social-media-trends-research

name

social-media-trends-research

description

Programmatic social media and marketing research using free tools: pytrends (Google Trends), yars (Reddit without API keys), and Perplexity MCP (Twitter/TikTok/Web). Use when finding trending topics in a niche, tracking keyword velocity and volume, monitoring Reddit discussions, discovering what's going viral, or researching content opportunities before writing. Zero-cost research stack with built-in rate limiting. Complements content-marketing-social-listening skill with executable code.

Social Media Trends Research

Overview

Programmatic trend research using three free tools:

• pytrends: Google Trends data (velocity, volume, related queries)
• yars: Reddit scraping without API keys
• Perplexity MCP: Twitter/TikTok/Web trends (via Claude's built-in MCP)

This skill provides executable code for trend research. Use alongside content-marketing-social-listening for strategy and perplexity-search for deep queries.

Quick Setup

# Install dependencies (one-time)
pip install pytrends requests --break-system-packages

No API keys required. Reddit scraping uses public .json endpoints.

---

Tool 1: pytrends (Google Trends)

What It Provides

• Real-time trending searches by country
• Interest over time for keywords
• Related queries (rising = velocity indicators)
• Interest by region
• Related topics

Basic Usage

from pytrends.request import TrendReq
import time

# Initialize (no API key needed)
pytrends = TrendReq(hl='en-US', tz=330)  # tz=330 for India (IST)

# Get real-time trending searches
trending = pytrends.trending_searches(pn='india')
print(trending.head(20))

Research Your Niche Keywords

from pytrends.request import TrendReq
import time

pytrends = TrendReq(hl='en-US', tz=330)

# Define your niche keywords (max 5 per request)
keywords = ['heart health', 'cardiology', 'cholesterol']

# Build payload
pytrends.build_payload(keywords, timeframe='now 7-d', geo='IN')

# Get interest over time
interest = pytrends.interest_over_time()
print(interest)

# CRITICAL: Wait between requests to avoid rate limiting
time.sleep(3)

# Get related queries (THIS IS GOLD - shows rising topics)
related = pytrends.related_queries()
for kw in keywords:
    print(f"\n=== Rising queries for '{kw}' ===")
    rising = related[kw]['rising']
    if rising is not None:
        print(rising.head(10))

Find Viral/Breakout Topics

from pytrends.request import TrendReq
import time

pytrends = TrendReq(hl='en-US', tz=330)

def find_breakout_topics(keyword, geo=''):
    """Find topics with explosive growth (potential viral content)"""
    pytrends.build_payload([keyword], timeframe='today 3-m', geo=geo)
    time.sleep(3)  # Rate limiting
    
    related = pytrends.related_queries()
    rising = related[keyword]['rising']
    
    if rising is not None:
        # Filter for breakout topics (marked as "Breakout" or very high %)
        breakouts = rising[rising['value'] >= 1000]  # 1000%+ growth
        return breakouts
    return None

# Example usage
breakouts = find_breakout_topics('heart health', geo='IN')
print(breakouts)

Rate Limiting Rules for pytrends

import time

# SAFE: 1 request per 3-5 seconds for casual use
time.sleep(5)

# BULK RESEARCH: 1 request per 60 seconds
time.sleep(60)

# If you get rate limited (429 error): Wait 60-120 seconds, then continue
# If persistent issues: Wait 4-6 hours before resuming

Useful Timeframes

Timeframe	Use Case
'now 1-H'	Last hour (real-time spikes)
'now 4-H'	Last 4 hours
'now 1-d'	Last 24 hours
'now 7-d'	Last 7 days (best for trends)
'today 1-m'	Last 30 days
'today 3-m'	Last 90 days (velocity analysis)
'today 12-m'	Last year (seasonal patterns)

---

Tool 2: Reddit (No API Keys - Public JSON Endpoints)

What It Provides

• Search Reddit for any keyword
• Get hot/top/rising posts from subreddits
• Post engagement data (upvotes, comments)
• No authentication required

Basic Usage

import requests
import time

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}

# Search Reddit for your niche
url = "https://www.reddit.com/search.json?q=heart+health&limit=10&sort=relevance&t=week"
response = requests.get(url, headers=headers, timeout=10)
data = response.json()

# Display results
for child in data.get('data', {}).get('children', []):
    post = child.get('data', {})
    print(f"Title: {post.get('title')}")
    print(f"Subreddit: r/{post.get('subreddit')}")
    print(f"Score: {post.get('score')}")
    print("---")

Get Hot Posts from Specific Subreddits

import requests
import time

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}

# Define subreddits relevant to your niche
subreddits = ['cardiology', 'health', 'medicine']

for sub in subreddits:
    print(f"\n=== Hot in r/{sub} ===")
    try:
        url = f"https://www.reddit.com/r/{sub}/hot.json?limit=10"
        response = requests.get(url, headers=headers, timeout=10)
        data = response.json()
        
        for child in data.get('data', {}).get('children', [])[:5]:
            post = child.get('data', {})
            print(f"- [{post.get('score')}] {post.get('title')[:60]}...")
    except Exception as e:
        print(f"Error: {e}")
    
    time.sleep(3)  # Rate limiting between requests

Using the Bundled Reddit Scraper

A helper class is included in scripts/reddit_scraper.py:

from scripts.reddit_scraper import SimpleRedditScraper

scraper = SimpleRedditScraper()

# Search
results = scraper.search("heart health tips", limit=20)
for post in results['posts']:
    print(f"[{post['score']}] r/{post['subreddit']}: {post['title']}")

# Get subreddit hot posts
results = scraper.get_subreddit("health", sort="hot", limit=10)
for post in results['posts']:
    print(f"[{post['score']}] {post['title']}")

Rate Limiting Rules for Reddit

import time

# SAFE: 1 request per 2-3 seconds
time.sleep(3)

# If you get 429 errors: Wait 5-10 minutes
# Never do more than 60 requests per hour

---

Tool 3: Perplexity MCP (Twitter/TikTok/Web)

Use Claude's built-in Perplexity MCP for platforms you can't scrape directly.

Query Templates for Trend Research

Twitter/X Trends:

"What are the most discussed [YOUR NICHE] topics on Twitter/X this week? 
Include specific examples of viral tweets and their engagement."

TikTok Trends (works from India):

"What [YOUR NICHE] content is trending on TikTok right now? 
Include hashtags, view counts, and content formats that are working."

YouTube Trends:

"What [YOUR NICHE] videos are getting the most views on YouTube this week? 
Include channel names, view counts, and video topics."

LinkedIn Professional:

"What [YOUR NICHE] topics are professionals discussing on LinkedIn this week? 
Include examples of high-engagement posts."

General Viral Content:

"What [YOUR NICHE] content has gone viral across social media in the past 7 days? 
Include platform, format, and why it resonated."

Using Perplexity with perplexity-search Skill

If you have the perplexity-search skill installed:

python scripts/perplexity_search.py \
  "What cardiology topics are trending on Twitter and TikTok this week? Include specific viral posts and hashtags." \
  --model sonar-pro

---

Combined Research Workflow

Complete Trend Research Function

from pytrends.request import TrendReq
import requests
import time
import json
from datetime import datetime

class TrendResearcher:
    def __init__(self):
        self.pytrends = TrendReq(hl='en-US', tz=330)
        self.reddit_headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
        }
    
    def _reddit_request(self, url):
        """Make a Reddit API request."""
        try:
            response = requests.get(url, headers=self.reddit_headers, timeout=10)
            response.raise_for_status()
            return response.json()
        except Exception as e:
            return {'error': str(e)}
    
    def research_niche(self, keywords, subreddits=None, geo='IN'):
        """
        Complete trend research for a niche.
        
        Args:
            keywords: List of keywords (max 5)
            subreddits: List of subreddit names to monitor
            geo: Geographic region code
        
        Returns:
            Dictionary with all research data
        """
        results = {
            'timestamp': datetime.now().isoformat(),
            'keywords': keywords,
            'google_trends': {},
            'reddit': {},
            'recommendations': []
        }
        
        # 1. Google Trends - Interest Over Time
        print("📊 Fetching Google Trends data...")
        try:
            self.pytrends.build_payload(keywords[:5], timeframe='now 7-d', geo=geo)
            results['google_trends']['interest'] = self.pytrends.interest_over_time().to_dict()
            time.sleep(5)
            
            # Related queries (rising topics)
            related = self.pytrends.related_queries()
            results['google_trends']['rising_queries'] = {}
            for kw in keywords[:5]:
                rising = related[kw]['rising']
                if rising is not None:
                    results['google_trends']['rising_queries'][kw] = rising.head(10).to_dict()
            time.sleep(5)
        except Exception as e:
            results['google_trends']['error'] = str(e)
        
        # 2. Reddit Research
        print("👽 Fetching Reddit discussions...")
        if subreddits:
            for sub in subreddits[:5]:
                try:
                    url = f"https://www.reddit.com/r/{sub}/hot.json?limit=10"
                    data = self._reddit_request(url)
                    posts = []
                    for child in data.get('data', {}).get('children', [])[:5]:
                        post = child.get('data', {})
                        posts.append({
                            'title': post.get('title', ''),
                            'score': post.get('score', 0),
                            'comments': post.get('num_comments', 0)
                        })
                    results['reddit'][sub] = posts
                    time.sleep(3)
                except Exception as e:
                    results['reddit'][sub] = {'error': str(e)}
        
        # 3. Keyword search on Reddit
        print("🔍 Searching Reddit for keywords...")
        for kw in keywords[:3]:
            try:
                url = f"https://www.reddit.com/search.json?q={kw}&limit=10&sort=relevance&t=week"
                data = self._reddit_request(url)
                posts = []
                for child in data.get('data', {}).get('children', [])[:5]:
                    post = child.get('data', {})
                    posts.append({
                        'title': post.get('title', ''),
                        'subreddit': post.get('subreddit', ''),
                        'score': post.get('score', 0),
                        'comments': post.get('num_comments', 0)
                    })
                results['reddit'][f'search_{kw}'] = posts
                time.sleep(3)
            except Exception as e:
                results['reddit'][f'search_{kw}'] = {'error': str(e)}
        
        # 4. Generate recommendations
        results['recommendations'] = self._generate_recommendations(results)
        
        return results
    
    def _generate_recommendations(self, data):
        """Generate content recommendations from research data"""
        recommendations = []
        
        # From rising queries
        rising = data.get('google_trends', {}).get('rising_queries', {})
        for kw, queries in rising.items():
            if isinstance(queries, dict) and 'query' in queries:
                for query in list(queries['query'].values())[:3]:
                    recommendations.append({
                        'source': 'Google Trends',
                        'topic': query,
                        'reason': f"Rising search term related to '{kw}'"
                    })
        
        # From Reddit hot posts
        for sub, posts in data.get('reddit', {}).items():
            if isinstance(posts, list):
                for post in posts[:2]:
                    if post.get('score', 0) > 50:
                        recommendations.append({
                            'source': f'Reddit r/{sub}',
                            'topic': post.get('title', ''),
                            'reason': f"High engagement ({post.get('score')} upvotes)"
                        })
        
        return recommendations

# Usage Example
if __name__ == "__main__":
    researcher = TrendResearcher()
    
    results = researcher.research_niche(
        keywords=['heart health', 'cardiology', 'cholesterol'],
        subreddits=['cardiology', 'health', 'medicine'],
        geo='IN'
    )
    
    # Save results
    with open('trend_research.json', 'w') as f:
        json.dump(results, f, indent=2, default=str)
    
    # Print recommendations
    print("\n🎯 CONTENT RECOMMENDATIONS:")
    for rec in results['recommendations']:
        print(f"- [{rec['source']}] {rec['topic']}")
        print(f"  Why: {rec['reason']}")

---

Quick Reference Commands

Daily Trend Check (5 minutes)

from pytrends.request import TrendReq
import requests
import time

# Quick Google Trends check
pytrends = TrendReq(hl='en-US', tz=330)
pytrends.build_payload(['your keyword'], timeframe='now 1-d')
print(pytrends.related_queries()['your keyword']['rising'])

time.sleep(5)

# Quick Reddit check  
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}
url = "https://www.reddit.com/search.json?q=your+keyword&limit=10&t=day"
response = requests.get(url, headers=headers, timeout=10)
data = response.json()
for child in data.get('data', {}).get('children', [])[:5]:
    post = child.get('data', {})
    print(f"[{post.get('score')}] {post.get('title')}")

Weekly Deep Dive

# Use the TrendResearcher class above with:
# - 5 core keywords
# - 5 relevant subreddits
# - 90-day timeframe for velocity analysis

# Then use Perplexity MCP for:
# - Twitter trends in your niche
# - TikTok viral content
# - YouTube trending videos
# - LinkedIn discussions

---

Integration with Writing Skills

After research, pass findings to your writing skills:

1. Run trend research (this skill)
2. Identify top 3-5 opportunities
3. Use content-marketing-social-listening for strategy
4. Use cardiology-content-repurposer or similar for content creation
5. Use authentic-voice for final polish

---

Troubleshooting

pytrends Issues

Error	Solution
429 Too Many Requests	Wait 60 seconds, then increase sleep time
Empty results	Check if keyword has search volume
Connection error	Check internet, retry in 5 minutes

Reddit Issues

Error	Solution
429 Rate Limited	Wait 10 minutes
Subreddit not found	Check subreddit name spelling
Empty results	Subreddit may be private or quarantined
Connection timeout	Increase timeout, check internet

---

Best Practices

1. Always use rate limiting: Sleep between requests
2. Research in batches: Do weekly deep dives, not constant polling
3. Save results: Cache research data locally
4. Cross-reference: Validate trends across multiple platforms
5. Act fast: Viral windows are short (24-72 hours)

---

Platform Coverage Summary

Platform	Tool	Cost	Risk
Google Trends	pytrends	Free	Very Low
Reddit	requests (public JSON)	Free	Low
Twitter/X	Perplexity MCP	Free*	None
TikTok	Perplexity MCP	Free*	None
YouTube	Perplexity MCP	Free*	None
LinkedIn	Perplexity MCP	Free*	None

*Uses Claude's built-in MCP or OpenRouter credits if using perplexity-search skill

---

Bundled Resources

• scripts/trend_research.py: Main CLI tool for complete trend research
• scripts/reddit_scraper.py: Simple Reddit scraper class (no API keys)