🏯 Twitter Replies Scraper – Fast, Reliable & Complete Reply Data Extraction

Extract X (Twitter) replies, tweet comments, reply authors, and complete engagement metrics with structured, ready-to-use JSON output. This Twitter replies scraper pulls replies straight from a Tweet ID or Tweet URL, no login, no proxy. The most affordable Apify Twitter reply scraper, priced per result so you only pay for the data you actually collect.

⚡ Input: Tweet ID or Tweet URL — no search syntax required 💰 Price: $0.0004 per dataset item + query fee ✅ Two collection modes: default replies flow or search-based conversation_id flow 📊 Data Points: 30+ fields per reply including full author profile and engagement metrics 🎯 Free Plan friendly: Demo Mode included for testing before you commit

Looking for a reliable Twitter (X) replies scraper to extract tweet comments, monitor brand mentions, or build a sentiment analysis dataset? Need to scrape replies to any tweet including reply author bios, follower counts, and engagement metrics? This Apify actor delivers complete conversation-level intelligence in one run.

📚 Table of Contents

• 🧭 What does Twitter Replies Scraper do?
• 🐉 Features and Functionality
• Important Note About Usage
• ⚡ Performance & Technical Details
• 💰 Pricing
• 🆓 Demo Mode & Free Users
• 🍚 Input Parameters
• 🍜 Output
• Search Flow vs Replies Flow
• 🍳 Custom Map Function
• 🔧 Troubleshooting & Common Issues
• 🎯 Who Needs This Twitter Replies Scraper? (Use Cases & Industries)
• 💡 How to Scrape Twitter Replies: Step-by-Step Guide
• ❓ Frequently Asked Questions (FAQ)
• Contact

🧭 What does Twitter Replies Scraper do?

The API Dojo Twitter Replies Scraper is a specialized data extraction actor built to scrape replies, comments, and conversation threads from any public tweet on X (Twitter). Give it a Tweet ID or a Tweet URL and it returns every available reply in a clean, structured dataset — without needing proxies, cookies, or a Twitter developer account.

With automatic pagination, a search-based fallback mode for deeper conversation coverage, and a custom map function for on-the-fly field transformation, this scraper gives you full visibility into how people are responding to any post. Whether you're tracking brand sentiment, researching audience reactions, or building a dataset for social listening, Twitter Replies Scraper makes it easy to collect and reshape exactly the reply data you need.

This actor is ideal for brand monitoring, sentiment analysis, audience research, and lead generation — helping marketers, community managers, analysts, and growth teams turn raw reply threads into structured, actionable data.

🐉 Features and Functionality

The Twitter Replies Scraper gives you complete, structured access to reply data on X — covering reply text, author profiles, engagement metrics, and conversation context. It's built not just for scraping, but for conversation-level intelligence — the foundation for brand monitoring, research, and growth workflows at scale.

⚡ Scrape Twitter reply data from:

• ✅ Any public Tweet — Provide a Tweet ID or Tweet URL and pull every available reply.
• ✅ Reply Author Profiles — Full profile object per reply: bio, followers, following, verification status, join date.
• ✅ Engagement Metrics — Likes, retweets, replies, quotes, views, and bookmarks for every reply.
• ✅ Conversation Context — conversationId, inReplyToId, and inReplyToUsername so you can rebuild the thread.
• ✅ Mentions & Entities — User mentions, hashtags, and other embedded entities extracted per reply.
• ✅ Reply Source — The client used to post the reply (web, Android, iOS, third-party apps).

🧠 Smart Functionalities

Capability	What It Does	Why It Matters
Tweet ID or URL Input	Accept either raw Tweet IDs or full Tweet/X URLs interchangeably.	Simplifies input whether you're pulling IDs from an API or copy-pasting links.
Dual Collection Modes	Default replies endpoint, or useSearch: true for a conversation_id: search-based flow.	Lets you fall back to a broader collection method when the default flow misses replies.
maxItems Control	Cap total replies collected per run.	Keeps costs predictable for both quick tests and full-scale pulls.
Custom Map Function	Pass a JS function to reshape, rename, or filter fields in every output row.	Get exactly the schema your downstream tool or database expects, with zero post-processing.
Structured Data Output	JSON / CSV / Excel / XML / RSS ready for dashboards, CRMs, or automation platforms.	Instant integration with your existing tools.
No Proxy Required	Runs smoothly out of the box without additional proxy configuration.	Less setup, more scraping.
Free Plan Demo Mode	Run the actor up to 5 times per month on Apify's Free Plan, each run capped at 10 items.	Test the actor and validate the output format at zero cost.

🧭 Complete Your Twitter Data Stack

Need comprehensive X (Twitter) intelligence? The Twitter Replies Scraper works seamlessly alongside other specialized extraction tools to create a complete data collection ecosystem. Each actor targets a unique layer of Twitter — from search and profiles to user accounts and reply threads.

By combining these purpose-built scrapers, you can construct multi-dimensional datasets for brand monitoring, competitive research, audience analysis, and social listening. Choose the tools that match your specific use case, or deploy them together for maximum coverage.

🏯 Tweet Scraper	🏯 Twitter Scraper Lite	🏯 Twitter (X) User Scraper
💬 Twitter Replies Scraper	👤 Twitter Profile Scraper	📋 Twitter List Scraper

Important Note About Usage

This scraper is designed to fetch replies from real, public tweets. To ensure stable performance and fair usage, please follow these rules:

• The tweet must exist and be public. Deleted, protected, or invalid Tweet IDs/URLs are not supported.
• The tweet should have replies. Scraping tweets with zero replies returns an empty dataset and wastes run time.
• Use useSearch: true if the default replies flow returns fewer results than expected — the search-based flow can surface additional conversation tweets.

⚡ Performance & Technical Details

The API Dojo Twitter Replies Scraper is engineered to deliver fast, reliable, and consistent reply extraction at scale. With flexible input options, a search-based fallback mode, and a custom map function, it handles everything from a single-thread pull to large conversation-monitoring jobs.

🚀 Key Technical Highlights

⚡ Feature	📊 Specification	🧾 Description
🚀 Dual Input Methods	Tweet ID or Tweet URL	Flexible configuration for streamlined data collection
🔁 Dual Collection Modes	Default replies flow / search-based flow	Broader coverage when the standard endpoint falls short
🛡️ No Proxy Required	Stable out-of-the-box	Runs reliably without additional proxy setup
📦 Structured Output	JSON / CSV / Excel / XML / RSS	Export-ready format for CRMs, databases, and analytics tools
⚙️ Custom Configuration	maxItems, customMapFunction	Fine-tune extraction and reshape output to match specific requirements
🆓 Free Plan Demo Mode	5 runs / month, 10 items / run capped	Validate output before committing to a paid plan
📊 Rich Data Fields	30+ fields per reply	Full reply text, author profile, engagement, and conversation metadata

🧭 Infrastructure Reliability

This actor is built for production-grade reply scraping with enterprise-level stability. Its dual collection modes and automatic pagination maintain consistent coverage across thousands of runs, ensuring reliable performance even during high-volume monitoring jobs. Perfect for continuous brand tracking, ongoing sentiment analysis, and integration with social listening platforms.

💰 Pricing

The Apify Twitter Replies Scraper uses a transparent, pay-per-result pricing model — you're charged for the dataset items you collect plus a small per-query fee, depending on which collection flow you use.

📊 Pricing Overview

💵 Pricing Item	🧮 Price	🧭 Description
📦 Dataset Item	$0.0004	Charged for each reply added to the dataset
💬 Replies Query	$0.014	Charged for each query made using the default replies flow (replies-query)
🔎 Search Query	$0.016	Charged for each query made using the search-based flow (useSearch: true, search-query)

🎁 Free Results for Paying Users

On top of the per-item pricing, paid-plan users get an additional free allowance per query, roughly the same size (~40 items) on either flow: the first page of results is free on the default replies flow, and the first two pages are free on the search-based flow (useSearch: true) — the flows just use different page sizes. Dataset-item charges only start applying after that.

💵 Understanding Your Costs

• Single Tweet, default flow: One tweetIds entry with useSearch off costs $0.014 for the replies query (replies-query). On a paid plan, the first page of replies (~40 items) is free — you're only charged $0.0004 per item once you go past it.
• Single Tweet, search flow: One tweetIds entry with useSearch: true costs $0.016 for the search query (search-query). On a paid plan, the first two pages (~40 items) are free — $0.0004 per item applies after that.
• Batch run (10 Tweet IDs), default flow: 10 replies queries (replies-query) cost 10 × $0.014 = $0.14, plus $0.0004 per item collected beyond each query's free first page (~40 items each).
• Free Plan Demo run: No separate query or item charges — capped at 10 items per run, up to 5 runs per month.

🧭 Why It Works So Well

• ✅ Pay per result, with transparent per-item and per-query pricing.
• 🚀 Scale from a single-thread reply pull to ongoing brand-monitoring jobs seamlessly.
• 🎁 Paid users get ~40 free items per query on either flow — the first page on the default flow, or the first two pages on the search flow.
• 🆓 Free Plan Demo Mode lets you test the actor (5 runs/month, 10 items/run) before subscribing.

🆓 Demo Mode & Free Users

Users on the Free Plan can use the actor only in Demo Mode. Free users can run the actor up to 5 times per month, with each run capped at a maximum of 10 items — just enough to test it out. Paid-plan users are not subject to this cap and additionally get ~40 items free per query (the first page on the default flow, or the first two pages on the search-based flow). To use this actor without limitations, subscribe to a paid plan on Apify.

🍚 Input Parameters

The Twitter Replies Scraper offers streamlined configuration for extracting replies from any tweet — with flexible input methods and a powerful custom transformation option.

🧩 Field	📝 Type	📖 Description	🪄 Default Value
startUrls	Array	Twitter (X) URLs to pull replies from. Required if tweetIds is empty.	[]
tweetIds	Array	Tweet IDs to pull replies from. Required if startUrls is empty.	[]
useSearch	Boolean	Switches reply fetching to the search-based conversation_id: flow instead of the default replies flow.	false
maxItems	Number	Maximum number of replies to receive as output. Leave empty for unlimited.	Infinity
customMapFunction	String	JavaScript function that reshapes, renames, or filters fields on every output row. Must return an object.	—

⚡ Supported Input Types

• 🔢 Tweet IDs — Direct numeric tweet identifiers (e.g., 1981345445986402472)
• 🔗 Tweet URLs — Full X/Twitter status links (e.g., https://x.com/Nike/status/1981345445986402472)

📌 Important: The target tweet must exist, be public, and have at least one reply. If the default flow returns fewer replies than expected, enable useSearch for broader coverage.

🍜 Output

The Twitter Replies Scraper returns comprehensive, structured JSON data for every reply — full reply text, author profile, engagement metrics, and conversation context, ready to analyze or feed into your own pipeline.

📦 Example Output Object

{
    "type": "reply",
    "id": "1981347133442888040",
    "url": "https://x.com/Trueblue1123134/status/1981347133442888040",
    "twitterUrl": "https://twitter.com/Trueblue1123134/status/1981347133442888040",
    "text": "@Nike W",
    "fullText": "@Nike W",
    "source": "Twitter for Android",
    "retweetCount": 0,
    "replyCount": 0,
    "likeCount": 1,
    "quoteCount": 0,
    "viewCount": 147,
    "createdAt": "Thu Oct 23 13:09:02 +0000 2025",
    "lang": "und",
    "bookmarkCount": 0,
    "isReply": true,
    "inReplyToId": "1981345445986402472",
    "conversationId": "1981345445986402472",
    "inReplyToUserId": "415859364",
    "inReplyToUsername": "Nike",
    "author": {
        "type": "user",
        "userName": "Trueblue1123134",
        "url": "https://x.com/Trueblue1123134",
        "id": "1975160847581560834",
        "name": "Hello",
        "isVerified": false,
        "isBlueVerified": false,
        "followers": 14,
        "following": 97,
        "createdAt": "Mon Oct 06 11:27:05 +0000 2025",
        "favouritesCount": 15,
        "mediaCount": 6,
        "statusesCount": 55
    },
    "entities": {
        "user_mentions": [
            {
                "id_str": "415859364",
                "name": "Nike",
                "screen_name": "Nike"
            }
        ]
    },
    "isRetweet": false,
    "isQuote": false,
    "media": []
}

🧭 Output Structure Highlights

🪄 Field	📖 Description
id, text, fullText	Unique reply identifier and full reply text
retweetCount, likeCount, viewCount	Complete engagement metrics for performance analysis
conversationId, inReplyToId	Thread context so you can rebuild the full conversation
author	Full author profile: username, followers, verification status, account age
entities	Mentions and other embedded entities within the reply
createdAt	Timestamp of the reply for temporal / trend analysis
url, twitterUrl	Direct links to the original reply on X and Twitter domains

All reply data exports in JSON, CSV, Excel, XML, or RSS — ready for immediate integration with CRMs, sentiment-analysis pipelines, data warehouses, or custom dashboards. Perfect for brand monitoring tools, audience research workflows, and lead-generation lists.

Search Flow vs Replies Flow

By default, the actor uses the Twitter replies endpoint to retrieve replies directly.

When useSearch is enabled, the actor instead performs a Twitter search using:

conversation_id:<tweetId>

Example:

conversation_id:1981345445986402472

This method may return a broader set of conversation tweets and can sometimes retrieve results that are not available through the standard replies endpoint — useful when the default flow returns thinner results than you expect.

🍳 Custom Map Function

Use this function to reshape the output of every row you get back from this actor. It receives each row as an argument, so you can rename fields, change formatting, or pick only the attributes you want in the final output.

The return value of this function has to be an object.

Example:

(object) => {
    return {
        username: object.author.userName,
        replyText: object.fullText,
        likes: object.likeCount + " likes",
    };
}

This example will:

• Add a new field username
• Add a new field replyText
• Reformat the like count into a new field likes

Result:

{
    "username": "Trueblue1123134",
    "replyText": "@Nike W",
    "likes": "1 likes"
}

You can use the function to:

• Add new fields
• Change existing fields
• Select only the fields you want in the output

🔧 Troubleshooting & Common Issues

Encountering issues with the Twitter Replies Scraper? Below are solutions to common problems based on actual actor configuration and user feedback.

❓ Getting Few Replies? (Low Result Count)

Problem: The scraper returns fewer replies than you expected to see on the tweet.

Solution: Check the maxItems field first — if it's set too low, the run stops early. Then try enabling useSearch: true. The search-based conversation_id: flow sometimes surfaces replies the default replies endpoint misses, especially on tweets with very large or old conversation threads.

Example configuration:

{
    "tweetIds": ["1981345445986402472"],
    "useSearch": true,
    "maxItems": 500
}

📂 Are Some Reply Fields Missing? (Incomplete Data)

Problem: Some output fields appear empty, or the Console preview doesn't show everything you expected.

Solution: The Apify Console preview only displays a subset of fields. To access the complete reply data:

• Navigate to the "Storage" tab in the Apify Console.
• Choose either "Download the results" (JSON, CSV, or Excel with all fields) or "Open in a New Tab" to view the full dataset in-browser.

⚠️ Getting No Results? (Zero Data Returned)

Problem: The actor runs but returns 0 results.

Solution: Confirm that:

• The Tweet ID or URL is valid, public, and actually has replies. Test it manually on x.com first.
• You haven't mistyped the Tweet ID — copy it directly from the tweet URL.
• The tweet isn't from a protected/private account — only public tweets can be scraped.

Start with a known tweet that has plenty of replies to verify the scraper works, then move to your target tweets.

🚨 Actor Run Failed or Shows Errors?

Problem: The actor stops with an error status or shows failure messages.

Solution: Check the Log tab in the Apify Console for specific error messages. Verify your input JSON syntax — invalid JSON causes immediate failure. Test with a minimal input (one Tweet ID, low maxItems) to isolate the issue.

📧 Need Additional Help?

If you've tried the solutions above and still experience issues with reply scraping:

Contact Support: apidojo10@gmail.com — You name it, we get it.

🎯 Who Needs This Twitter Replies Scraper? (Use Cases & Industries)

The API Dojo Twitter Replies Scraper is a specialized X reply data extraction tool built for professionals who need structured, conversation-level intelligence. Whether you're focused on brand monitoring, sentiment analysis, lead generation, or community discovery — this Twitter replies scraper turns raw reply threads into actionable business insight.

📢 Brand Monitoring & Social Listening Teams

Use Twitter Replies Scraper to track how audiences respond to brand tweets in real time. Social listening teams pull every reply to a brand's own posts, or to a campaign hashtag's top tweet, to catch complaints, praise, and viral moments early.

Key capabilities:

• Scrape replies to any brand or competitor tweet to monitor sentiment as it unfolds
• Extract reply author profiles to distinguish real customers from bots and spam accounts
• Track engagement metrics on replies to surface the most-amplified reactions
• Feed reply text directly into sentiment-analysis pipelines
• Monitor product-launch tweets for early customer feedback

Example: Scrape all replies to a product-launch tweet within the first 24 hours to flag negative sentiment spikes before they escalate into a PR issue.

📊 Sentiment Analysis & Data Science Teams

Use Twitter Replies Scraper for training and validating NLP sentiment models. Data teams love the clean, structured reply output with full text, timestamps, and engagement fields ready to feed straight into a model pipeline.

Key capabilities:

• Scrape thousands of replies across many tweets to build labeled training datasets
• Extract engagement-weighted sentiment signals (likes/views per reply)
• Analyze reply language distribution (lang field) for multilingual sentiment models
• Combine reply text with author metadata for bot-detection features
• Track sentiment shifts across a conversation over time using createdAt

Example: Scrape 10,000 replies across 50 competitor product-announcement tweets to train a sentiment classifier tuned specifically for tech-launch reactions.

🔍 Competitive Intelligence & Market Analysts

Use Twitter Replies Scraper for competitor monitoring and audience-reaction benchmarking. Analysts track how the market responds to a competitor's announcements, pricing changes, or product launches by scraping the replies underneath their tweets.

Key capabilities:

• Scrape replies to competitor announcement tweets to benchmark audience reaction
• Extract reply author follower counts to weight influential responses more heavily
• Track reply volume and engagement over time as a proxy for announcement impact
• Identify recurring complaints or praise themes across multiple competitor tweets
• Monitor conversation threads following price changes or feature releases

Example: Scrape replies to a competitor's pricing-change announcement to gauge customer backlash and inform your own positioning.

💼 Lead Generation Specialists & B2B Sales

Use Twitter Replies Scraper for B2B lead discovery inside relevant conversations. Lead-gen professionals scrape replies to industry-relevant tweets to find people actively discussing pain points your product solves.

Key capabilities:

• Scrape replies to industry-thought-leader tweets to find engaged prospects
• Extract reply author bios and follower counts to qualify leads
• Identify users repeatedly replying to competitor tweets with complaints
• Build outreach lists from reply authors discussing specific pain points
• Track which accounts consistently engage with a given topic or hashtag conversation

Example: Scrape replies to a viral tweet about a common industry problem to build a targeted list of prospects already discussing that exact pain point.

🏘️ Community Managers & Growth Teams

Use Twitter Replies Scraper for community discovery and engagement analysis. Community managers scrape replies to their own posts (or partner/collaborator posts) to find their most engaged followers and identify who to spotlight or reward.

Key capabilities:

• Scrape replies to owned content to identify top engaged community members
• Extract engagement metrics to rank the most valuable replies for reposting
• Track reply author growth (followers, following, join date) to spot rising community voices
• Identify spam or low-quality reply patterns to inform moderation rules
• Monitor reply sentiment on community announcements or AMAs

Example: Scrape all replies to a weekly community AMA thread to identify the most engaged members for a loyalty shout-out or ambassador program.

🎓 Researchers & Academic Analysts

Use Twitter Replies Scraper for social-media and discourse research. Academic researchers use structured reply data to study conversation dynamics, misinformation spread, and public reaction to news events.

Key capabilities:

• Scrape replies at scale for discourse-analysis and conversation-structure studies
• Extract structured data on reply timing, volume, and author demographics
• Track how a conversation thread evolves over hours or days using createdAt
• Analyze mention networks (user_mentions) to map conversation participants
• Build longitudinal datasets tracking reactions to recurring news events

Example: Scrape replies to 200 news-outlet tweets covering the same event to study how public reaction differs by source and phrasing.

💡 How to Scrape Twitter Replies: Step-by-Step Guide

The Twitter Replies Scraper is designed for simplicity — whether you're pulling replies from a single tweet or monitoring dozens of conversations. Follow this guide to start scraping in minutes.

🚀 Quick Start: 3 Steps to Scrape Twitter Replies

Step 1: Choose Your Input Method

The scraper accepts two input formats:

• Tweet URLs: Paste the full status link
  • Example: https://x.com/Nike/status/1981345445986402472
• Tweet IDs: Enter the numeric ID directly
  • Example: 1981345445986402472

Step 2: Configure Your Parameters

Set your extraction preferences:

• maxItems: Control total replies collected (leave empty for unlimited)
• useSearch: Enable for broader, search-based conversation coverage
• customMapFunction: Reshape output fields with custom JavaScript (optional)

Step 3: Run & Export

Click "Start" and watch the scraper extract every available reply. Export results as JSON, CSV, Excel, XML, or RSS for immediate use in your workflows.

📋 Method 1: Scrape Using Tweet URLs

Best for: Single conversations, manual lists, quick one-off pulls

{
    "startUrls": [
        "https://x.com/Nike/status/1981345445986402472"
    ],
    "maxItems": 100
}

Output: Every available reply to that tweet, including full author profile and engagement metrics.

📋 Method 2: Scrape Using Tweet IDs

Best for: Bulk operations, IDs pulled from another API or dataset

{
    "tweetIds": [
        "1981345445986402472"
    ],
    "maxItems": 100
}

🔎 Advanced: Use the Search-Based Flow for Broader Coverage

Use case: The default flow returns fewer replies than you expect, or you want to catch conversation tweets outside the standard replies endpoint

{
    "tweetIds": [
        "1981345445986402472"
    ],
    "useSearch": true,
    "maxItems": 1000
}

Result: The actor searches conversation_id:1981345445986402472 and returns a broader set of conversation tweets.

🧪 Advanced: Reshape Output with a Custom Map Function

Use case: You only need a few fields, or you want them renamed to match your own schema

{
    "tweetIds": ["1981345445986402472"],
    "maxItems": 100,
    "customMapFunction": "(object) => ({ username: object.author.userName, replyText: object.fullText, likes: object.likeCount })"
}

🔧 Best Practices for Twitter Reply Scraping

✅ DO:

• Verify the tweet exists and is public before running a large job
• Use reasonable maxItems — start with 50-100 replies for testing
• Enable useSearch when the default flow underperforms on a specific tweet
• Use customMapFunction to keep only the fields your pipeline needs, cutting downstream processing
• Test on the Free Plan's Demo Mode before committing to a paid plan

❌ DON'T:

• Scrape tweets with zero replies — this wastes run time and returns nothing
• Assume useSearch always returns more — test both flows on your specific tweet before deciding
• Use customMapFunction for filtering out required fields you'll need later — keep a superset until you're sure of your schema
• Scrape protected/private account tweets — only public tweets are accessible

❓ Frequently Asked Questions (FAQ)

Can I scrape Twitter replies without a Twitter developer account?

Yes, this Twitter Replies Scraper extracts all public reply data without requiring an X/Twitter developer account, API key, or login.

How much does it cost to scrape replies from a tweet?

You pay $0.0004 per reply collected, plus $0.014 per query on the default replies flow (or $0.016 per query if you use the search-based flow). On a paid plan, the first page of results (~40 items) per query is free, so a 500-reply pull on the default flow costs roughly (500 - 40) × $0.0004 + $0.014 = $0.198 total.

What's the difference between the default flow and the search-based flow?

The default flow queries Twitter's replies endpoint directly. The search-based flow (useSearch: true) instead searches conversation_id:<tweetId>, which can surface a broader set of conversation tweets not always available through the standard endpoint.

Can I use both a Tweet ID and a Tweet URL in the same run?

Yes. You can populate both tweetIds and startUrls in the same input — the actor processes both lists.

Does this scraper work on the Apify Free Plan?

Yes, Free Plan users get Demo Mode — up to 5 runs per month, each capped at 10 items — enough to test the actor and validate output format before subscribing.

Can I extract reply author bio and follower data?

Yes, every reply includes a full author object with username, bio, follower/following counts, verification status, account creation date, and more.

Can I filter or reshape the output fields?

Yes, use the customMapFunction input to write a JavaScript function that renames, reformats, or selects only the fields you want in the final dataset.

What happens if I enter an invalid Tweet ID or URL?

Invalid or non-existent Tweet IDs/URLs return no results for that entry. The actor continues processing any other valid entries in your input. Check the Log tab for specific error messages.

Can I scrape replies to protected/private account tweets?

No, this scraper only accesses replies on public tweets. Protected or restricted accounts cannot be scraped.

How many replies can I scrape per run?

Set any value for maxItems, or leave it empty for unlimited extraction (subject to how many replies actually exist on the tweet).

Can I automate reply scraping on a schedule?

Yes, use Apify's built-in scheduler to run the actor automatically at set intervals — useful for ongoing brand monitoring or tracking reactions to a live campaign.

What data formats can I export?

Export scraped reply data in JSON, CSV, Excel (.xlsx), XML, or RSS, or access it via the Apify API.

Can I rebuild the full conversation thread from the output?

Yes, each reply includes conversationId, inReplyToId, and inReplyToUsername, letting you reconstruct the thread structure from the flat dataset.

Does the scraper extract mentions and hashtags from replies?

Yes, the entities object includes user_mentions and other embedded entities found in each reply's text.

How accurate are the engagement metrics?

Metrics are extracted directly from Twitter/X at the moment of scraping and reflect the exact values shown on the platform at that time.

Can I use this for sentiment analysis pipelines?

Absolutely. The clean, structured fullText field per reply, combined with engagement metrics and timestamps, plugs directly into most sentiment-analysis or NLP pipelines.

Do I need proxies to scrape Twitter replies?

No, proxies are not required. The scraper runs reliably without any additional proxy setup or cost.

Why did my run return fewer replies than I see on the tweet itself?

This can happen with the default replies flow on very large threads. Try enabling useSearch: true to run the broader conversation_id: search flow, which sometimes surfaces additional replies.

Contact

If you need any sort of support, please send an email to apidojo10@gmail.com. You name it, we get it.