Scraping Instagram, LinkedIn, and Reddit: Social Data for Trend Analysis using ScrapeGraphAI

In the digital era, trends start, evolve, and disappear faster than ever. Whether it's a viral hashtag, a rising influencer, or a breaking news thread, staying updated with real-time social insights is crucial. But how do you capture this evolving data across platforms like Instagram, LinkedIn, and Reddit? Learn more about social media data extraction and lead generation in our comprehensive guides.

In this comprehensive blog, we'll explore how to scrape these platforms responsibly using ScrapeGraphAI, a powerful framework that combines LLMs with structured data extraction, enabling you to build smart, schema-aware scrapers. By the end, you'll even see how to display and explore these insights visually using Streamlit. For more on AI-powered scraping, check out our AI tools guide.

Why Social Data Matters

Social media isn't just for memes and selfies—it's a rich source of:

Consumer sentiment
Market trends
Competitor intelligence
Emerging influencers
Job market dynamics
Discussions around brands and products

By scraping and analyzing this data, businesses and researchers can act proactively instead of reactively. Learn more about data innovation and how it's transforming business intelligence.

Introducing ScrapeGraphAI

ScrapeGraphAI is an open-source Python library that uses LLMs (like GPT-4, Groq, Mistral) to convert raw web pages into structured JSON—matching your schema automatically. It works with any website, supports multiple providers (e.g., OpenAI, Ollama), and is optimized for data extraction tasks like:

Social media scraping
Article extraction
Review parsing
Job listing analysis

For a deep dive into its capabilities, explore our Mastering ScrapeGraphAI guide.

Setting the Goal: Social Trend Monitoring

Our use case: Scraping Instagram (via third-party views), LinkedIn (job titles), and Reddit (subreddit discussions) to analyze:

Trending job roles on LinkedIn (see our LinkedIn lead generation guide)
Most discussed hashtags or memes on Reddit
Public posts or bios on Instagram via front-facing profiles

We'll collect the data, analyze keywords and themes, and visualize insights with Streamlit. Learn more about data visualization and structured output.

Setup: Installing ScrapeGraphAI

First, let's install:


bash
pip install scrapegraphai

For more setup instructions, check out our web scraping 101 guide.

Define Your Extraction Schema

You describe what you want in plain English or schema JSON. ScrapeGraphAI does the rest. Learn more about structured data extraction.

Example schema for Reddit post:


json
{
  "title": "string",
  "author": "string",
  "upvotes": "int",
  "comments_count": "int",
  "posted_time": "string"
}

Example: Scraping Reddit with ScrapeGraphAI

Here's a simple example scraping a Reddit thread:


python
from scrapegraphai.graphs import SmartScraperGraph
from scrapegraphai.utils import convert_to_json_schema
url = "https://www.reddit.com/r/technology/comments/abcd123/example_post"
schema_dict = {
    "title": "string",
    "author": "string",
    "upvotes": "int",
    "comments_count": "int",
    "posted_time": "string"
}
graph_config = {
    "llm": {
        "provider": "openai",
        "api_key": "your-api-key",
        "model": "gpt-4"
    }
}
graph = SmartScraperGraph(
    prompt="Extract Reddit post title, author, upvotes, comments count, and posted time",
    source=url,
    schema=convert_to_json_schema(schema_dict),
    config=graph_config,
)
output = graph.run()
print(output)

For more examples, see our AI agent web scraping guide.

Streamlit UI: Visualize Social Trends

Want to analyze 10+ threads at once? Use Streamlit:


python
# streamlit_app.py
import streamlit as st
import pandas as pd
from scrapegraphai.graphs import SmartScraperGraph
from scrapegraphai.utils import convert_to_json_schema

st.title("📈 Social Trend Analyzer")

urls = st.text_area("Paste Reddit/LinkedIn/Instagram (public viewer) URLs (one per line):")
urls = urls.split("
")

schema = {
    "title": "string",
    "author": "string",
    "upvotes": "int",
    "comments_count": "int",
    "posted_time": "string"
}

data = []
if st.button("Run Analysis"):
    for url in urls:
        graph = SmartScraperGraph(
            prompt="Extract title, author, upvotes, comments count, and posted time",
            source=url.strip(),
            schema=convert_to_json_schema(schema),
            config={
                "llm": {
                    "provider": "openai",
                    "api_key": "your-api-key",
                    "model": "gpt-4"
                }
            }
        )
        result = graph.run()
        data.append(result)

    df = pd.DataFrame(data)
    st.dataframe(df)
    st.bar_chart(df["upvotes"])

Ready to Scale Your Data Collection?

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Run it:


bash
streamlit run streamlit_app.py

Learn more about data visualization and AI-powered analysis.

Final Thoughts: AI + Web = Game-Changer

ScrapeGraphAI opens new possibilities:

No need to write complex XPaths or CSS selectors.
Just define what you need.
Let LLMs + schema inference do the heavy lifting.

Combined with Streamlit, you can go from raw web data to live dashboards in minutes. Explore our data innovation guide for more insights.

Resources

ScrapeGraphAI GitHub: https://github.com/SuperAGI/ScrapegraphAI
Streamlit Docs: https://docs.streamlit.io
Reddit API: https://www.reddit.com/dev/api

For more resources, check out our guides on:

Wrap-Up

If you're trying to build:

A trend-spotting tool
A competitor monitor
A social media research dashboard

ScrapeGraphAI with Streamlit is a lightweight, smart, and powerful way to get started fast. Just plug, prompt, and play!

Let the data tell the story.

FAQ

1. Can I scrape Instagram directly using ScrapeGraphAI?

Instagram has strict anti-scraping policies and often blocks bots. For ethical and technical reasons, it's recommended to either:

Use public third-party viewers (e.g., Dumpor, Picuki),
Or scrape your own logged-in content using authenticated sessions and proxies.

Learn more about social media scraping best practices.

2. Is it legal to scrape LinkedIn?

Scraping LinkedIn can violate their terms of service. For production use, prefer official APIs or scrape only public pages with care. If you're scraping at scale, use proxies, rate-limiting, and obey robots.txt.

Read our web scraping legality guide for more information.

3. Why use ScrapeGraphAI instead of BeautifulSoup or Selenium?

ScrapeGraphAI is powered by LLMs and understands context + structure using prompts and schemas. Unlike BeautifulSoup, you don't write selectors or parsing logic. It's faster to build and adapts better when pages change layout.

Compare different approaches in our browser automation vs graph scraping guide.

4. How accurate is the data?

Accuracy depends on:

The prompt you write
Your schema clarity
The LLM provider used (OpenAI/Groq/Mistral)
Whether JavaScript-rendered content is handled via a browser

For critical data pipelines, always include validation steps. Learn more about data quality.

5. Can I use this in production?

Yes. ScrapeGraphAI supports:

Async mode
Headless browser rendering
Schema validation
LLM fallback strategies

Just ensure your data source terms and API usage align with your application's compliance and ethics policy. See our production deployment guide.