Block 1 - If Tweet
- Type / Role
- n8n-nodes-base.if - if
- Config choices
- Version 2.2
This workflow is provided as-is. Please review and test before using in production.
This workflow automates brand monitoring on X by analyzing both the text and the images in posts. It uses multi modal AI to score brand relevance, filters out noise, logs important mentions in Airt...
n8n-nodes-base.if, n8n-nodes-base.set, n8n-nodes-base.stickynote, n8n-nodes-base.splitinbatches, n8n-nodes-base.airtable, @n8n/n8n-nodes-langchain.outputparserstructured, n8n-nodes-base.switch, n8n-nodes-base.scheduletrigger
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Atta.
Original n8n.io sourceThis workflow automates brand monitoring on X by analyzing both the text and the images in posts. It uses multi-modal AI to score brand relevance, filters out noise, logs important mentions in Airtable, and sends real-time alerts to a Telegram group for high-priority posts.
Traditional brand monitoring tools often miss the most authentic user content because they only track text. They can't "see" your logo in a photo or your product featured in a video without a direct keyword mention.
This workflow acts as an AI agent that overcomes this blind spot. It finds mentions of your brand on X and then uses Google Gemini's multi-modal capabilities to perform a comprehensive analysis of both the text and any attached images. This allows it to understand the full context of a mention, score its relevance to your brand, and take the appropriate action, creating a powerful "visual intelligence" system.
The workflow runs on a schedule to find, analyze, and triage brand mentions.
Tesla OR $TSLA). It then filters these results to find unique mentions not already processed.To get this workflow running, you will need to configure your Airtable base and provide credentials for Apify, Google, and Telegram.
Of course. Based on the Config node parameters you provided, the setup process is much more centralized. Here is the corrected and rewritten "Step-by-Step Configuration" section.
Of course. Here is the rewritten "Step-by-Step Configuration" section with the link to the advanced search documentation.
Set up Your Airtable Base: Before configuring the workflow, create a new table in your Airtable base. For the workflow to function correctly, this table must contain fields to store the analysis results. Create fields with the following names: postId, postURL, postText, postDateCreated, authorUsername, authorName, sentiment, relevanceScore, relevanceReasoning, mediaPhotosAnalysis, and status. Once the table is created, have your Base ID and Table ID ready to use in the Config node.
Edit the Config Node: The majority of the setup is handled in the first Config node. Click on it and edit the following parameters in the "Expressions" tab:
searchTerms: Replace the example with the keywords, hashtags, and accounts you want to monitor. The field supports advanced search operators for complex queries. For a full list of available parameters, see the Twitter Advanced Search documentation.airtableBaseId: Paste your Airtable Base ID here.airtableTableId: Paste your Airtable Table ID here.lang: Set the two-letter language code for the posts you want to find (e.g., "en" for English).min_faves: Set the minimum number of "favorites" a post should have to be considered.tweetsToScrape: Define the maximum number of posts the scraper should find in each run.actorId: This is the specific Apify actor for scraping X. You can leave this as is unless you intend to use a different one.Configure the Telegram Node: In the final node, "Send High Relevance Posts to Monitoring Group", you need to manually set the destination for the alerts.
This workflow is a powerful framework that can be adapted for various monitoring needs.
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
| Workflow | Monitor brand mentions on X with Gemini AI visual analysis & Telegram alerts |
|---|---|
| Complexity | advanced |
| Nodes | 24 |
| Categories | Market Research, Multimodal AI |
| Author | Atta |
| Published | 07 Sept 2025 |
Use the JSON export at /data/workflows/8355/8355.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
This workflow automates brand monitoring on X by analyzing both the text and the images in posts. It uses multi modal AI to score brand relevance, filters out noise, logs important mentions in Airt...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Market Research, Multimodal AI use case.