Social listening & brand monitoring with UnifAPI

Build an AI agent that tracks brand mentions, sentiment, and emerging conversation across Twitter / X, Reddit, TikTok, and news — through a single API. Live use-case page: unifapi.com/solutions/social-listening.

The problem

A traditional social-listening stack stitches together a Twitter scraper, a Reddit client, a TikTok provider, and a news API — each with its own shape, auth, rate limits, and billing. The agent code becomes 60% glue and 40% product.

The UnifAPI shape

One key, four calls, one consistent JSON shape:

# Mentions on Twitter / X
curl https://api.unifapi.com/v1/twitter/search \
  -H "Authorization: Bearer $UNIFAPI_KEY" \
  -d '{"q": "\"Acme Corp\"", "since": "24h"}'

# Mentions on Reddit
curl https://api.unifapi.com/v1/reddit/search \
  -H "Authorization: Bearer $UNIFAPI_KEY" \
  -d '{"q": "Acme Corp", "subreddit": "all", "since": "24h"}'

# Discussion on TikTok
curl https://api.unifapi.com/v1/tiktok/search \
  -d '{"q": "Acme Corp"}' \
  -H "Authorization: Bearer $UNIFAPI_KEY"

# Press coverage
curl https://api.unifapi.com/v1/news/search \
  -d '{"q": "Acme Corp", "since": "24h"}' \
  -H "Authorization: Bearer $UNIFAPI_KEY"

Pipe the four streams into the agent’s reasoning loop. Same auth, same error model, same pagination.

APIs used

Typical agent loop

  1. Discover — scheduled UnifAPI calls every N minutes for the brand keywords
  2. Classify — pass results through an LLM (model routed by your LLM gateway of choice) for sentiment + topic
  3. Enrich — for high-priority hits, scrape the linked URL via URL → markdown
  4. Notify — post to Slack, file a ticket, or escalate

Why pay-per-call wins here

Social listening volume is bursty — a launch week is 100× a normal day. UnifAPI’s pay-per-call pricing lets the bill match the load. Subscription-based vendors force you to size for peak and overpay in the troughs.

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