---
title: "Separate social signals from product facts"
url: https://www.datawebot.com/blog/grok-ai-social-commerce-monitoring
description: "A manual handoff recipe to adapt in your own environment. datawebot's product records do not include a live social feed or engagement metrics."
updated: 2026-09-14
author: datawebot team
---

Extraction guide

# Separate social signals from product facts

A manual handoff recipe to adapt in your own environment.

Updated September 14, 2026 · datawebot team

[← All guides](https://www.datawebot.com/blog)Field map ↓Try the workflow ↗

01 / INPUT**Authorized social dataset + product observations**

02 / YOUR WORKFLOW**Map fields → inspect → hand off**

03 / OUTPUT**Two attributable evidence streams**

## Keep the right fields.

Manual data handoff

| Field / step | Input | Rule |
| --- | --- | --- |
| Source record | Product and variant identifiers | Keep identity as strings. |
| Join | Verified product reference | A mention does not establish a sale. |
| Evidence | source_url + retrieved_at | Keep the original observation attached. |

## Before you use the output

datawebot's product records do not include a live social feed or engagement metrics.

## Check one record first.

Start with the smallest useful scope. Compare the returned record with its source, confirm variant identity and inspect missing fields before applying the mapping to a larger dataset.

Keep extraction warnings and the retrieval timestamp with downstream output. When a required field is missing, leave it unresolved or return to the source; do not fill it from an assumption.

Working references [Product and variant schema ↗](https://www.datawebot.com/docs/data) · [Supported endpoints ↗](https://www.datawebot.com/scraper) · [Incomplete or unavailable results ↗](https://www.datawebot.com/docs/troubleshooting)

[Continue this workflow](https://www.datawebot.com/market/social-commerce)