Track Google search trends by querying the same keywords daily, measuring result freshness and content shifts, and detecting when new topics start appearing consistently across search results. Google Trends provides aggregate interest data, but it does not show you the actual content ranking for a topic. By tracking search results directly, you can see which pages are rising, which topics are generating new content, and how the SERP composition changes over time, giving you actionable content intelligence.
Prerequisites
- Python 3.8+ installed
- requests library installed
- A Scavio API key from scavio.dev
- A set of keywords to track
Walkthrough
Step 1: Define tracked keywords
Set up the keywords and categories you want to monitor for trending signals.
Python
import os, requests, json, datetime, hashlib
API_KEY = os.environ['SCAVIO_API_KEY']
TRACKED = [
{'keyword': 'ai agent framework', 'category': 'tech'},
{'keyword': 'remote work tools', 'category': 'productivity'},
{'keyword': 'search api', 'category': 'tech'},
{'keyword': 'cold email tools', 'category': 'sales'},
]
TRENDS_FILE = 'trends_history.json'
print(f'Tracking {len(TRACKED)} keywords for trends')Step 2: Capture daily SERP snapshot
Search each keyword and store a snapshot of the results for comparison.
Python
def capture_snapshot(keyword: str) -> dict:
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': API_KEY},
json={'platform': 'google', 'query': keyword}, timeout=15)
data = resp.json()
results = data.get('organic_results', [])
return {
'keyword': keyword,
'date': datetime.date.today().isoformat(),
'result_count': len(results),
'top_5_titles': [r.get('title', '') for r in results[:5]],
'top_5_urls': [r.get('link', '') for r in results[:5]],
'content_hash': hashlib.md5(json.dumps([r.get('title', '') for r in results[:5]]).encode()).hexdigest(),
'has_featured_snippet': 'featured_snippet' in data or 'answer_box' in data,
'paa_count': len(data.get('people_also_ask', [])),
}
snap = capture_snapshot('ai agent framework')
print(f"{snap['keyword']}: {snap['result_count']} results, hash={snap['content_hash'][:8]}")Step 3: Detect content velocity
Measure how fast the search results are changing to identify trending topics.
Python
def load_history() -> list:
try:
with open(TRENDS_FILE) as f:
return json.load(f)
except FileNotFoundError:
return []
def save_history(history: list):
with open(TRENDS_FILE, 'w') as f:
json.dump(history, f, indent=2)
def content_velocity(keyword: str, history: list) -> dict:
entries = [h for h in history if h['keyword'] == keyword]
entries.sort(key=lambda x: x['date'])
if len(entries) < 2:
return {'velocity': 'unknown', 'changes': 0}
recent = entries[-5:] if len(entries) >= 5 else entries
changes = 0
for i in range(1, len(recent)):
if recent[i]['content_hash'] != recent[i-1]['content_hash']:
changes += 1
velocity = changes / (len(recent) - 1)
return {
'velocity': 'high' if velocity > 0.6 else 'medium' if velocity > 0.3 else 'low',
'changes': changes,
'days_tracked': len(recent),
}
history = load_history()
vel = content_velocity('ai agent framework', history)
print(f"Velocity: {vel['velocity']} ({vel['changes']} changes)")Step 4: Run daily tracking
Execute the daily tracking routine and store results.
Python
import time
def daily_track(tracked: list) -> list:
history = load_history()
snapshots = []
for item in tracked:
snap = capture_snapshot(item['keyword'])
snap['category'] = item['category']
snapshots.append(snap)
history.append(snap)
vel = content_velocity(item['keyword'], history)
print(f" {item['keyword']}: velocity={vel['velocity']}")
time.sleep(0.3)
save_history(history)
return snapshots
snapshots = daily_track(TRACKED)Step 5: Generate trends report
Produce a report showing which topics are trending up based on content velocity and SERP changes.
Python
def trends_report(tracked: list) -> str:
history = load_history()
lines = [f'Trends Report - {datetime.date.today().isoformat()}', '']
trending = []
for item in tracked:
vel = content_velocity(item['keyword'], history)
entries = [h for h in history if h['keyword'] == item['keyword']]
latest = entries[-1] if entries else {}
status = 'TRENDING' if vel['velocity'] == 'high' else 'STABLE' if vel['velocity'] == 'low' else 'WATCH'
lines.append(f"[{status}] {item['keyword']} ({item['category']})")
lines.append(f" Velocity: {vel['velocity']}, Top: {latest.get('top_5_titles', [''])[0][:50]}")
if vel['velocity'] == 'high':
trending.append(item['keyword'])
lines.append(f'\nTrending topics: {len(trending)}')
report = '\n'.join(lines)
print(report)
return report
trends_report(TRACKED)Python Example
Python
import requests, os, hashlib, json
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def serp_hash(keyword):
data = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': 'google', 'query': keyword}).json()
titles = [r.get('title', '') for r in data.get('organic_results', [])[:5]]
return hashlib.md5(json.dumps(titles).encode()).hexdigest()[:8]
print(serp_hash('ai agent framework'))JavaScript Example
JavaScript
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function serpHash(keyword) {
const r = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: H,
body: JSON.stringify({platform: 'google', query: keyword})
});
const titles = ((await r.json()).organic_results || []).slice(0, 5).map(r => r.title);
return titles.join('|').slice(0, 40);
}
serpHash('ai agent framework').then(console.log);Expected Output
JSON
A daily trend tracking system that monitors SERP changes, measures content velocity, and identifies trending topics based on how fast search results are shifting.