# Exploring TwitterXApi's Get Multiple Users by Usernames Endpoint: A Guide to Fetching User Profiles on X (Formerly Twitter) In the realm of social media data analysis, accessing user profiles efficiently is crucial for tasks like network mapping, influence tracking, or content curation. TwitterXApi.com's **Get Multiple Users by Usernames** endpoint simplifies this by allowing you to retrieve detailed profile information for multiple X (formerly Twitter) users in a single request. Whether you're building a social graph, verifying accounts, or researching influencers, this POST endpoint delivers comprehensive user data without the constraints of official APIs. In this blog post, we'll break down how the API works, its parameters, response format, and practical examples. By the end, you'll be ready to integrate it into your applications for bulk user lookups. ## Why Use the Get Multiple Users by Usernames Endpoint? This endpoint stands out for its batch processing capability, enabling you to fetch profiles for up to multiple users at once. Key benefits include: - Retrieve rich details like follower counts, bio, location, verification status, and more. - Handle usernames or profile URLs directly. - Efficient for large-scale operations, with null responses for non-existent users. It's ideal for: - **Influence Analysis**: Compare metrics across influencers like @elonmusk and @sundarpichai. - **Data Enrichment**: Add profile info to datasets for machine learning. - **Verification Tools**: Check if accounts are verified or protected. - **Social Network Mapping**: Build comprehensive user relationship graphs. - **Competitor Research**: Analyze profiles of industry leaders and competitors. TwitterXApi manages the scraping, so you get reliable data effortlessly. ## API Overview The endpoint is a POST request to `https://api.twitterxapi.com/twitter/users`. It requires Bearer token authentication (from your TwitterXApi dashboard). The body is a JSON array of usernames or profile URLs. ### Key Request Parameters - **Body** (array of strings, required): List of usernames (e.g., ["elonmusk", "sundarpichai"]) or profile URLs. No additional query parameters are needed—the simplicity is part of its power. ### Response Structure A successful response (HTTP 200) includes: - **code**: Status code (e.g., 200). - **msg**: Message (e.g., "success"). - **data**: Array of user objects (or null for unfound users), with fields like userId, isBlueVerified, followersCount, description, and more. Errors (e.g., HTTP 422) provide validation details. ## Code Examples: How to Use the API Let's fetch profiles for @elonmusk and @sundarpichai. Replace `` with your Bearer token. ### Example 1: Basic cURL Request This retrieves profiles for multiple users in a single request. {`curl --request POST \\ --url https://api.twitterxapi.com/twitter/users \\ --header 'Authorization: Bearer ' \\ --header 'Content-Type: application/json' \\ --data '["elonmusk", "sundarpichai"]'`} **Expected Response Snippet** (abbreviated): {`{ "code": 200, "msg": "success", "data": [ { "userId": "44196397", "isBlueVerified": true, "createdAt": "Tue Jun 02 20:12:29 +0000 2009", "description": "Elon Musk's bio", "location": "Austin, TX", "followersCount": 150000000, "name": "Elon Musk", "username": "elonmusk", "verified": true }, { "userId": "14130366", "isBlueVerified": true, "createdAt": "Tue Mar 11 23:51:24 +0000 2008", "description": "Sundar Pichai's bio", "location": "Mountain View, CA", "followersCount": 3000000, "name": "Sundar Pichai", "username": "sundarpichai", "verified": true } ] }`} ### Example 2: Python Script for Fetching and Processing User Profiles This script fetches user data and prints key metrics. Install `requests` if needed (`pip install requests`). {`import requests import json # Your Bearer token TOKEN = "" # API endpoint url = "https://api.twitterxapi.com/twitter/users" # Request payload (array of usernames) payload = ["elonmusk", "sundarpichai", "tim_cook", "satyanadella"] headers = { "Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json" } # Make the POST request response = requests.post(url, headers=headers, data=json.dumps(payload)) # Check response if response.status_code == 200: data = response.json() print("Fetch successful! Retrieved", len(data["data"]), "user profiles.") # Print details for each user for user in data["data"]: if user: # Skip null for unfound users print(f"Username: {user['username']}") print(f"Name: {user['name']}") print(f"Followers: {user['followersCount']:,}") print(f"Blue Verified: {user['isBlueVerified']}") print(f"Legacy Verified: {user['verified']}") print(f"Location: {user.get('location', 'Not specified')}") print(f"Bio: {user.get('description', 'No bio')[:100]}...") print("---") else: print("User not found (null response)") print("---") else: print("Error:", response.status_code, response.text)`} This script processes the response and displays user info, handling any null entries gracefully. ## Advanced Examples: Bulk User Analysis Here's a more sophisticated example that analyzes user data and provides insights: {`import requests import json import pandas as pd from datetime import datetime class TwitterUserAnalyzer: def __init__(self, bearer_token): self.bearer_token = bearer_token self.base_url = "https://api.twitterxapi.com/twitter/users" self.headers = { "Authorization": f"Bearer {bearer_token}", "Content-Type": "application/json" } def fetch_users(self, usernames): """ Fetch multiple user profiles """ response = requests.post( self.base_url, headers=self.headers, data=json.dumps(usernames) ) if response.status_code == 200: return response.json()['data'] else: print(f"Error: {response.status_code} - {response.text}") return None def analyze_users(self, usernames): """ Fetch and analyze user profiles """ users_data = self.fetch_users(usernames) if not users_data: return None # Filter out null responses valid_users = [user for user in users_data if user is not None] if not valid_users: print("No valid users found") return None # Convert to DataFrame for analysis df = pd.DataFrame(valid_users) # Basic statistics analysis = { 'total_users': len(valid_users), 'verified_users': len(df[df['verified'] == True]), 'blue_verified_users': len(df[df['isBlueVerified'] == True]), 'avg_followers': df['followersCount'].mean(), 'median_followers': df['followersCount'].median(), 'max_followers': df['followersCount'].max(), 'min_followers': df['followersCount'].min(), 'users_with_location': len(df[df['location'].notna()]), 'users_with_bio': len(df[df['description'].notna()]) } return analysis, df def compare_influencers(self, usernames): """ Compare influencer metrics """ users_data = self.fetch_users(usernames) if not users_data: return None valid_users = [user for user in users_data if user is not None] print("=== INFLUENCER COMPARISON ===") print(f"{'Username':<20} {'Followers':<15} {'Verified':<10} {'Blue Verified':<15}") print("-" * 65) # Sort by follower count sorted_users = sorted(valid_users, key=lambda x: x['followersCount'], reverse=True) for user in sorted_users: followers = f"{user['followersCount']:,}" verified = "✓" if user['verified'] else "✗" blue_verified = "✓" if user['isBlueVerified'] else "✗" print(f"{user['username']:<20} {followers:<15} {verified:<10} {blue_verified:<15}") return sorted_users # Example usage analyzer = TwitterUserAnalyzer("") # Tech leaders comparison tech_leaders = [ "elonmusk", "sundarpichai", "tim_cook", "satyanadella", "jeffbezos", "billgates", "zuck", "jack" ] print("Analyzing tech leaders...") analysis, df = analyzer.analyze_users(tech_leaders) if analysis: print("\\n=== ANALYSIS RESULTS ===") print(f"Total users analyzed: {analysis['total_users']}") print(f"Verified users: {analysis['verified_users']}") print(f"Blue verified users: {analysis['blue_verified_users']}") print(f"Average followers: {analysis['avg_followers']:,.0f}") print(f"Median followers: {analysis['median_followers']:,.0f}") print(f"Users with location: {analysis['users_with_location']}") print(f"Users with bio: {analysis['users_with_bio']}") print("\\n") analyzer.compare_influencers(tech_leaders)`} ## Handling Large-Scale Operations When working with hundreds or thousands of users, implement batch processing and error handling: {`import requests import json import time from typing import List, Dict, Any, Optional class BulkUserProcessor: def __init__(self, bearer_token: str, batch_size: int = 50): self.bearer_token = bearer_token self.base_url = "https://api.twitterxapi.com/twitter/users" self.headers = { "Authorization": f"Bearer {bearer_token}", "Content-Type": "application/json" } self.batch_size = batch_size self.processed_users = [] self.failed_batches = [] def process_batch(self, usernames: List[str]) -> Optional[List[Dict[str, Any]]]: """ Process a single batch of usernames """ try: response = requests.post( self.base_url, headers=self.headers, data=json.dumps(usernames), timeout=30 ) if response.status_code == 200: return response.json()['data'] else: print(f"Batch failed: {response.status_code} - {response.text}") return None except requests.exceptions.RequestException as e: print(f"Request failed: {e}") return None def process_all_users(self, all_usernames: List[str], delay: int = 1) -> List[Dict[str, Any]]: """ Process all users in batches with rate limiting """ all_results = [] # Split into batches for i in range(0, len(all_usernames), self.batch_size): batch = all_usernames[i:i + self.batch_size] batch_number = (i // self.batch_size) + 1 total_batches = (len(all_usernames) + self.batch_size - 1) // self.batch_size print(f"Processing batch {batch_number}/{total_batches} ({len(batch)} users)...") batch_results = self.process_batch(batch) if batch_results: all_results.extend(batch_results) print(f"Batch {batch_number} completed successfully") else: self.failed_batches.append(batch) print(f"Batch {batch_number} failed") # Rate limiting delay if i + self.batch_size < len(all_usernames): print(f"Waiting {delay} second(s) before next batch...") time.sleep(delay) return all_results def retry_failed_batches(self) -> List[Dict[str, Any]]: """ Retry failed batches """ if not self.failed_batches: print("No failed batches to retry") return [] print(f"Retrying {len(self.failed_batches)} failed batches...") retry_results = [] for i, batch in enumerate(self.failed_batches): print(f"Retrying batch {i + 1}/{len(self.failed_batches)}...") batch_results = self.process_batch(batch) if batch_results: retry_results.extend(batch_results) print(f"Retry batch {i + 1} succeeded") else: print(f"Retry batch {i + 1} failed again") time.sleep(2) # Longer delay for retries return retry_results def save_results(self, results: List[Dict[str, Any]], filename: str): """ Save results to JSON file """ # Filter out null results valid_results = [user for user in results if user is not None] with open(filename, 'w', encoding='utf-8') as f: json.dump(valid_results, f, indent=2, ensure_ascii=False) print(f"Saved {len(valid_results)} user profiles to {filename}") # Example: Process a large list of users processor = BulkUserProcessor("", batch_size=25) # Example list of tech influencers and journalists large_user_list = [ "elonmusk", "sundarpichai", "tim_cook", "satyanadella", "jeffbezos", "billgates", "zuck", "jack", "richardbranson", "oprah", "neiltyson", "stephenfry", "rickygervais", "justinbieber", "ladygaga", "kanyewest", "arianagrande", "rihanna", "taylorswift13", "shakira" # Add more usernames as needed ] print(f"Processing {len(large_user_list)} users in batches...") # Process all users all_results = processor.process_all_users(large_user_list, delay=2) # Retry any failed batches retry_results = processor.retry_failed_batches() # Combine all results final_results = all_results + retry_results print(f"\\nFinal results: {len(final_results)} users processed") print(f"Valid profiles: {len([u for u in final_results if u is not None])}") print(f"Not found: {len([u for u in final_results if u is None])}") # Save to file processor.save_results(final_results, "bulk_user_profiles.json")`} ## Use Case Examples ### Social Network Analysis {`def analyze_user_network(central_users, analyzer): """ Analyze a network of users for influence patterns """ users_data = analyzer.fetch_users(central_users) valid_users = [user for user in users_data if user is not None] # Calculate influence metrics influence_metrics = [] for user in valid_users: influence_score = ( user['followersCount'] * 0.7 + # Follower weight (1000 if user['verified'] else 0) * 0.2 + # Verification weight (500 if user['isBlueVerified'] else 0) * 0.1 # Blue verification weight ) influence_metrics.append({ 'username': user['username'], 'name': user['name'], 'followers': user['followersCount'], 'influence_score': influence_score, 'verified': user['verified'], 'blue_verified': user['isBlueVerified'] }) # Sort by influence score influence_metrics.sort(key=lambda x: x['influence_score'], reverse=True) print("=== INFLUENCE RANKING ===") for i, user in enumerate(influence_metrics, 1): print(f"{i:2d}. {user['username']:<20} Score: {user['influence_score']:,.0f}") return influence_metrics # Example usage tech_leaders = ["elonmusk", "sundarpichai", "tim_cook", "satyanadella"] influence_ranking = analyze_user_network(tech_leaders, analyzer)`} ### Account Verification Checker {`def check_verification_status(usernames, analyzer): """ Check verification status for multiple accounts """ users_data = analyzer.fetch_users(usernames) verification_report = { 'verified_legacy': [], 'verified_blue': [], 'not_verified': [], 'not_found': [] } for i, user in enumerate(users_data): username = usernames[i] if user is None: verification_report['not_found'].append(username) elif user['verified']: verification_report['verified_legacy'].append({ 'username': user['username'], 'name': user['name'], 'followers': user['followersCount'] }) elif user['isBlueVerified']: verification_report['verified_blue'].append({ 'username': user['username'], 'name': user['name'], 'followers': user['followersCount'] }) else: verification_report['not_verified'].append({ 'username': user['username'], 'name': user['name'], 'followers': user['followersCount'] }) # Print report print("=== VERIFICATION REPORT ===") print(f"Legacy Verified: {len(verification_report['verified_legacy'])}") print(f"Blue Verified: {len(verification_report['verified_blue'])}") print(f"Not Verified: {len(verification_report['not_verified'])}") print(f"Not Found: {len(verification_report['not_found'])}") return verification_report # Example usage accounts_to_check = [ "elonmusk", "sundarpichai", "some_fake_account", "tim_cook", "another_fake_one", "satyanadella" ] verification_report = check_verification_status(accounts_to_check, analyzer)`} ## Best Practices and Tips ### Efficient Batch Processing 1. **Optimal Batch Size**: Start with 25-50 users per request and adjust based on response times 2. **Rate Limiting**: Add delays between requests to avoid overwhelming the API 3. **Error Handling**: Always check for null responses (user not found) 4. **Retry Logic**: Implement retry mechanisms for failed requests ### Data Quality {`def validate_and_clean_usernames(usernames): """ Clean and validate usernames before API call """ cleaned_usernames = [] for username in usernames: # Remove @ symbol if present clean_username = username.lstrip('@') # Handle full URLs if 'twitter.com/' in clean_username or 'x.com/' in clean_username: clean_username = clean_username.split('/')[-1] # Remove any query parameters clean_username = clean_username.split('?')[0] # Basic validation (alphanumeric + underscore) if clean_username and clean_username.replace('_', '').replace('.', '').isalnum(): cleaned_usernames.append(clean_username) else: print(f"Skipping invalid username: {username}") return list(set(cleaned_usernames)) # Remove duplicates # Example usage messy_usernames = [ "@elonmusk", "https://twitter.com/sundarpichai", "tim_cook", "x.com/satyanadella?ref=search", "invalid@username!", "", "duplicate", "duplicate" ] clean_usernames = validate_and_clean_usernames(messy_usernames) print("Cleaned usernames:", clean_usernames)`} ## Potential Use Cases and Applications ### Use Cases: - **Social Network Mapping**: Fetch profiles of connected users to build comprehensive graphs - **Competitor Analysis**: Compare metrics across industry leaders and competitors - **Bulk Verification**: Check verification status for lists of accounts efficiently - **Influencer Research**: Analyze follower counts, engagement, and verification status - **Data Enrichment**: Add profile information to existing datasets for machine learning - **Account Monitoring**: Track changes in user profiles over time - **Fraud Detection**: Identify suspicious patterns in user account data ### Best Practices: - Limit batches to stay within rate limits—check your plan on the TwitterXApi dashboard - Use profile URLs if usernames change frequently (e.g., "https://x.com/elonmusk") - Combine with other endpoints like followers or tweets for deeper insights - Cache results when possible to reduce API calls - Handle null responses gracefully for users that don't exist - Implement proper error handling and retry logic for production use ## Conclusion TwitterXApi's Get Multiple Users by Usernames endpoint is an efficient and powerful way to gather user profile data in bulk, making it perfect for developers, researchers, and analysts working with social media data. The ability to fetch multiple profiles in a single request dramatically reduces the complexity and overhead of user data collection. Whether you're mapping social networks, analyzing influencer metrics, or enriching datasets, this endpoint provides the reliability and comprehensive data you need. The simple request format combined with rich response data makes it easy to integrate into any application or workflow. ### Getting Started 1. **Sign up** at [TwitterXApi.com](https://www.twexapi.io/) and get your Bearer token 2. **Start with small batches** to understand the response format 3. **Gradually scale up** to larger batch sizes based on your needs 4. **Implement proper error handling** for production applications ### Next Steps - Explore other TwitterXApi endpoints for comprehensive Twitter data collection - Combine user data with tweet analysis for deeper insights - Build monitoring systems to track user profile changes over time - Integrate with data visualization tools for social network analysis Try it out and enhance your social media projects today! If you have questions or ideas, comment below or reach out on [X](https://x.com/axiaisacat). Happy coding! --- *Disclaimer: This blog is based on TwitterXApi's documentation as of July 2025. API details may change—always refer to the official docs. This content is for educational purposes only. Respect platform terms when collecting user data and ensure compliance with applicable laws and regulations.*