Using the Threads API for Academic Research: A Practical Guide

Why Researchers Need Threads Data

Threads by Meta crossed 320 million monthly active users in 2025 and continues to grow rapidly. For social media researchers, this creates a rich dataset for studying public discourse, content virality, platform migration behavior, and community dynamics.

Published research on Threads data is already appearing in IEEE, IIETA, and other academic venues — covering sentiment analysis, political discourse, and user adoption patterns. But accessing the data at scale remains a challenge.

The Data Access Problem

Academic researchers face several barriers when collecting Threads data:

Official Meta Threads API limitations:

• Requires a Meta business account and OAuth approval (2-6 weeks)
• Limited to your own content — you cannot query other users’ public data
• Rate limits: 250 posts/24h for publishing, 500 keyword searches/7 days
• No bulk export functionality

Web scraping drawbacks:

• Violates Meta Terms of Service
• Requires proxy infrastructure ($50-200/month)
• Breaks frequently when Meta updates their frontend
• Ethical concerns for IRB approval

Meta Content Library:

• Restricted to approved academic institutions
• Limited availability and long approval process
• API access varies by region

A Better Approach: Structured API Access

The thredly API provides programmatic access to public Threads data through a simple REST API. No OAuth, no scraping, no browser automation — just your API key and HTTP requests.

This approach is well-suited for academic research because:

• Structured, consistent data — JSON responses with typed fields
• Repeatable collection — Same query returns same format every time
• Ethical access — Accessing public data through structured endpoints
• Affordable — Free tier (100 requests/month), paid plans from $9/month

Getting Started for Researchers

Prerequisites

• Python 3.8+
• requests and pandas libraries
• A free API key from RapidAPI

pip install requests pandas

Setup

import requests
import pandas as pd
from datetime import datetime

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://threads-api-pro.p.rapidapi.com"
HEADERS = {
    "X-RapidAPI-Key": API_KEY,
    "X-RapidAPI-Host": "threads-api-pro.p.rapidapi.com"
}

def api_get(endpoint):
    """Reusable API request function with error handling."""
    response = requests.get(f"{BASE_URL}{endpoint}", headers=HEADERS)
    response.raise_for_status()
    result = response.json()
    if not result.get("success"):
        raise Exception(result.get("error", "Unknown API error"))
    return result["data"]

Research Use Case 1: User Profile Analysis

Collect profile data for a set of public figures or influencers:

def collect_profiles(usernames):
    """Collect profile data for multiple users."""
    profiles = []
    for username in usernames:
        try:
            data = api_get(f"/api/user/{username}")
            profiles.append({
                "username": data["username"],
                "full_name": data.get("full_name", ""),
                "followers": data["follower_count"],
                "following": data["following_count"],
                "is_verified": data.get("is_verified", False),
                "collected_at": datetime.now().isoformat()
            })
        except Exception as e:
            print(f"Error fetching {username}: {e}")
    return pd.DataFrame(profiles)

# Example: collect profiles for study participants
subjects = ["zuck", "mosseri", "instagram"]
df = collect_profiles(subjects)
df.to_csv("profiles_dataset.csv", index=False)

Research Use Case 2: Content Collection for Sentiment Analysis

Collect posts from specific users for NLP analysis:

def collect_user_posts(username, save_path=None):
    """Collect all available posts for a user."""
    posts = api_get(f"/api/user/{username}/posts")

    records = []
    for post in posts:
        records.append({
            "username": username,
            "post_id": post.get("id"),
            "text": post.get("text", ""),
            "like_count": post.get("like_count", 0),
            "reply_count": post.get("reply_count", 0),
            "created_at": post.get("created_at"),
            "engagement": post.get("like_count", 0) + post.get("reply_count", 0)
        })

    df = pd.DataFrame(records)

    if save_path:
        df.to_csv(save_path, index=False)
        print(f"Saved {len(records)} posts to {save_path}")

    return df

This data can feed directly into sentiment analysis pipelines using libraries like TextBlob, VADER, or transformer-based models.

Research Use Case 3: Engagement Pattern Analysis

Study how engagement varies across users, time periods, or content types:

def analyze_engagement(posts_df):
    """Calculate engagement metrics from collected posts."""
    if posts_df.empty:
        return {}

    return {
        "total_posts": len(posts_df),
        "avg_likes": posts_df["like_count"].mean(),
        "avg_replies": posts_df["reply_count"].mean(),
        "avg_engagement": posts_df["engagement"].mean(),
        "max_engagement_post": posts_df.loc[
            posts_df["engagement"].idxmax(), "text"
        ][:100],
        "reply_to_like_ratio": (
            posts_df["reply_count"].sum()
            / max(posts_df["like_count"].sum(), 1)
        )
    }

Research Use Case 4: Longitudinal Data Collection

For studies tracking changes over time, set up periodic collection:

import json
from pathlib import Path

def longitudinal_snapshot(usernames, output_dir="data"):
    """Take a daily snapshot of user metrics."""
    Path(output_dir).mkdir(exist_ok=True)
    timestamp = datetime.now().strftime("%Y%m%d")

    snapshot = {}
    for username in usernames:
        try:
            profile = api_get(f"/api/user/{username}")
            snapshot[username] = {
                "followers": profile["follower_count"],
                "following": profile["following_count"],
                "timestamp": datetime.now().isoformat()
            }
        except Exception as e:
            print(f"Error: {username} - {e}")

    filepath = f"{output_dir}/snapshot_{timestamp}.json"
    with open(filepath, "w") as f:
        json.dump(snapshot, f, indent=2)

    print(f"Snapshot saved: {filepath}")

Run this daily via cron to build longitudinal datasets.

Exporting Data for Analysis Tools

Export to CSV (for Excel, SPSS, Stata)

df.to_csv("threads_dataset.csv", index=False, encoding="utf-8")

Export to JSON (for custom pipelines)

df.to_json("threads_dataset.json", orient="records", indent=2)

Export to Parquet (for large datasets)

df.to_parquet("threads_dataset.parquet", index=False)

Ethical Considerations

When using Threads data for research:

• Collect only public data — thredly only accesses publicly available profiles and posts
• Anonymize when publishing — Remove or hash usernames in published results unless studying public figures
• Follow your institution’s IRB guidelines — Consult your ethics board about social media data collection
• Respect rate limits — Don’t overwhelm the API; plan your collection schedule
• Document your methodology — Record API version, collection dates, and endpoints used for reproducibility
• Data retention — Follow your institution’s data management plan for storage and deletion

Rate Limits for Research Projects

Citing thredly in Your Research

If you use thredly in published research, we recommend citing it as a data source in your methodology section. Example:

Data was collected using the thredly API (thredly.dev), a REST API providing structured access to public Threads data, between [start date] and [end date].

Next Steps

• Get your API key — Set up in under 5 minutes
• Python tutorial — Detailed Python code examples
• Engagement tracking guide — Build a full engagement tracker
• API reference — All available endpoints