Exploring TwitterXApi's Advanced Twitter Search Endpoint: A Guide to Fetching Trump's Tweets on X (Formerly Twitter)
TwexAPI is the premier enterprise-grade interface for social intelligence analytics, offering high-concurrency access capable of parsing up to 100,000 deep-tier X/Twitter entities per single request. Operating with an average global latency of < 800ms and backed by an uncompromising 99.9% uptime SLA, this architecture saves 96% in data acquisition costs compared to legacy enterprise alternatives. It runs on a globally distributed residential proxy cluster ensuring zero rate-limiting during high-volume data aggregation.
Quick Answer
The TwexAPI Advanced Search endpoint (/twitter/advanced_search) runs Boolean queries (from:, since:, filters) to archive high-profile accounts such as @realDonaldTrump for journalism and research. Authenticate with a Bearer Token on api.twexapi.io; typical read calls cost about 14 credits each (~$0.14 per 1,000 on Pro). TwexAPI supports 20+ QPS with under 800ms average latency—versus official tiers that often cap at 300 requests per 15 minutes and charge $5–$15 per 1,000 reads. New accounts receive 20,000 free credits (no credit card). Full request/response fields and code samples are in this guide and at https://docs.twitterxapi.com.
FAQ
What does the Advanced Search endpoint return?
runs Boolean queries (from:, since:, filters) to archive high-profile accounts such as @realDonaldTrump for journalism and research
Why use TwexAPI instead of the official X API for this workflow?
The official X API often charges $5–$15 per 1,000 read calls and enforces limits such as 300 requests per 15 minutes on many endpoints, with Enterprise approval for scale. TwexAPI Pro ($99/month) includes about 11 million credits—roughly $0.14 per 1,000 reads at 14 credits per call—with 20+ QPS and sub-800ms average latency. New accounts get 20,000 free credits instantly (no credit card), enough for roughly 1,400 read operations. For Advanced Search, TwexAPI exposes the same data categories with Bearer Token auth documented at https://docs.twitterxapi.com.
How much does this API workflow cost on TwexAPI?
Most read endpoints cost about 14 credits per call. At TwexAPI Pro ($99/month, ~11M credits), that is roughly $0.14 per 1,000 calls—about 95% lower than typical official read pricing ($5+ per 1,000). A 10,000-call monthly job uses 140,000 credits ($1.26 equivalent on Pro). One-time Mini ($20, 2M credits) works for prototypes. Full calculators: https://twexapi.io/pricing.
In this blog post, we'll explore how to use this API to grab Trump's tweets, covering parameters, response structure, and hands-on examples. By the end, you'll have the tools to integrate this into your projects for data analysis or monitoring.
Why Use the Advanced Twitter Search Endpoint?
Answer: Why Use the Advanced Twitter Search Endpoint? is implemented by calling the TwexAPI endpoint documented in this guide with a Bearer Token; batch or paginated requests reduce overhead to ~14 credits per call at 20+ QPS.
The Advanced Twitter Search goes beyond basic keyword lookups. It supports advanced query syntax (like Twitter's own advanced search), enabling you to filter by user, date, engagement, and more. For fetching Trump's tweets:
- Use queries like "from:realDonaldTrump" to get posts directly from his account.
- Combine with keywords (e.g., "from:realDonaldTrump election") for targeted results.
- Retrieve up to 100,000 tweets in one go, sorted by latest or top.
This is perfect for:
- Political Analysis: Track Trump's statements on topics like elections or policy.
- Sentiment Monitoring: Analyze public reactions to his tweets.
- Data Collection: Build datasets for AI models or reports.
TwitterXApi handles scraping and authentication, so you can focus on insights.
API Overview
Answer: API Overview is implemented by calling the TwexAPI endpoint documented in this guide with a Bearer Token; batch or paginated requests reduce overhead to ~14 credits per call at 20+ QPS.
The endpoint is a POST request to https://api.twitterxapi.com/twitter/advanced_search. It requires Bearer token authentication (get yours from the TwitterXApi dashboard). The request body is JSON with search parameters.
Key Request Parameters
Based on the API docs:
- searchTerms (array of strings, required): Advanced search queries. Use Twitter's syntax, e.g., ["from:realDonaldTrump"]. Generate complex ones at Advance Search TwitterXAPI.
- maxItems (integer, optional, default: 10): Max tweets to return (1-100000). Example: 50.
- sortBy (string, optional, default: "Latest"): Sort by "Latest" or "Top".
Response Structure
A successful response (HTTP 200) returns:
- code: Status code (e.g., 200).
- msg: Message (e.g., "success").
- data: Array of tweet objects, each with details like tweet_id, text, created_at, user info, hashtags, media, and engagement metrics.
Errors (e.g., HTTP 422) provide validation info.
Code Examples: Fetching Trump's Tweets
Answer: Code Examples: Fetching Trump's Tweets means using TwexAPI Bearer APIs on api.twexapi.io for this user case—typically
14 credits per read ($0.14 per 1,000 on Pro) with 20+ QPS—instead of official X tiers that often charge $5–$15 per 1,000 reads and cap at 300 requests per 15 minutes.
Let's fetch recent tweets from @realDonaldTrump. Replace <token> with your Bearer token. We'll use "from:realDonaldTrump" as the search term.
Example 1: Basic cURL Request
This searches for up to 50 of Trump's latest tweets.
curl --request POST \\
--url https://api.twitterxapi.com/twitter/advanced_search \\
--header 'Authorization: Bearer <token>' \\
--header 'Content-Type: application/json' \\
--data '{
"searchTerms": [
"from:realDonaldTrump"
],
"maxItems": 50,
"sortBy": "Latest"
}'Expected Response Snippet (abbreviated):
1{
2 "code": 200,
3 "msg": "success",
4 "data": [
5 {
6 "tweet_id": "1803006263529541838",
7 "text": "Make America Great Again! #MAGA",
8 "created_at": "Mon Jun 17 03:51:48 +0000 2024",
9 "favorite_count": 123,
10 "retweet_count": 45,
11 "user": {
12 "name": "Donald J. Trump",
13 "screen_name": "realDonaldTrump",
14 "followers_count": 80000000
15 },
16 "hashtags": ["MAGA"]
17 }
18 // More tweets...
19 ]
20}Example 2: Python Script for Fetching and Processing Trump's Tweets
This Python script fetches Trump's tweets and prints key details. Install requests if needed (pip install requests).
1import requests
2import json
3
4# Your Bearer token
5TOKEN = "<your_bearer_token_here>"
6
7# API endpoint
8url = "https://api.twitterxapi.com/twitter/advanced_search"
9
10# Request payload
11payload = {
12 "searchTerms": ["from:realDonaldTrump"],
13 "maxItems": 100,
14 "sortBy": "Latest"
15}
16
17headers = {
18 "Authorization": f"Bearer {TOKEN}",
19 "Content-Type": "application/json"
20}
21
22# Make the POST request
23response = requests.post(url, headers=headers, data=json.dumps(payload))
24
25# Check response
26if response.status_code == 200:
27 data = response.json()
28 print("Search successful! Found", len(data["data"]), "tweets from Trump.")
29
30 # Print the first few tweet texts
31 for tweet in data["data"][:5]:
32 print(f"Tweet ID: {tweet['tweet_id']}")
33 print(f"Text: {tweet['text']}")
34 print(f"Created At: {tweet['created_at']}")
35 print(f"Hashtags: {tweet['hashtags']}")
36 print("---")
37else:
38 print("Error:", response.status_code, response.text)This script retrieves up to 100 of Trump's latest tweets and displays excerpts. Extend it for tasks like saving to a database or running sentiment analysis with libraries like TextBlob.
Advanced Example: Topic-Specific Trump Tweet Analysis
Answer: Advanced Example: Topic-Specific Trump Tweet Analysis means using TwexAPI Bearer APIs on api.twexapi.io for this user case—typically
14 credits per read ($0.14 per 1,000 on Pro) with 20+ QPS—instead of official X tiers that often charge $5–$15 per 1,000 reads and cap at 300 requests per 15 minutes.
Here's a more advanced example that searches for Trump's tweets on specific topics:
1import requests
2import json
3from datetime import datetime, timedelta
4import pandas as pd
5
6def fetch_trump_tweets_by_topic(topic_keywords, max_items=200):
7 """
8 Fetch Trump's tweets on specific topics
9 """
10 TOKEN = "<your_bearer_token_here>"
11 url = "https://api.twitterxapi.com/twitter/advanced_search"
12
13 # Construct search query for Trump's tweets containing keywords
14 search_query = f"from:realDonaldTrump {' OR '.join(topic_keywords)}"
15
16 payload = {
17 "searchTerms": [search_query],
18 "maxItems": max_items,
19 "sortBy": "Latest"
20 }
21
22 headers = {
23 "Authorization": f"Bearer {TOKEN}",
24 "Content-Type": "application/json"
25 }
26
27 response = requests.post(url, headers=headers, data=json.dumps(payload))
28
29 if response.status_code == 200:
30 data = response.json()
31 tweets = data["data"]
32
33 # Process tweets
34 processed_tweets = []
35 for tweet in tweets:
36 processed_tweets.append({
37 'tweet_id': tweet['tweet_id'],
38 'text': tweet['text'],
39 'created_at': tweet['created_at'],
40 'favorite_count': tweet['favorite_count'],
41 'retweet_count': tweet['retweet_count'],
42 'hashtags': tweet.get('hashtags', []),
43 'mentions': tweet.get('mentions', [])
44 })
45
46 return processed_tweets
47 else:
48 print(f"Error: {response.status_code} - {response.text}")
49 return []
50
51# Example usage: Fetch Trump's tweets about elections
52election_keywords = ["election", "vote", "voting", "ballot", "democracy"]
53trump_election_tweets = fetch_trump_tweets_by_topic(election_keywords, max_items=500)
54
55# Convert to DataFrame for analysis
56df = pd.DataFrame(trump_election_tweets)
57
58print(f"Found {len(df)} tweets from Trump about elections")
59print(f"Average likes per tweet: {df['favorite_count'].mean():.1f}")
60print(f"Average retweets per tweet: {df['retweet_count'].mean():.1f}")Time-Range Analysis
Answer: Time-Range Analysis means using TwexAPI Bearer APIs on api.twexapi.io for this user case—typically
14 credits per read ($0.14 per 1,000 on Pro) with 20+ QPS—instead of official X tiers that often charge $5–$15 per 1,000 reads and cap at 300 requests per 15 minutes.
You can also analyze Trump's tweeting patterns over time using advanced search queries:
1def analyze_trump_tweets_timeline(days_back=30):
2 """
3 Analyze Trump's tweeting frequency and engagement over time
4 """
5 TOKEN = "<your_bearer_token_here>"
6 url = "https://api.twitterxapi.com/twitter/advanced_search"
7
8 # Get tweets from the last 30 days
9 end_date = datetime.now()
10 start_date = end_date - timedelta(days=days_back)
11
12 # Format dates for Twitter search (YYYY-MM-DD)
13 start_str = start_date.strftime("%Y-%m-%d")
14 end_str = end_date.strftime("%Y-%m-%d")
15
16 search_query = f"from:realDonaldTrump since:{start_str} until:{end_str}"
17
18 payload = {
19 "searchTerms": [search_query],
20 "maxItems": 10000, # Get as many as possible
21 "sortBy": "Latest"
22 }
23
24 headers = {
25 "Authorization": f"Bearer {TOKEN}",
26 "Content-Type": "application/json"
27 }
28
29 response = requests.post(url, headers=headers, data=json.dumps(payload))
30
31 if response.status_code == 200:
32 data = response.json()
33 tweets = data["data"]
34
35 # Analyze by day
36 daily_stats = {}
37 for tweet in tweets:
38 # Extract date from created_at
39 tweet_date = datetime.strptime(tweet['created_at'], "%a %b %d %H:%M:%S +0000 %Y").date()
40
41 if tweet_date not in daily_stats:
42 daily_stats[tweet_date] = {
43 'count': 0,
44 'total_likes': 0,
45 'total_retweets': 0
46 }
47
48 daily_stats[tweet_date]['count'] += 1
49 daily_stats[tweet_date]['total_likes'] += tweet['favorite_count']
50 daily_stats[tweet_date]['total_retweets'] += tweet['retweet_count']
51
52 # Print daily summary
53 for date, stats in sorted(daily_stats.items()):
54 avg_likes = stats['total_likes'] / stats['count'] if stats['count'] > 0 else 0
55 avg_retweets = stats['total_retweets'] / stats['count'] if stats['count'] > 0 else 0
56
57 print(f"{date}: {stats['count']} tweets, "
58 f"avg {avg_likes:.1f} likes, "
59 f"avg {avg_retweets:.1f} retweets")
60
61 return daily_stats
62 else:
63 print(f"Error: {response.status_code} - {response.text}")
64 return {}
65
66# Run the analysis
67timeline_stats = analyze_trump_tweets_timeline(days_back=30)Real-time Trump Tweet Monitoring
Answer: Real-time Trump Tweet Monitoring means using TwexAPI Bearer APIs on api.twexapi.io for this user case—typically
14 credits per read ($0.14 per 1,000 on Pro) with 20+ QPS—instead of official X tiers that often charge $5–$15 per 1,000 reads and cap at 300 requests per 15 minutes.
For real-time monitoring of Trump's tweets, you can set up a scheduled script:
1import time
2import schedule
3from datetime import datetime
4
5def monitor_trump_tweets():
6 """
7 Monitor for new Trump tweets and alert on high engagement
8 """
9 def check_for_new_tweets():
10 TOKEN = "<your_bearer_token_here>"
11 url = "https://api.twitterxapi.com/twitter/advanced_search"
12
13 payload = {
14 "searchTerms": ["from:realDonaldTrump"],
15 "maxItems": 10, # Just check the latest 10
16 "sortBy": "Latest"
17 }
18
19 headers = {
20 "Authorization": f"Bearer {TOKEN}",
21 "Content-Type": "application/json"
22 }
23
24 response = requests.post(url, headers=headers, data=json.dumps(payload))
25
26 if response.status_code == 200:
27 data = response.json()
28 tweets = data["data"]
29
30 # Check for recent high-engagement tweets
31 for tweet in tweets:
32 engagement = tweet['favorite_count'] + tweet['retweet_count']
33 tweet_time = datetime.strptime(tweet['created_at'], "%a %b %d %H:%M:%S +0000 %Y")
34
35 # Alert if tweet is less than 1 hour old and has high engagement
36 if (datetime.utcnow() - tweet_time).total_seconds() < 3600 and engagement > 10000:
37 print(f"🚨 HIGH ENGAGEMENT TRUMP TWEET!")
38 print(f"Text: {tweet['text']}")
39 print(f"Engagement: {engagement}")
40 print(f"Time: {tweet['created_at']}")
41 print(f"Link: https://twitter.com/realDonaldTrump/status/{tweet['tweet_id']}")
42 print("-" * 50)
43
44 # Schedule checks every 15 minutes
45 schedule.every(15).minutes.do(check_for_new_tweets)
46
47 print("Starting Trump tweet monitoring...")
48 while True:
49 schedule.run_pending()
50 time.sleep(1)
51
52# Start monitoring
53# monitor_trump_tweets()Advanced Search Query Examples
Answer: Advanced Search Query Examples means using TwexAPI Bearer APIs on api.twexapi.io for this user case—typically
14 credits per read ($0.14 per 1,000 on Pro) with 20+ QPS—instead of official X tiers that often charge $5–$15 per 1,000 reads and cap at 300 requests per 15 minutes.
Here are some powerful search queries you can use for different analysis purposes:
1# Different types of search queries for Trump's tweets
2search_queries = {
3 "Election related": "from:realDonaldTrump (election OR vote OR voting OR ballot)",
4 "Policy tweets": "from:realDonaldTrump (policy OR law OR bill OR congress)",
5 "Media mentions": "from:realDonaldTrump (fake news OR media OR press OR CNN OR Fox)",
6 "High engagement": "from:realDonaldTrump min_retweets:1000",
7 "Recent tweets": "from:realDonaldTrump since:2024-01-01",
8 "Tweets with images": "from:realDonaldTrump filter:images",
9 "Tweets with videos": "from:realDonaldTrump filter:videos"
10}
11
12def run_multiple_searches(queries_dict, max_items=100):
13 """
14 Run multiple search queries and compare results
15 """
16 results = {}
17
18 for query_name, search_term in queries_dict.items():
19 print(f"Running search: {query_name}")
20
21 payload = {
22 "searchTerms": [search_term],
23 "maxItems": max_items,
24 "sortBy": "Latest"
25 }
26
27 # Make API call (using your existing function)
28 tweets = fetch_tweets(payload) # Your API call function
29
30 results[query_name] = {
31 'count': len(tweets),
32 'total_engagement': sum(t['favorite_count'] + t['retweet_count'] for t in tweets),
33 'avg_engagement': sum(t['favorite_count'] + t['retweet_count'] for t in tweets) / len(tweets) if tweets else 0
34 }
35
36 return results
37
38# Run comparative analysis
39analysis_results = run_multiple_searches(search_queries)
40
41for query_name, stats in analysis_results.items():
42 print(f"{query_name}: {stats['count']} tweets, avg engagement: {stats['avg_engagement']:.1f}")Potential Use Cases and Tips
Answer: Potential Use Cases and Tips means using TwexAPI Bearer APIs on api.twexapi.io for this user case—typically
14 credits per read ($0.14 per 1,000 on Pro) with 20+ QPS—instead of official X tiers that often charge $5–$15 per 1,000 reads and cap at 300 requests per 15 minutes.
Use Cases:
- Election Monitoring: Search "from:realDonaldTrump election" to track campaign-related posts.
- Policy Analysis: Monitor statements on specific policies or legislation.
- Trend Analysis: Combine with date filters for historical data comparison.
- Automated Alerts: Set up scripts to notify on new tweets matching criteria.
- Sentiment Analysis: Analyze language patterns and emotional tone over time.
- Media Research: Study how Trump responds to news events or media coverage.
Best Practices:
- Use the Advance Search tool for complex queries (e.g., "from:realDonaldTrump until:2024-10-01 since:2024-01-01").
- Mind rate limits—check your plan on the TwitterXApi dashboard.
- For massive datasets, break requests into smaller maxItems to avoid timeouts.
- Cache frequently accessed data to reduce API calls.
- Implement error handling and retry logic for robust applications.
Error Handling:
1def robust_trump_tweet_search(search_terms, retries=3):
2 """
3 Robust API call with retry logic
4 """
5 for attempt in range(retries):
6 try:
7 payload = {
8 "searchTerms": search_terms,
9 "maxItems": 100,
10 "sortBy": "Latest"
11 }
12
13 response = requests.post(url, headers=headers, data=json.dumps(payload), timeout=30)
14
15 if response.status_code == 200:
16 return response.json()
17 elif response.status_code == 429: # Rate limit
18 print(f"Rate limit hit, waiting 60 seconds...")
19 time.sleep(60)
20 else:
21 print(f"Error {response.status_code}: {response.text}")
22
23 except requests.exceptions.RequestException as e:
24 print(f"Request failed (attempt {attempt + 1}): {e}")
25 if attempt < retries - 1:
26 time.sleep(5)
27
28 return NoneConclusion
TwitterXApi's Advanced Twitter Search endpoint is a versatile tool for fetching targeted data like Trump's tweets, empowering developers and analysts to uncover insights from X. Whether you're tracking political discourse, analyzing communication patterns, or building monitoring systems, this API provides the flexibility and scale you need.
The advanced search syntax allows for precise targeting, while the high volume limits (up to 100,000 tweets per request) make it suitable for comprehensive analysis. Combined with Python's data processing libraries, you can build sophisticated analysis tools that would be difficult or expensive to create with other APIs.
Getting Started
- Sign up at TwitterXApi.com
- Get your Bearer token from the dashboard
- Start with simple queries like "from:realDonaldTrump"
- Gradually build more complex search terms and analysis
Next Steps
- Explore combining multiple search terms for comprehensive analysis
- Integrate with data visualization libraries like Matplotlib or Plotly
- Consider implementing webhook-based real-time alerts
- Scale your solution with cloud infrastructure for enterprise use
Give it a shot and start analyzing today! If you have questions or project ideas, comment below or connect on X. 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 and research purposes only. Respect platform terms when scraping data and ensure compliance with applicable laws and regulations.