Brand mentions happen across Google, Reddit, TikTok, and YouTube simultaneously. Monitoring each platform separately is expensive and fragmented. This pipeline queries all four platforms from one API, deduplicates mentions, scores sentiment, and generates a unified daily digest. Total cost: $0.020 per brand per day.
Prerequisites
- Python 3.8+
- requests library
- A Scavio API key from scavio.dev
- Brand name and competitor names
Walkthrough
Step 1: Query all platforms for brand mentions
Search Google, Reddit, TikTok, and YouTube for your brand in one sweep.
Python
import os, requests, json
from datetime import datetime
from collections import Counter
API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
TH = {'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'}
BRAND = 'Scavio'
def search_platform(query, platform=None):
body = {'query': query, 'country_code': 'us'}
if platform:
body['platform'] = platform
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json=body).json()
return [{'title': r.get('title', ''), 'link': r.get('link', ''),
'snippet': r.get('snippet', ''), 'platform': platform or 'google'}
for r in data.get('organic_results', [])]
def search_tiktok(query):
data = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos',
headers=TH, json={'query': query}).json()
videos = data.get('videos', data.get('data', {}).get('videos', []))
return [{'title': v.get('desc', '')[:80], 'link': f'tiktok.com/@{v.get("author", {}).get("uniqueId", "")}',
'snippet': v.get('desc', ''), 'platform': 'tiktok',
'plays': v.get('stats', {}).get('playCount', 0)} for v in videos]
all_mentions = []
for platform in [None, 'reddit', 'youtube']:
mentions = search_platform(BRAND, platform)
all_mentions.extend(mentions)
print(f'{platform or "google":10}: {len(mentions)} mentions')
tiktok_mentions = search_tiktok(BRAND)
all_mentions.extend(tiktok_mentions)
print(f'{"tiktok":10}: {len(tiktok_mentions)} mentions')
print(f'\nTotal: {len(all_mentions)} mentions. Cost: $0.020')Step 2: Score sentiment and reach per platform
Classify mention sentiment and estimate reach across platforms.
Python
POSITIVE = ['best', 'great', 'love', 'recommend', 'amazing', 'excellent', 'perfect']
NEGATIVE = ['worst', 'terrible', 'avoid', 'hate', 'broken', 'expensive', 'scam']
def score_mention(mention):
text = f'{mention["title"]} {mention["snippet"]}'.lower()
pos = sum(1 for w in POSITIVE if w in text)
neg = sum(1 for w in NEGATIVE if w in text)
if pos > neg: return 'positive'
if neg > pos: return 'negative'
return 'neutral'
def platform_report(mentions):
by_platform = {}
for m in mentions:
p = m['platform']
if p not in by_platform:
by_platform[p] = []
by_platform[p].append(m)
print(f'\n=== Cross-Platform Brand Report - {BRAND} ===')
for platform, items in by_platform.items():
sentiments = Counter(score_mention(m) for m in items)
print(f'\n [{platform.upper()}] {len(items)} mentions')
print(f' Positive: {sentiments["positive"]} | Neutral: {sentiments["neutral"]} | Negative: {sentiments["negative"]}')
for item in items[:2]:
print(f' - {item["title"][:55]}')
platform_report(all_mentions)Step 3: Generate unified daily digest
Compile all platform data into a single actionable report.
Python
def daily_digest(mentions):
total = len(mentions)
sentiments = Counter(score_mention(m) for m in mentions)
platforms = Counter(m['platform'] for m in mentions)
print(f'\n=== Daily Brand Digest - {datetime.now().strftime("%Y-%m-%d")} ===')
print(f' Brand: {BRAND}')
print(f' Total mentions: {total}')
print(f' Sentiment: +{sentiments["positive"]} neutral:{sentiments["neutral"]} -{sentiments["negative"]}')
print(f' Platforms: {", ".join(f"{p}({c})" for p, c in platforms.most_common())}')
# Highlight negative mentions that need attention
negative = [m for m in mentions if score_mention(m) == 'negative']
if negative:
print(f'\n NEEDS ATTENTION ({len(negative)} negative mentions):')
for m in negative[:3]:
print(f' [{m["platform"]}] {m["title"][:50]}')
print(f' {m["link"][:60]}')
# Highlight high-reach mentions
tiktok_high = [m for m in mentions if m.get('plays', 0) > 10000]
if tiktok_high:
print(f'\n HIGH REACH TikTok ({len(tiktok_high)} videos >10K plays):')
for m in tiktok_high[:3]:
print(f' {m["plays"]:,} plays: {m["title"][:40]}')
print(f'\n Daily cost: $0.020 (4 platform searches)')
daily_digest(all_mentions)Python Example
Python
import os, requests
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
TH = {'Authorization': f'Bearer {os.environ["SCAVIO_API_KEY"]}', 'Content-Type': 'application/json'}
brand = 'Scavio'
for p in [None, 'reddit', 'youtube']:
body = {'query': brand, 'country_code': 'us'}
if p: body['platform'] = p
data = requests.post('https://api.scavio.dev/api/v1/search', headers=SH, json=body).json()
print(f'{p or "google"}: {len(data.get("organic_results", []))} mentions')
tt = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos', headers=TH, json={'query': brand}).json()
print(f'tiktok: {len(tt.get("videos", []))} mentions')
print('Cost: $0.020')JavaScript Example
JavaScript
const SH = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
for (const p of [null, 'reddit', 'youtube']) {
const body = { query: 'Scavio', country_code: 'us' };
if (p) body.platform = p;
const data = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: SH, body: JSON.stringify(body)
}).then(r => r.json());
console.log(`${p || 'google'}: ${(data.organic_results || []).length} mentions`);
}Expected Output
JSON
google : 8 mentions
reddit : 5 mentions
youtube : 4 mentions
tiktok : 6 mentions
Total: 23 mentions. Cost: $0.020
=== Daily Brand Digest - 2026-05-20 ===
Brand: Scavio
Total mentions: 23
Sentiment: +12 neutral:9 -2
Platforms: google(8), tiktok(6), reddit(5), youtube(4)
NEEDS ATTENTION (2 negative mentions):
[reddit] Scavio API returning 429 errors today
Daily cost: $0.020 (4 platform searches)