Twitter Comments Scraper lets you extract comments and user details from any tweet URL. This tool is perfect for marketers and product owners aiming to reach potential leads directly by analyzing interactions on Twitter. It simplifies collecting social insights while saving time.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Twitter Comments Scraper enables automated extraction of comments and associated user information from tweets. It helps solve the problem of manually tracking engagement, making it ideal for marketers, social analysts, and product teams looking to identify potential audiences.
- Requires Twitter cookies to authenticate and use existing session for scraping.
- Customizable and randomizable delay between scraping pages to avoid rate limits.
- Easy export of Twitter cookies using the Chrome Cookie-Editor extension.
- Works seamlessly for extracting detailed user engagement data.
- Provides structured output ready for further analysis or marketing outreach.
| Feature | Description |
|---|---|
| Comment Extraction | Collect all comments for a given tweet including replies. |
| User Details | Extract profile information such as username, followers, bio, and verified status. |
| Custom Delay | Randomize delay between page requests to reduce detection risk. |
| Easy Cookie Integration | Use exported Twitter cookies to authenticate without login. |
| Output Formatting | Structured JSON output for easy analysis and integration. |
| Field Name | Field Description |
|---|---|
| conversation_id_str | Unique identifier for the tweet conversation. |
| full_text | Text content of the comment. |
| user.screen_name | Twitter handle of the user. |
| user.name | Full name of the user. |
| user.description | Bio of the user. |
| user.followers_count | Number of followers the user has. |
| user.friends_count | Number of accounts the user follows. |
| favorite_count | Number of likes for the comment. |
| reply_count | Number of replies to the comment. |
| retweet_count | Number of retweets for the comment. |
| created_at | Timestamp when the comment was posted. |
| entities.urls | URLs mentioned in the comment. |
[
{
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Fri Jul 14 12:15:36 +0000 2023",
"conversation_id_str": "1679808034837774336",
"display_text_range": [0, 105],
"entities": {
"user_mentions": [],
"urls": [
{
"display_url": "the-viral-supplement.ck.page/signup",
"expanded_url": "https://the-viral-supplement.ck.page/signup",
"url": "https://t.co/EHVtpm5BS3",
"indices": [82, 105]
}
],
"hashtags": [],
"symbols": []
},
"favorite_count": 2,
"favorited": false,
"full_text": "Did someone say free viral Tweet breakdowns in your inbox every week?\n\nJoin here👇\nhttps://t.co/EHVtpm5BS3",
"in_reply_to_screen_name": "_danreynolds_",
"reply_count": 2,
"retweet_count": 0,
"user": {
"id_str": "1158101960",
"name": "— Daniel",
"screen_name": "_danreynolds_",
"location": "FREE viral tweet breakdowns ↓",
"description": "Quit my city job to write online // helping you find freedom in writing and business // Ghostwriting personal brands at SocialScaler",
"followers_count": 2601,
"friends_count": 399,
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1629598305838678016/2qaaBYMb_normal.jpg"
}
}
]
twitter-comments-scraper/
├── src/
│ ├── runner.py
│ ├── extractors/
│ │ ├── twitter_parser.py
│ │ └── utils_time.py
│ ├── outputs/
│ │ └── exporters.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.txt
│ └── sample.json
├── requirements.txt
└── README.md
- Marketers use it to collect potential leads from Twitter interactions, so they can run targeted campaigns efficiently.
- Social analysts use it to track user engagement trends, so they can generate insights on audience behavior.
- Product owners use it to identify interested users, so they can directly communicate product updates.
- Influencers use it to analyze engagement on their tweets, so they can optimize content strategy.
Q: Do I need a Twitter account to use this scraper? A: Yes, you need a Twitter account to export cookies which the scraper uses to authenticate requests.
Q: Can this scraper handle large tweet threads? A: Yes, it can scrape extended conversations, including replies and nested comments, with configurable delays.
Q: Is the output compatible with data analysis tools? A: Absolutely, the JSON output is structured for easy integration with analytics or marketing automation tools.
Q: Are there any rate limits? A: The scraper includes randomized delays to minimize hitting Twitter rate limits, but excessive usage may still require session management.
Primary Metric: Average scraping speed of 50–100 comments per minute depending on tweet size. Reliability Metric: Maintains over 95% success rate for authenticated sessions with valid cookies. Efficiency Metric: Low CPU usage and minimal memory footprint, allowing concurrent scraping tasks. Quality Metric: Extracted user and comment data is complete and accurate, preserving all essential fields for analysis.
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