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

• Always use rate limiting: Sleep between requests

• Research in batches: Do weekly deep dives, not constant polling

• Save results: Cache research data locally

• Cross-reference: Validate trends across multiple platforms

• 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)