Marketing & Growth

How to monitor brand mentions across social with AI

Marketing & Growth3 AI tools6 steps6 friction points

Want an agent to handle this on your live data? See the Starch version →

Monitoring brand mentions across social media means knowing when someone talks about your company on X, Reddit, LinkedIn, or anywhere else — before it becomes a customer complaint you missed, a partnership you never followed up on, or a PR moment you let slip by. For most operators running small teams, this ends up being either a tab nobody remembers to check or a paid tool that costs more than the signal is worth.

The workflow feels like a natural AI job because the underlying task is essentially reading and classifying text at scale. Someone tweets about your product — is it praise, a complaint, a question, or noise? That's exactly what language models are good at. Operators reasonably assume they can point an AI at a firehose of social data and get back a clean, prioritized feed of things that actually matter.

ChatGPT, Claude, and Gemini can genuinely help with parts of this workflow — particularly classifying mentions, drafting response templates, and summarizing sentiment trends once you have the raw data in hand. What they can't do on their own is fetch that data, run continuously, or surface new mentions tomorrow without you starting the whole process again from scratch.

Skip the manual work → See the Starch version

Marketing & Growth3 AI tools6 steps6 friction points

AI walkthrough

How to do it with AI today

A practical walkthrough using ChatGPT, Claude, and other off-the-shelf LLMs — what they're good at, what you'll have to do by hand.

Tools that work for this

ChatGPTClaudeGemini

Step-by-step

1 Manually collect brand mentions by searching X, Reddit, and LinkedIn for your brand name, product name, and common misspellings. Export or copy the results — X's search export is limited, so plan to do this by hand or via a browser-based scrape.

2 Open ChatGPT or Claude and paste the raw mentions text into a new conversation. Include context about your brand — what you do, who your customers are, and what kinds of mentions actually matter to you.

3 Prompt the model to classify each mention by sentiment (positive, negative, neutral), urgency (requires response today, can wait, no action needed), and mention type (complaint, question, praise, mention without direct address).

4 Ask the model to pull out the highest-priority mentions and draft a short response for each one that requires a reply — include your brand voice guidelines in the prompt so the tone stays consistent.

5 Prompt the model to summarize the week's mentions: top themes, sentiment breakdown, any spikes worth investigating, and names of accounts with meaningful follower counts who engaged with your brand.

6 Copy the summary and any drafted responses into your comms tool or doc of choice. Schedule time next week to repeat the entire process — none of this persists or runs again on its own.

Prompts you can copy

Here are 47 tweets mentioning [Brand Name] from the past 7 days. Classify each one as positive, negative, or neutral, flag any that need a response within 24 hours, and list the top 3 themes you see.

Draft a friendly, non-defensive reply to this complaint tweet about our onboarding experience. Keep it under 280 characters. Our tone is direct and human, not corporate.

Based on these Reddit comments mentioning [Brand Name], summarize what users seem to think we're best at, where they're frustrated, and whether sentiment this week looks better or worse than the batch I showed you last week.

Here are 30 LinkedIn comments and posts mentioning our company. Identify anyone who seems like a potential customer or partner, and flag anyone with a large audience who said something negative we should address.

Create a weekly brand mention report template I can fill in each Monday — include sections for sentiment trend, top complaints, top praise, accounts worth engaging, and one recommended action for the week.

Reality check

Where this gets hard

The walkthrough above works — until your numbers change, the LLM hallucinates, or you have to re-paste everything next month.

No live connection to X, Reddit, or LinkedIn — every session starts with you manually copying mentions, which means gaps any week you're too busy to do it.

LLMs have no memory of last week's mentions, so trend analysis ('is sentiment improving?') requires you to re-paste historical data every single time.

Mention volume fluctuates wildly — a spike day with 200+ posts will hit context limits in most models, forcing you to chunk inputs and re-run the prompt multiple times.

Sentiment classifications drift between sessions; the model that called something 'urgent' last Tuesday may categorize the same type of post as 'neutral' this Tuesday with no explanation.

Nothing triggers automatically — if a critical complaint hits X at 11pm on a Friday, you won't see it until you manually run the process again, likely Monday.

Drafted responses live in a chat window with no connection to your actual posting workflow — you still copy, paste, and post everything by hand.

Tired of the friction?

Starch runs the whole workflow on live data — no copy-paste, no hallucinated numbers, no re-prompting next month.

See the Starch version →

Starch alternative

The same workflow on Starch

Starch is an agentic operating system — instead of you manually running prompts against a paste of mentions, an agent builds a persistent app that tracks your brand across social continuously, surfaces what matters, and connects to the rest of your stack.

The X Mentions Tracker starter app monitors your brand handle and keywords on X daily through browser automation — no X API key required. Mentions are logged automatically, so you're not starting from a blank page each week.

Connect Gmail or Slack from Starch's integration catalog and the agent can route high-priority mentions — negative sentiment, high-follower accounts, urgent questions — directly to your inbox or a dedicated channel without you checking a dashboard.

Describe the monitoring app you actually want in plain English: 'Track mentions of [Brand] on X and Reddit daily, classify by sentiment, flag anything from accounts with more than 5,000 followers, and send me a Monday morning digest.' Starch builds that app and runs it on a schedule.

The Growth Analyst starter app connects to PostHog and emails you a weekly digest covering what's driving traffic and conversions — pair it with your mentions tracker to see whether a spike in brand conversation actually moved signups that week.

Because Starch stores logged mentions in its database rather than a chat window, you can query trends over time — 'how did sentiment compare the week before and after our last product launch' — without re-pasting historical data.

Get closed-beta access →

Toolkit