
Extract public Reel data from Instagram using a list URL, including creator usernames, titles, hashtags, engagement metrics, and more. Outputs structured data and can be integrated into automation workflows or third-party systems.
The Instagram Comments Scraper lets you extract structured comment data from public Instagram posts using one or more Post URLs. Just paste the target links, run the scraper, and collect comments, replies, and engagement metrics in a structured format. It is built for user feedback analysis, brand reputation monitoring, competitor research, and comment data collection at scale.
Compared with manually reading comment threads, this scraper makes it much faster to collect public Instagram comment data at scale and turn it into structured output for analysis, monitoring, and reporting.
| 💬 Comment text | 👤 Commenter username |
| 🔗 Commenter profile URL | 📅 Comment timestamp |
| ❤️ Comment likes | ↩️ Reply count |
| 🧵 Reply content | #️⃣ Hashtags used in comments |
| 🆔 Comment ID | 🆔 Post ID |
| 👤 Original poster | 🔗 Post URL |
The Instagram Comments Scraper is designed to be simple to use, so you can start extracting data quickly even without prior scraping experience.
Enter the Instagram Post URL you want to scrape comments from. Example URL: https://www.instagram.com/reel/DWK3Ku9jDpZ/?igsh=MTl5aTllaXd0ZXZiNQ==
This scraper is best for cases where you already know the exact post you want to analyze and need a fast way to extract comment-thread data, such as user feedback analysis, competitor comment research, or brand reputation monitoring.
Supported input:
post_url: The Instagram post URL to scrape comments from.Recommended inputs: Brand post URLs, competitor post URLs, trending content URLs Not recommended: Profile URLs, Reel listing pages, private post links, or invalid redirect URLs

When the run is complete, the collected Instagram comment data is organized under the Output tab as a structured dataset. For easier review, results are typically shown in a table-like view and can be downloaded in CSV or JSON format.

Yes. Because it captures comment text, likes, replies, and nested interaction data, it works well for sentiment analysis, complaint detection, keyword clustering, and broader audience feedback research.
Yes. The current field set includes both reply counts and reply content, which makes it useful for analyzing deeper discussion threads rather than only surface-level comments.
A post scraper focuses on the post itself, such as captions, media, and top-level engagement metrics. A comments scraper is centered on audience feedback, comment content, and reply structure, making it better for conversation-level analysis.
Yes. This scraper is a strong fit for social listening because it can extract public comments, nested replies, and interaction signals that support trend analysis and public opinion monitoring.
Yes. Since the output includes comment text and hashtags used in comments, it can support filtering, topic detection, and targeted comment analysis around specific themes.
CSV is ideal for Excel, Google Sheets, and BI dashboards. JSON is better for databases, scripts, NLP pipelines, and automated analytics workflows.
Yes. The current tool description indicates that each request retrieves live public Instagram data rather than relying on cached output.
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