The Problem
Marketing teams manually search for brand mentions and reviews across Google, Reddit, and YouTube. Compiling sentiment and extracting actionable feedback from scattered sources takes hours of weekly effort with no standardized format.
The Scavio Solution
Build an n8n workflow that uses Scavio to search multiple platforms for brand mentions, collects review snippets, and compiles a structured weekly report with sentiment indicators and source links.
Before
Spending 4-6 hours per week manually searching Google, Reddit, and YouTube for brand mentions and reviews, then compiling findings in ad-hoc formats.
After
Automated n8n workflow runs daily, collects mentions across platforms, and delivers a structured report with sentiment tags and source URLs every Monday morning.
Who It Is For
Marketing teams using n8n for content and review automation.
Key Benefits
- Automated multi-platform review monitoring
- Structured weekly reports with sentiment indicators
- Zero manual search effort after initial setup
- Source URLs for every mention for easy follow-up
Python Example
import requests
PLATFORMS = ["google", "reddit", "youtube"]
def collect_brand_mentions(brand: str) -> dict:
all_mentions = {}
for platform in PLATFORMS:
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": SCAVIO_API_KEY, "Content-Type": "application/json"},
json={
"query": f"{brand} review feedback 2026",
"platform": platform,
"limit": 10
}
)
results = resp.json().get("results", [])
mentions = []
for r in results:
snippet = r.get("snippet", "")
sentiment = "positive" if any(w in snippet.lower() for w in ["great", "love", "best", "recommend"]) else "neutral"
if any(w in snippet.lower() for w in ["bad", "worst", "avoid", "terrible"]):
sentiment = "negative"
mentions.append({
"title": r["title"],
"url": r["link"],
"snippet": snippet,
"sentiment": sentiment
})
all_mentions[platform] = mentions
return all_mentions
report = collect_brand_mentions("Scavio")
for platform, mentions in report.items():
print(f"\n{platform.upper()} ({len(mentions)} mentions):")
for m in mentions:
print(f" [{m['sentiment']}] {m['title']}")JavaScript Example
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
fetch('https://api.scavio.dev/api/v1/search', {method: 'POST', headers: H, body: JSON.stringify({query: 'example', country_code: 'us'})}).then(r => r.json()).then(d => console.log(d.organic_results?.length + ' results'));Platforms Used
Web search with knowledge graph, PAA, and AI overviews
Community, posts & threaded comments from any subreddit
YouTube
Video search with transcripts and metadata