Create a cross-channel brand-mention review queue
Collect permitted public mentions, remove repeats, and use an explicit relevance and sentiment rubric to surface items that need communications review.
How this works
Collect permitted public mentions, remove repeats, and use an explicit relevance and sentiment rubric to surface items that need communications review. Brand monitoring becomes actionable when relevance and urgency are separated from sentiment. A negative joke, a genuine service complaint, and a factual media error may all need different handling even if a sentiment classifier assigns the same label.
Collect permitted public mentions, resolve entity matches, and deduplicate repeated material before applying the team's response rubric. The assistant proposes relevance, topic, and urgency with evidence. Communications owners decide whether and how to respond.
Before you start
Entity definitions
List brand names, aliases, product terms, and common false matches.
Review policy
Define what constitutes a complaint, correction, opportunity, or urgent escalation.
Collection access
Choose public sources or approved exports and record coverage limits.
Prepare the inputs
- mention_records
- Required
Posts with stable IDs
Keep text, context, source, and capture date.
- entity_aliases
- Required
Names and exclusion examples
Disambiguate common words and similarly named entities.
- review_rubric
- Required
Labels and routing rules
Separate sentiment from urgency.
- prior_mentions
- Required
Existing IDs
Prevent duplicate review tasks.
- ownership_map
- Required
Topic-to-owner mapping
Provide a fallback queue for unknown topics.
Implementation, step by step
Collect and preserve context
- Retrieve permitted mentions within the selected window.
- Include the surrounding thread or article passage needed to interpret the mention.
Checkpoint: A dated mention inventory.
Resolve actual relevance
- Match the mention to the intended brand or product using contextual evidence.
- Keep uncertain entity matches out of confident volume metrics.
Checkpoint: A relevance-reviewed set.
Deduplicate coverage
- Group reposts and copied articles while retaining meaningful new commentary.
- Link each group to its original and avoid issuing repeated tasks for the same issue.
Checkpoint: A canonical mention queue.
Apply the response rubric
- Classify topic, stated sentiment, and possible urgency independently.
- Explain the passage that triggered an escalation rather than infer the author's intent.
Checkpoint: A reasoned triage record.
Route for human review
- Assign the appropriate communications or service owner and prepare a concise context note.
- Draft a response only if requested; do not promise remedies on the brand's behalf.
Checkpoint: An owned review task.
Close the learning loop
- Record the owner's decision and any verified resolution.
- Use recurring false matches to refine aliases and examples without rewriting historical evidence.
Checkpoint: A maintained mention-review history.
Decisions along the way
The name refers to another entity
Exclude with a documented reason.
A service issue is urgent but politely worded
Route by the issue rule, not positive sentiment.
A post is ambiguous satire
Mark interpretation uncertain and request contextual review.
What you will produce
brand-mention-queue.csv
CSVSupports practical triage.
Include: mention_id · entity_match · topic · sentiment · urgency · evidence · owner · status
mention-digest.md
MarkdownSummarizes verified patterns for the team.
Include: new_issues · recurring_topics · coverage_gaps · resolutions
Check the result
- Review false-positive entity matches.
- Confirm duplicate coverage does not inflate incident counts.
- Check every escalation cites a concrete passage.
- Ensure sentiment labels do not replace the response policy.
When something goes wrong
Source access stops
Mark the gap and continue only with verified sources.
A duplicate creates repeated tasks
Merge task references while preserving the audit history.
A draft invents a refund or commitment
Remove the promise and route to an authorized owner.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Triage mentions of a booking product.
Inputs
Synthetic example. Synthetic M1 says the product's name in an unrelated context; M2 politely reports a confirmed booking disappearing; M3 reposts M2.
Expected result
Synthetic expected result. Exclude M1, route M2 as a service issue, and link M3 as repeated coverage. Do not count three incidents.
Run a small pilot
Start small
A limited source set with known false matches and repeated mentions.
Record the baseline
Record review time and missed or unnecessary escalations.
Agree success criteria
Agree relevance precision and useful routing criteria.
Stop or revise
Stop automatic routing if entity ambiguity overwhelms the queue.
Keep the scope clear
- Monitoring does not authorize replies or access to private discussions.
- Retain only participant details necessary for the authorized review.
Check before using
Review sarcasm, ambiguous names, and false matches. Use permitted access methods and do not equate negative sentiment with a verified incident.
Business activities
- Create and publish content
- Research customers and markets
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: Brand terms and aliases · Approved sources · Relevance and escalation rules
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow