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This guide covers how to search for tweets and users, discover trending topics, and explore what’s popular on Twitter.

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Searching tweets

Search for tweets using keywords, hashtags, or advanced search operators. 
tweets = await client.search_tweet(
    query='python programming',
    product='Top',  # Options: 'Top', 'Latest', 'Media'
    count=20
)

for tweet in tweets:
    print(f'@{tweet.user.screen_name}: {tweet.text}')
    print(f'Likes: {tweet.favorite_count}, Retweets: {tweet.retweet_count}')
    print('---')

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Search products

Twikit supports three types of tweet searches: 
# Get most relevant and popular tweets
tweets = await client.search_tweet('AI', product='Top', count=20)

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Pagination through search results

# Initial search
results = await client.search_tweet('machine learning', product='Latest', count=20)

for tweet in results:
    print(tweet.text)

# Get next page of results
more_results = await results.next()
for tweet in more_results:
    print(tweet.text)

# Get previous page
previous_results = await results.previous()

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Advanced search queries

Use Twitter’s search operators for more precise results: 
# Search with hashtags
tweets = await client.search_tweet('#python', product='Top')

# Search from specific user
tweets = await client.search_tweet('from:elonmusk', product='Latest')

# Search mentions
tweets = await client.search_tweet('to:twitter', product='Latest')

# Search with multiple keywords (AND)
tweets = await client.search_tweet('python AND machine learning', product='Top')

# Search with OR operator
tweets = await client.search_tweet('python OR javascript', product='Top')

# Exclude terms
tweets = await client.search_tweet('python -java', product='Top')

# Search for phrases
tweets = await client.search_tweet('"artificial intelligence"', product='Top')

# Search tweets with links
tweets = await client.search_tweet('python filter:links', product='Latest')

# Search tweets with media
tweets = await client.search_tweet('sunset filter:media', product='Latest')

# Search by language
tweets = await client.search_tweet('bonjour lang:fr', product='Latest')

# Search tweets with minimum engagement
tweets = await client.search_tweet('python min_faves:100', product='Top')
tweets = await client.search_tweet('python min_retweets:50', product='Top')
You can combine multiple search operators for more specific queries:
query = 'python OR javascript from:github -spam filter:links min_faves:10'
tweets = await client.search_tweet(query, product='Latest')

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Searching users

Find users based on their profile information. 
users = await client.search_user('python developer', count=20)

for user in users:
    print(f'@{user.screen_name} - {user.name}')
    print(f'Bio: {user.description}')
    print(f'Followers: {user.followers_count}')
    print('---')

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Pagination through user search results

results = await client.search_user('data scientist', count=20)

# Get more results
more_results = await results.next()

for user in more_results:
    print(f'@{user.screen_name}')

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Filter and sort search results

results = await client.search_user('AI researcher', count=50)

# Filter users by follower count
popular_users = [u for u in results if u.followers_count > 1000]

# Sort by followers
sorted_users = sorted(results, key=lambda u: u.followers_count, reverse=True)

print('Top 10 users by followers:')
for user in sorted_users[:10]:
    print(f'@{user.screen_name}: {user.followers_count} followers')
Explore trending topics and hashtags on Twitter. 
trends = await client.get_trends(
    category='trending',  # Options: 'trending', 'for-you', 'news', 'sports', 'entertainment'
    count=20
)

for trend in trends:
    print(f'Trend: {trend.name}')
    if trend.tweets_count:
        print(f'  Tweets: {trend.tweets_count}')
    print(f'  Domain: {trend.domain_context}')

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Trend categories

Explore different types of trends: 
# Get what's trending overall
trends = await client.get_trends('trending', count=20)
Once you find a trending topic, search for related tweets: 
trends = await client.get_trends('trending', count=10)

for trend in trends:
    print(f'\nSearching for: {trend.name}')
    
    # Search tweets about this trend
    tweets = await client.search_tweet(trend.name, product='Latest', count=10)
    
    for tweet in tweets:
        print(f'  @{tweet.user.screen_name}: {tweet.text[:50]}...')
Get trends for specific geographic locations.

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Get available locations

locations = await client.get_available_locations()

for location in locations:
    print(f'{location.name}, {location.country} (WOEID: {location.woeid})')

# Get trends for a specific location using WOEID
# WOEID for New York: 2459115
# WOEID for London: 44418
# WOEID for Tokyo: 1118370

nyc_trends = await client.get_place_trends(2459115)

print(f"Trending in {nyc_trends['locations'][0]['name']}:")
for trend in nyc_trends['trends']:
    print(f"  {trend.name}")
    if trend.tweet_volume:
        print(f"    Volume: {trend.tweet_volume} tweets")

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Location object methods

locations = await client.get_available_locations()
nyc = next(loc for loc in locations if loc.name == 'New York')

print(f'Location: {nyc.name}')
print(f'Country: {nyc.country}')
print(f'WOEID: {nyc.woeid}')

# Get trends for this location
trends = await nyc.get_trends()

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Similar tweets (Premium)

Find tweets similar to a specific tweet. 
tweet = await client.get_tweet_by_id('1234567890')

# Get similar tweets
similar_tweets = await tweet.get_similar_tweets()

print(f'Original tweet: {tweet.text}')
print(f'\nSimilar tweets:')
for similar in similar_tweets:
    print(f'  @{similar.user.screen_name}: {similar.text}')
The get_similar_tweets() method requires Twitter Premium/Blue subscription.

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Practical examples

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Monitor keywords in real-time

import asyncio

async def monitor_keyword(keyword):
    seen_tweet_ids = set()
    
    while True:
        try:
            tweets = await client.search_tweet(
                query=keyword,
                product='Latest',
                count=20
            )
            
            for tweet in tweets:
                if tweet.id not in seen_tweet_ids:
                    print(f'New tweet about {keyword}:')
                    print(f'  @{tweet.user.screen_name}: {tweet.text}')
                    seen_tweet_ids.add(tweet.id)
            
            # Check every 60 seconds
            await asyncio.sleep(60)
        except Exception as e:
            print(f'Error: {e}')
            await asyncio.sleep(60)

# Monitor a specific keyword
await monitor_keyword('Python')

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Find influencers in a niche

users = await client.search_user('AI researcher', count=50)

# Filter and sort by engagement
influencers = sorted(
    [u for u in users if u.followers_count > 5000],
    key=lambda u: u.followers_count,
    reverse=True
)

print('Top AI researcher influencers:')
for i, user in enumerate(influencers[:10], 1):
    print(f'{i}. @{user.screen_name}')
    print(f'   Followers: {user.followers_count:,}')
    print(f'   Tweets: {user.statuses_count:,}')
    print(f'   Bio: {user.description[:100]}...')
    print()

import time

trending_hashtags = set()

# Get current trends
trends = await client.get_trends('trending', count=30)

for trend in trends:
    if trend.name.startswith('#'):
        trending_hashtags.add(trend.name)
        print(f'Trending: {trend.name}')
        
        # Search recent tweets with this hashtag
        tweets = await client.search_tweet(
            query=trend.name,
            product='Latest',
            count=5
        )
        
        print('  Recent tweets:')
        for tweet in tweets:
            print(f'    @{tweet.user.screen_name}: {tweet.text[:60]}...')
        print()

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Build a sentiment analysis dataset

import json

keyword = 'python programming'
tweets_data = []

# Collect tweets
results = await client.search_tweet(keyword, product='Latest', count=100)
for tweet in results:
    tweets_data.append({
        'id': tweet.id,
        'text': tweet.text,
        'user': tweet.user.screen_name,
        'likes': tweet.favorite_count,
        'retweets': tweet.retweet_count,
        'created_at': tweet.created_at
    })

# Get more tweets
for _ in range(4):  # Get 5 pages total
    try:
        results = await results.next()
        for tweet in results:
            tweets_data.append({
                'id': tweet.id,
                'text': tweet.text,
                'user': tweet.user.screen_name,
                'likes': tweet.favorite_count,
                'retweets': tweet.retweet_count,
                'created_at': tweet.created_at
            })
    except:
        break

# Save to file
with open('tweets_dataset.json', 'w') as f:
    json.dump(tweets_data, f, indent=2)

print(f'Collected {len(tweets_data)} tweets about "{keyword}"')

categories = ['trending', 'news', 'sports', 'entertainment']
trends_by_category = {}

for category in categories:
    trends = await client.get_trends(category, count=10)
    trends_by_category[category] = [t.name for t in trends]
    print(f'\n{category.upper()} trends:')
    for trend in trends:
        print(f'  {trend.name}')

# Find common trends across categories
common_trends = set(trends_by_category['trending'])
for category in categories[1:]:
    common_trends &= set(trends_by_category[category])

if common_trends:
    print(f'\nTrends appearing in all categories: {common_trends}')

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Trend attributes

Access detailed information about trends: 
trends = await client.get_trends('trending', count=5)

for trend in trends:
    print(f'Name: {trend.name}')
    print(f'Tweets count: {trend.tweets_count}')
    print(f'Domain context: {trend.domain_context}')
    print(f'Grouped trends: {trend.grouped_trends}')
    print('---')

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Best practices

1

Use specific search queries

Narrow your searches with operators to get more relevant results and reduce API calls.
2

Implement pagination wisely

Don’t fetch too many pages at once. Process results in batches to stay within rate limits.
3

Cache trend results

Trends don’t change frequently. Cache results for a few minutes to avoid unnecessary API calls.
4

Handle search errors

Some queries may return no results or cause errors. Always handle exceptions gracefully.

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