Social listening posts

Code public posts about a topic by stance, and separate first-hand experience from repeated claims.

Run this on your file Download the template CSV

Before you run it: Replace sulfite-free wine with your topic in the on_topic and stance questions. You can edit every question in the runner.

Columns to upload

Name the columns in your CSV like this. Extra columns are fine: every column of the row is sent with the questions.

Column	What it holds
`platform`	Where it was posted
`text`	Post text
`date`	Post date

Questions

Each question adds a column to your file. The answer is one of the listed values, or Unsure when the model's confidence is below the cutoff: 70% for pick-one and scale questions, 80% for yes or no.

New column	Question	Possible answers
`on_topic`Yes or no	Is the post about sulfite-free wine?	yes The post is about sulfite-free or no-added-sulfite wine no The post is about something else
`stance`Pick one	What is the writer's position on sulfite-free wine?	for Says it is better or recommends it against Says it is worse, a fad or not worth it neutral Reports or asks without taking a side
`first_hand`Yes or no	Does the writer describe their own experience?	yes They describe something that happened to them no They repeat what others say or give an opinion
`health_claim`Yes or no	Does the post make a claim about health, headaches or allergies?	yes It makes a health claim no It makes no health claim

Example row

Column	Value
`platform`	Reddit
`date`	2026-06-14
`text`	Switched to no-sulfite wines last year and my headaches stopped. Could be placebo but I'm not going back.

Answers from a test run on this row

Column	Answer	Confidence
`on_topic`	yes	98%
`stance`	for	99%
`first_hand`	yes	98%
`health_claim`	yes	98%

More research use cases

Use case	What it does	New columns
Survey open-ended answers	Code the free-text answers of a survey into themes and sentiment, and pull out the ones that name a specific change.	`theme` `sentiment` `actionable` `off_topic`
User interview snippets	Tag quotes from user interviews by the pain they describe and how serious it is.	`pain_point` `severity` `workaround` `feature_request`
Literature review abstract screening	Screen the titles and abstracts of a literature search against your inclusion criteria before you read the full texts.	`design` `population` `outcome` `include`
News and media content analysis	Code news articles by frame and tone for a media study.	`frame` `tone` `cites_study`
App store reviews	Sort app reviews into bugs, requests and praise, and see which version the problems come from.	`category` `sentiment` `crash`