How to Scrape Twitter/X Data with Apify (2026 Guide)

Quick answer: what can you scrape from Twitter/X?

Apify's Twitter/X Scraper actors extract: tweets (text, date, likes, retweets, replies), user profiles (bio, follower count, following count, verified status), hashtags, mentions, URLs in tweets, and search results by keyword or hashtag. You can monitor brand mentions, track competitor activity, analyze sentiment, and discover trending topics — all without Twitter's expensive API ($100-$2,500/month).

For marketing teams, this means real-time brand monitoring and competitive intelligence at a fraction of Twitter API costs.

Step-by-step: scraping Twitter/X with Apify

Here is the complete workflow:

1. Create a free Apify account at apify.com — Create a free Apify account at apify.com
2. Go to Apify Store and search for "Twitter Scraper" or "X Scraper" — look for well-maintained actors with high star ratings — Go to Apify Store and search for "Twitter Scraper" or "X Scraper" — look for well-maintained actors with high star ratings
3. Click "Try for free" to open the actor configuration — Click "Try for free" to open the actor configuration
4. Choose your search type "Search" (tweets matching a keyword/hashtag), "Profile" (tweets from a specific user), or "Followers" (follower list of a user)
5. For keyword search Enter your search query, e.g., "yourbrand OR @yourbrand" to capture all mentions. Add hashtags: "#yourindustry"
6. Set parameters max results (start with 100 for testing), date range (last 7 days, 30 days, or custom), language filter
7. Click "Start" — the actor begins scraping. 100 tweets takes ~1-2 minutes — Click "Start" — the actor begins scraping. 100 tweets takes ~1-2 minutes
8. Export data Download as JSON, CSV, or Excel from the Dataset tab
9. Analyze Open in Excel/Google Sheets or feed into ChatGPT for sentiment analysis

Data fields available from Twitter/X scraper

The Apify Twitter/X Scraper extracts these fields per tweet:

• Tweet text: Full text of the tweet including emojis and links
• Tweet URL: Direct link to the tweet
• Date: Timestamp of when the tweet was posted
• Author: Username, display name, bio, follower count, verified status
• Engagement metrics: Like count, retweet count, reply count, quote tweet count, view count
• Hashtags: All hashtags used in the tweet
• Mentions: All @mentions in the tweet
• URLs: All links shared in the tweet
• Media: URLs of images or videos attached to the tweet
• Conversation ID: Thread identifier for grouping replies
• Language: Detected language of the tweet
• Geo (if available): Location data if the user opted to share it

Marketing use cases for Twitter/X scraping

Twitter/X data powers several marketing intelligence workflows:

• Brand monitoring: Search for your brand name, product names, and @handle. Track mention volume, sentiment, and engagement over time
• Competitor monitoring: Search for competitor names and handles. Compare mention volume and sentiment to your brand
• Influencer identification: Scrape tweets about your industry. Identify users with high engagement who frequently post about your topic
• Trend discovery: Scrape trending hashtags in your industry. Spot emerging topics before they peak
• Customer feedback: Search for "yourbrand" + "love" OR "hate" OR "broken" OR "amazing". Collect real customer sentiment
• Content research: Scrape popular tweets in your niche. Identify what content formats and topics generate the most engagement
• Crisis detection: Set up real-time monitoring for negative sentiment spikes. Alert your team when mention volume or negativity increases
• Hashtag research: Scrape tweets with industry hashtags. Find related hashtags and optimal posting times

Sentiment analysis with AI

Raw tweet data is just text. To make it actionable, run sentiment analysis using AI:

• Export tweets as CSV from Apify
• Import into ChatGPT or Claude with the prompt: "Analyze the sentiment of these tweets about [your brand]. Categorize each as positive, negative, or neutral. Summarize the key themes."
• For larger datasets (1,000+ tweets): Use a sentiment analysis API like OpenAI's API or Google Cloud Natural Language API
• Track sentiment over time: Run weekly scrapes, analyze sentiment, and chart the trend. A sudden negative spike indicates a PR issue
• Identify key themes: Group tweets by topic (product quality, customer service, pricing, features). This reveals what drives positive vs negative sentiment
• Competitor comparison: Run the same sentiment analysis on competitor tweets. Benchmark your sentiment ratio against theirs
• Action items: Convert negative themes into action items. If 30% of negative tweets mention "slow customer service", that is a clear operational priority

Scheduled monitoring setup

For ongoing brand monitoring, set up scheduled Apify runs:

• Go to your Twitter Scraper actor in Apify
• Click "Schedules" → "Create schedule"
• Set frequency: Every 24 hours for daily monitoring, every 6 hours for real-time monitoring
• Set the search query: "yourbrand OR @yourbrand OR #yourbrand"
• Set max results: 200-500 per run is sufficient for daily monitoring
• Enable notifications: Apify can email you when each run completes
• Export accumulated data: Download the full dataset weekly or monthly for trend analysis
• Set up alerts: Use Apify webhooks to trigger a notification (Slack, email) when mention volume exceeds a threshold

Cost comparison: Apify vs Twitter API

The cost difference is significant:

• Twitter/X API Basic: $100/month — limited to 10,000 tweets/month read
• Twitter/X API Pro: $5,000/month — 1,000,000 tweets/month read
• Apify free tier: $5/month included — enough for ~2,000-5,000 tweets/month
• Apify Starter: $49/month — enough for ~25,000-50,000 tweets/month
• Apify Scale: $499/month — enough for ~500,000+ tweets/month
• Savings: Apify Starter ($49) vs Twitter API Basic ($100) = 51% cheaper with 2.5-5x more data
• Savings: Apify Scale ($499) vs Twitter API Pro ($5,000) = 90% cheaper
• Social listening tools (Brandwatch, Mention): $800-$2,000/month. Apify + ChatGPT = $49-100/month