# Threads Brand Monitor (`digitalnomadph/threads-brand-monitor`) Actor Monitor Threads for brand mentions, competitor posts, keyword activity, and alerts with scheduled Apify runs. - **URL**: https://apify.com/digitalnomadph/threads-brand-monitor.md - **Developed by:** [DigitalNomadPH](https://apify.com/digitalnomadph) (community) - **Categories:** Social media, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 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. 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 ## Threads Brand Monitor Track brand mentions, competitor posts, and keyword activity on Threads from scheduled Apify runs — without needing a Threads account or API access. --- ### What does Threads Brand Monitor do? Threads Brand Monitor fetches recent posts from Threads handles and keyword searches, normalises them into structured records, and emits per-group summaries and alerts. You configure one or more **watchlists** — each watchlist is a named group of handles and/or keywords — and the actor produces: - **Post records** for every matched Threads post - **Profile records** with follower counts, bios, and verification status for each monitored handle - **Summary records** per watchlist group with volume, engagement, sentiment breakdown, and change vs. the previous run - **Alert records** when thresholds are crossed (risk keyword hit, mention spike, high-engagement post) - A **run summary** with totals and timing Run it on a schedule (daily or hourly) to track how a brand, competitor, or campaign evolves on Threads over time. --- ### Why use Threads Brand Monitor? - **No Threads account required** — works with publicly accessible Threads pages - **Scheduled monitoring** — pair with Apify's built-in scheduler for daily or hourly reports - **Delta mode** — on repeat runs, only new posts since the last run are flagged `is_new_since_last_run: true`, avoiding duplicates in downstream tools - **Multi-group watchlists** — track your brand, competitors, and campaign hashtags in a single run, each with its own summary - **Alerts included** — get notified when a risk keyword appears, volume spikes, or a post goes viral - **Structured output** — clean JSON records ready for Google Sheets, BigQuery, Zapier, or any webhook --- ### How to use 1. **Open the actor** on Apify Console and click **Try for free** 2. **Configure your watchlists** — add at least one group with handles or keywords to monitor (see Input section below) 3. **Set your risk keywords** — words that trigger alert records when found in post text (e.g. `outage`, `refund`, `bug`) 4. **Enable Delta Mode** if you plan to run on a schedule — this flags only new posts on each run 5. Click **Start** — the actor runs and saves results to the default dataset 6. **Export or connect** — download as CSV/JSON from the Output tab, or connect via Apify's API or integrations **For scheduled monitoring:** - Go to Schedules → Create schedule → select this actor - Run daily at 08:00 UTC for a morning brand briefing - Enable Delta Mode so each run only surfaces new activity --- ### Input | Field | Type | Default | Description | |---|---|---|---| | `watchlists` | array | — | **Required.** One or more named groups to monitor. Each group has a `name`, `type`, and lists of `handles` and/or `keywords`. | | `maxPostsPerHandle` | integer | 25 | Max posts to collect per handle per run. Threads embeds ~4–8 posts in the initial page load. | | `maxPostsPerKeyword` | integer | 50 | Max posts to collect per keyword search. | | `includeReplies` | boolean | false | Include reply posts from handle timelines. | | `includeProfileStats` | boolean | true | Emit a profile record per handle with follower count, bio, and verification status. | | `deltaMode` | boolean | false | Only flag posts newer than the last run as `is_new_since_last_run: true`. Recommended for scheduled runs. | | `riskKeywords` | array | `["outage","refund","bug",...]` | Posts containing these words trigger `negative_post` alert records. | | `minEngagement` | integer | 0 | Skip posts with fewer total interactions (likes + replies + reposts). Applies to handle posts only; keyword results always pass through. | | `alertThresholds` | object | `{mentionSpikeMultiplier: 3, minEngagementForAlert: 100}` | Controls when `mention_spike` and `new_high_engagement_post` alerts fire. | | `proxyConfiguration` | object | Apify residential | Residential proxies are recommended for keyword search. Threads may block datacenter IPs on search routes. | #### Example input ```json { "watchlists": [ { "name": "Our Brand", "type": "brand", "keywords": ["our-product-name", "#ourhashtag"], "handles": ["our-threads-handle"] }, { "name": "Competitors", "type": "competitor", "handles": ["competitor-a", "competitor-b"], "keywords": [] }, { "name": "Campaign — Launch Week", "type": "campaign", "keywords": ["#launchweek", "product launch"], "handles": [] } ], "maxPostsPerHandle": 25, "maxPostsPerKeyword": 50, "deltaMode": true, "riskKeywords": ["outage", "bug", "refund", "broken", "cancel", "scam"], "alertThresholds": { "mentionSpikeMultiplier": 3, "minEngagementForAlert": 500 } } ```` *** ### Output All records land in the actor's default dataset. Each record has a `record_type` field to distinguish them. #### Record types | `record_type` | One row per | Key fields | |---|---|---| | `post` | Each matched Threads post | `text`, `author_handle`, `published_at`, `like_count`, `reply_count`, `repost_count`, `risk_flags`, `sentiment_direction`, `is_new_since_last_run` | | `profile` | Each monitored handle | `handle`, `display_name`, `follower_count`, `bio`, `verified_status` | | `summary` | Each watchlist group | `total_posts_seen`, `new_posts`, `top_post_url`, `risk_post_count`, `volume_change_vs_previous_run` | | `alert` | Each threshold crossed | `alert_type`, `severity`, `message`, `trigger_value`, `post_url` | | `run_summary` | Entire run | `total_posts_returned`, `new_posts_returned`, `alerts_generated`, `run_started_at`, `run_finished_at` | #### Sample post record ```json { "record_type": "post", "group_name": "Our Brand", "group_type": "brand", "match_type": "keyword", "query": "our-product-name", "platform": "threads", "post_id": "3906263078447871896", "post_url": "https://www.threads.net/@someuser/post/3906263078447871896", "author_handle": "someuser", "author_name": "Some User", "text": "Just tried our-product-name for the first time and it's great!", "published_at": "2026-05-27T11:54:28.000Z", "like_count": 42, "reply_count": 7, "repost_count": 3, "risk_flags": [], "sentiment_direction": "positive", "is_new_since_last_run": true } ``` #### Sample summary record ```json { "record_type": "summary", "group_name": "Our Brand", "group_type": "brand", "new_posts": 12, "total_posts_seen": 12, "unique_authors": 9, "total_likes": 847, "total_replies": 134, "top_post_url": "https://www.threads.net/@topuser/post/...", "risk_post_count": 1, "positive_post_count": 7, "negative_post_count": 1, "neutral_post_count": 4, "volume_change_vs_previous_run": 4, "summary_generated_at": "2026-05-27T12:03:11.000Z" } ``` #### Sample alert record ```json { "record_type": "alert", "alert_type": "negative_post", "severity": "medium", "group_name": "Our Brand", "trigger_value": 1, "threshold": 1, "post_url": "https://www.threads.net/@user/post/...", "message": "1 post(s) matched risk keywords for \"Our Brand\"", "generated_at": "2026-05-27T12:03:11.000Z" } ``` *** ### Pricing and cost estimation This actor is billed per dataset item. Results include post, profile, summary, alert, and run\_summary records. The estimates below assume pay-per-result pricing around $1 per 1,000 dataset items and typical runs where most items are post records. | Scenario | Approx. dataset items per run | Estimated cost | |---|---|---| | 1 brand + 2 competitors, 3 keywords, daily | ~50-170 items | ~$0.05-$0.17/run | | 5 watchlist groups, 10 keywords, hourly | ~300-650 items | ~$0.30-$0.65/run | | Large campaign with 20 keywords | ~500-1,050 items | ~$0.50-$1.05/run | Residential proxy usage (recommended for keyword search) adds a small additional cost per run. Enable delta mode on scheduled runs to minimise volume — each run only surfaces new activity. *** ### Tips **Recommended schedule for brand monitoring:** - Daily at 08:00 UTC — morning briefing cadence - Enable `deltaMode: true` to avoid re-processing the same posts - Set `includeProfileStats: true` to track follower count changes over time **Keyword search quality:** - Threads keyword search returns posts by relevance, not recency — results vary between runs - Common keywords return more spam; use specific brand names or product handles for cleaner signal - Hashtags (e.g. `#yourhashtag`) work as keywords **Proxy configuration:** - Residential proxies are pre-selected and recommended — Threads blocks datacenter IPs on search routes - Handle-based monitoring works without proxies; keyword search benefits most from residential IPs **Connecting results to other tools:** - Use Apify's Google Sheets integration to append each run's posts to a tracking sheet - Use the webhook trigger to POST alert records to Slack or a CRM when `severity: "high"` - Filter the dataset by `record_type: "summary"` for a quick daily briefing *** ### FAQ **Does this require a Threads login?** No. The actor accesses public Threads pages only. Private accounts and login-gated content are not accessible. **How many posts does it return per handle?** Threads embeds 4–8 posts in the initial page load per handle. Setting `maxPostsPerHandle` higher than this will not return more posts — the limit is what Threads serves in the public page, not a parameter we control. **Why are engagement metrics null for keyword search results?** Keyword search uses Playwright to render the search results page. Engagement counts (likes, replies) are not reliably extractable from the rendered DOM without brittle class selectors. Handle-based posts include full engagement metrics from Threads' embedded JSON. **What languages does it support?** All languages. Threads is a global platform and search results include posts in any language. Sentiment detection uses English keyword matching only — non-English posts will return `sentiment_direction: null`. **Does delta mode work across platform runs?** Yes. Delta state is stored in Apify Key-Value Store, which persists between runs of the same actor. Each handle and keyword feed has its own anchor (last seen post ID and timestamp). **What happens if a handle doesn't exist or is private?** The actor logs a warning and skips that handle. Other handles and keywords in the same watchlist group continue processing normally. *** ### Support Found a bug or have a feature request? Use the Issues tab on the actor page, or contact via the Apify Console support chat. # Actor input Schema ## `watchlists` (type: `array`): Groups of handles and keywords to monitor. Each group produces separate summary rows. ## `maxPostsPerKeyword` (type: `integer`): Maximum posts to collect per keyword search query. ## `maxPostsPerHandle` (type: `integer`): Maximum posts to collect from each monitored handle's timeline. ## `dateFrom` (type: `string`): Only include posts published on or after this date. Format: YYYY-MM-DD ## `includeReplies` (type: `boolean`): If enabled, replies to posts are included in the output. Increases runtime significantly. ## `includeProfileStats` (type: `boolean`): If enabled, emits a profile record with follower count and bio for each monitored handle. ## `deltaMode` (type: `boolean`): Only return posts newer than the last run. Ideal for scheduled monitoring runs. ## `riskKeywords` (type: `array`): Posts matching any of these keywords trigger alert records and are flagged in output. ## `minEngagement` (type: `integer`): Only include posts with at least this many total interactions (likes + replies + reposts). Set to 0 to include all. ## `alertThresholds` (type: `object`): Configure when alert records are emitted. ## `proxyConfiguration` (type: `object`): Residential proxies are recommended for keyword search. Threads blocks datacenter IPs on some routes. ## Actor input object example ```json { "watchlists": [ { "name": "Our Brand", "type": "brand", "keywords": [ "our-product-name" ], "handles": [ "our-threads-handle" ] }, { "name": "Competitors", "type": "competitor", "handles": [ "competitor-handle" ], "keywords": [] } ], "maxPostsPerKeyword": 50, "maxPostsPerHandle": 25, "includeReplies": false, "includeProfileStats": true, "deltaMode": false, "riskKeywords": [ "outage", "refund", "bug", "broken", "support", "scam", "cancel" ], "minEngagement": 0, "alertThresholds": { "mentionSpikeMultiplier": 3, "minEngagementForAlert": 100 }, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (type: `string`): All output records, including posts, profiles, summaries, alerts, and run summaries. # 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 = { "watchlists": [ { "name": "Our Brand", "type": "brand", "keywords": [ "our-product-name" ], "handles": [ "our-threads-handle" ] }, { "name": "Competitors", "type": "competitor", "handles": [ "competitor-handle" ], "keywords": [] } ], "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("digitalnomadph/threads-brand-monitor").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 = { "watchlists": [ { "name": "Our Brand", "type": "brand", "keywords": ["our-product-name"], "handles": ["our-threads-handle"], }, { "name": "Competitors", "type": "competitor", "handles": ["competitor-handle"], "keywords": [], }, ], "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("digitalnomadph/threads-brand-monitor").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 '{ "watchlists": [ { "name": "Our Brand", "type": "brand", "keywords": [ "our-product-name" ], "handles": [ "our-threads-handle" ] }, { "name": "Competitors", "type": "competitor", "handles": [ "competitor-handle" ], "keywords": [] } ], "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call digitalnomadph/threads-brand-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=digitalnomadph/threads-brand-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Threads Brand Monitor", "description": "Monitor Threads for brand mentions, competitor posts, keyword activity, and alerts with scheduled Apify runs.", "version": "1.0", "x-build-id": "bnEFuaaiZ4XxeXhCO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/digitalnomadph~threads-brand-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-digitalnomadph-threads-brand-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/digitalnomadph~threads-brand-monitor/runs": { "post": { "operationId": "runs-sync-digitalnomadph-threads-brand-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/digitalnomadph~threads-brand-monitor/run-sync": { "post": { "operationId": "run-sync-digitalnomadph-threads-brand-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "watchlists" ], "properties": { "watchlists": { "title": "Watchlists", "type": "array", "description": "Groups of handles and keywords to monitor. Each group produces separate summary rows." }, "maxPostsPerKeyword": { "title": "Max Posts Per Keyword", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum posts to collect per keyword search query.", "default": 50 }, "maxPostsPerHandle": { "title": "Max Posts Per Handle", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum posts to collect from each monitored handle's timeline.", "default": 25 }, "dateFrom": { "title": "Date From", "type": "string", "description": "Only include posts published on or after this date. Format: YYYY-MM-DD" }, "includeReplies": { "title": "Include Replies", "type": "boolean", "description": "If enabled, replies to posts are included in the output. Increases runtime significantly.", "default": false }, "includeProfileStats": { "title": "Include Profile Stats", "type": "boolean", "description": "If enabled, emits a profile record with follower count and bio for each monitored handle.", "default": true }, "deltaMode": { "title": "Delta Mode", "type": "boolean", "description": "Only return posts newer than the last run. Ideal for scheduled monitoring runs.", "default": false }, "riskKeywords": { "title": "Risk Keywords", "type": "array", "description": "Posts matching any of these keywords trigger alert records and are flagged in output.", "default": [ "outage", "refund", "bug", "broken", "support", "scam", "cancel" ] }, "minEngagement": { "title": "Minimum Engagement Filter", "minimum": 0, "type": "integer", "description": "Only include posts with at least this many total interactions (likes + replies + reposts). Set to 0 to include all.", "default": 0 }, "alertThresholds": { "title": "Alert Thresholds", "type": "object", "description": "Configure when alert records are emitted.", "default": { "mentionSpikeMultiplier": 3, "minEngagementForAlert": 100 } }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Residential proxies are recommended for keyword search. Threads blocks datacenter IPs on some routes." } } }, "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 } } } } } } } } } } ```