoffee #v60", "likesCount": 680 }, { "caption": "Chemex pour-over #filtercoffee #chemex #specialtycoffee", "likesCount": 540 } ], "seedHashtags": [ "filtercoffee" ] }; // Run the Actor and wait for it to finish const run = await client.actor("enezli/instagram-hashtag-discovery").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 = { "posts": [ { "caption": "Morning brew #filtercoffee #specialtycoffee #coffeelover #v60", "likesCount": 420, }, { "caption": "New Ethiopia roast #filtercoffee #specialtycoffee #v60", "likesCount": 680, }, { "caption": "Chemex pour-over #filtercoffee #chemex #specialtycoffee", "likesCount": 540, }, ], "seedHashtags": ["filtercoffee"], } # Run the Actor and wait for it to finish run = client.actor("enezli/instagram-hashtag-discovery").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 '{ "posts": [ { "caption": "Morning brew #filtercoffee #specialtycoffee #coffeelover #v60", "likesCount": 420 }, { "caption": "New Ethiopia roast #filtercoffee #specialtycoffee #v60", "likesCount": 680 }, { "caption": "Chemex pour-over #filtercoffee #chemex #specialtycoffee", "likesCount": 540 } ], "seedHashtags": [ "filtercoffee" ] }' | apify call enezli/instagram-hashtag-discovery --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=enezli/instagram-hashtag-discovery", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Hashtag Discovery", "description": "Turn your Instagram posts (your own data or a connected IG scraper dataset) into related-hashtag discovery (co-occurrence), a competition tier (nano→mega) and a content strategy. Not a scraper — an intelligence layer, no anti-bot.", "version": "0.1", "x-build-id": "tSW0ovTAholTMyLgk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/enezli~instagram-hashtag-discovery/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-enezli-instagram-hashtag-discovery", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/enezli~instagram-hashtag-discovery/runs": { "post": { "operationId": "runs-sync-enezli-instagram-hashtag-discovery", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/enezli~instagram-hashtag-discovery/run-sync": { "post": { "operationId": "run-sync-enezli-instagram-hashtag-discovery", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "posts" ], "properties": { "posts": { "title": "Instagram posts (with captions)", "type": "array", "description": "The Instagram posts to analyze. Each post must contain at least a 'caption' (likesCount/commentsCount are optional). Paste your own data OR connect the dataset of any Instagram scraper actor to this input. Related hashtags are COMPUTED from these captions via co-occurrence." }, "seedHashtags": { "title": "Seed hashtags (optional)", "type": "array", "description": "Which hashtags to run related-tag discovery for (with or without '#'). If left EMPTY, the most frequent tags in the posts are auto-selected.", "items": { "type": "string" } }, "postCounts": { "title": "Post counts (optional)", "type": "object", "description": "Seed hashtag → total post count on Instagram (if known, e.g. {\"filtercoffee\": 85000}). The competition tier (nano→mega) is computed from this. If omitted, the tier is 'unknown'." }, "relatedLimit": { "title": "Number of related hashtags", "minimum": 1, "type": "integer", "description": "Maximum number of related hashtags to return per seed (ranked by co-occurrence).", "default": 30 } } }, "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 } } } } } } } } } } ```