Tweet Engagement: Comments, Quotes, and Retweets

Every tweet on X generates three types of public engagement: comments (replies), quote tweets, and retweets. The aggregate counts are visible on the tweet itself, but the actual people and content behind those numbers are not - unless you use an API. Sorsa provides dedicated endpoints to extract all three: who replied and what they said, who quoted the tweet and what they added, and who retweeted it. This guide covers how to retrieve a tweet’s full engagement data, from a high-level metrics snapshot down to the individual replies, quotes, and retweeters.

Starting Point: Get a Tweet’s Metrics

Before drilling into individual engagement, you often want the overview. The /tweet-info endpoint returns the full tweet object including all engagement counters.

Simplest Example

curl -X POST https://api.sorsa.io/v3/tweet-info \
  -H "ApiKey: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"tweet_link": "https://x.com/elonmusk/status/1234567890"}'

import requests

API_KEY = "YOUR_API_KEY"

def get_tweet(tweet_link):
    resp = requests.post(
        "https://api.sorsa.io/v3/tweet-info",
        headers={"ApiKey": API_KEY, "Content-Type": "application/json"},
        json={"tweet_link": tweet_link},
    )
    resp.raise_for_status()
    return resp.json()


tweet = get_tweet("https://x.com/elonmusk/status/1234567890")

print(f"Text: {tweet['full_text'][:100]}...")
print(f"Likes:    {tweet.get('likes_count', 0):,}")
print(f"Retweets: {tweet.get('retweet_count', 0):,}")
print(f"Quotes:   {tweet.get('quote_count', 0):,}")
print(f"Replies:  {tweet.get('reply_count', 0):,}")
print(f"Views:    {tweet.get('view_count', 0):,}")
print(f"Bookmarks:{tweet.get('bookmark_count', 0):,}")

For multiple tweets at once, use /tweet-info-bulk with up to 100 tweet links per request (see Optimizing API Usage). Now let’s look at who is behind each of those numbers.

Comments (Replies)

Endpoint: POST /v3/comments Returns the replies posted under a specific tweet. Each page contains up to 20 comments, and each comment is a full tweet object with its own engagement metrics and author profile.

Simplest Example

def get_comments(tweet_link):
    resp = requests.post(
        "https://api.sorsa.io/v3/comments",
        headers={"ApiKey": API_KEY, "Content-Type": "application/json"},
        json={"tweet_link": tweet_link},
    )
    resp.raise_for_status()
    return resp.json()

data = get_comments("https://x.com/elonmusk/status/1234567890")
for comment in data.get("tweets", []):
    print(f"@{comment['user']['username']}: {comment['full_text'][:80]}")

Parameters

Parameter	Type	Required	Description
`tweet_link`	string	Yes	Full URL of the tweet.
`next_cursor`	string	No	Pagination cursor for fetching more comments.

Paginating Through All Comments

Tweets with hundreds of replies require pagination. The pattern is the same cursor-based loop used across all Sorsa endpoints:

import time

def get_all_comments(tweet_link, max_pages=20):
    """Fetch all comments under a tweet with pagination."""
    all_comments = []
    next_cursor = None

    for page in range(max_pages):
        body = {"tweet_link": tweet_link}
        if next_cursor:
            body["next_cursor"] = next_cursor

        resp = requests.post(
            "https://api.sorsa.io/v3/comments",
            headers={"ApiKey": API_KEY, "Content-Type": "application/json"},
            json=body,
        )
        resp.raise_for_status()
        data = resp.json()

        comments = data.get("tweets", [])
        all_comments.extend(comments)

        next_cursor = data.get("next_cursor")
        if not next_cursor:
            break
        time.sleep(0.1)

    return all_comments


comments = get_all_comments("https://x.com/elonmusk/status/1234567890")
print(f"Collected {len(comments)} comments")

What You Can Do with Comment Data

Each comment is a full tweet object, so you have the text, engagement metrics, and author profile. This unlocks several analysis patterns: Sentiment analysis. Feed the full_text of each comment into an NLP classifier to gauge whether replies are positive, negative, or neutral. A tweet with 500 replies that are 80% negative tells a very different story than one with 500 positive replies. Identify top commenters. Sort by likes_count to find the most-engaged-with replies. These are the voices that shaped the conversation. Extract questions. Filter comments containing ”?” to find questions your audience is asking - useful for FAQ generation, content ideas, and customer support detection.

# Find the most-liked comments
top_comments = sorted(comments, key=lambda c: c.get("likes_count", 0), reverse=True)

print("Top 5 comments by likes:")
for c in top_comments[:5]:
    print(f"  @{c['user']['username']} ({c['likes_count']} likes): {c['full_text'][:80]}")

Quote Tweets

Endpoint: POST /v3/quotes Returns tweets that quoted (retweeted with comment) a specific tweet. Like comments, each quote is a full tweet object - you get the added commentary, engagement metrics, and author profile.

Simplest Example

def get_quotes(tweet_link):
    resp = requests.post(
        "https://api.sorsa.io/v3/quotes",
        headers={"ApiKey": API_KEY, "Content-Type": "application/json"},
        json={"tweet_link": tweet_link},
    )
    resp.raise_for_status()
    return resp.json()

data = get_quotes("https://x.com/elonmusk/status/1234567890")
for quote in data.get("tweets", []):
    print(f"@{quote['user']['username']} quoted: {quote['full_text'][:80]}")

Parameters

Parameter	Type	Required	Description
`tweet_link`	string	Yes	Full URL of the original tweet.
`next_cursor`	string	No	Pagination cursor.

Paginating Through All Quotes

def get_all_quotes(tweet_link, max_pages=20):
    """Fetch all quote tweets of a specific tweet."""
    all_quotes = []
    next_cursor = None

    for page in range(max_pages):
        body = {"tweet_link": tweet_link}
        if next_cursor:
            body["next_cursor"] = next_cursor

        resp = requests.post(
            "https://api.sorsa.io/v3/quotes",
            headers={"ApiKey": API_KEY, "Content-Type": "application/json"},
            json=body,
        )
        resp.raise_for_status()
        data = resp.json()

        quotes = data.get("tweets", [])
        all_quotes.extend(quotes)

        next_cursor = data.get("next_cursor")
        if not next_cursor:
            break
        time.sleep(0.1)

    return all_quotes

Why Quote Tweets Matter More Than Retweets

A retweet is passive amplification - the user pressed a button. A quote tweet is active engagement - the user added their own take, and that take is visible to their entire audience. Quotes often contain the most valuable signal: endorsements, criticisms, counterarguments, and derivative ideas. For content analysis and PR monitoring, quote tweets are where the real conversation happens.

quotes = get_all_quotes("https://x.com/brand/status/1234567890")

# Find quotes that reached the largest audiences
quotes.sort(key=lambda q: q["user"].get("followers_count", 0), reverse=True)

print("Quotes from the highest-reach accounts:")
for q in quotes[:5]:
    u = q["user"]
    print(f"  @{u['username']} ({u['followers_count']:,} followers)")
    print(f"  \"{q['full_text'][:80]}...\"\n")

Retweets (Who Retweeted)

Endpoint: POST /v3/retweets Returns the users who retweeted a specific tweet. Note: unlike /comments and /quotes, this endpoint returns a UsersResponse (array of user profiles), not a TweetsResponse. You get the retweeters’ profiles, not tweet objects.

Simplest Example

def get_retweeters(tweet_link):
    resp = requests.post(
        "https://api.sorsa.io/v3/retweets",
        headers={"ApiKey": API_KEY, "Content-Type": "application/json"},
        json={"tweet_link": tweet_link},
    )
    resp.raise_for_status()
    return resp.json()

data = get_retweeters("https://x.com/elonmusk/status/1234567890")
for user in data.get("users", []):
    print(f"@{user['username']} ({user['followers_count']} followers) retweeted")

Parameters

Parameter	Type	Required	Description
`tweet_link`	string	Yes	Full URL of the tweet.
`next_cursor`	string	No	Pagination cursor.

Response Format Difference

This is the key distinction to remember:

Endpoint	Returns	Response key	Contains
`/comments`	Tweets	`tweets`	Full tweet objects (text + author)
`/quotes`	Tweets	`tweets`	Full tweet objects (text + author)
`/retweets`	Users	`users`	User profile objects only

Retweets do not have their own text (a retweet is just a redistribution of the original), so the endpoint returns the profile of each retweeter instead.

Paginating Through All Retweeters

def get_all_retweeters(tweet_link, max_pages=20):
    """Fetch all users who retweeted a tweet."""
    all_users = []
    next_cursor = None

    for page in range(max_pages):
        body = {"tweet_link": tweet_link}
        if next_cursor:
            body["next_cursor"] = next_cursor

        resp = requests.post(
            "https://api.sorsa.io/v3/retweets",
            headers={"ApiKey": API_KEY, "Content-Type": "application/json"},
            json=body,
        )
        resp.raise_for_status()
        data = resp.json()

        users = data.get("users", [])
        all_users.extend(users)

        next_cursor = data.get("next_cursor")
        if not next_cursor:
            break
        time.sleep(0.1)

    return all_users

Analyzing Retweeter Profiles

Since you get full user profiles, you can analyze who amplified the tweet:

retweeters = get_all_retweeters("https://x.com/brand/status/1234567890")

total_reach = sum(u.get("followers_count", 0) for u in retweeters)
verified_count = sum(1 for u in retweeters if u.get("verified"))

print(f"Retweeters: {len(retweeters)}")
print(f"Combined follower reach: {total_reach:,}")
print(f"Verified retweeters: {verified_count}")

Full Engagement Breakdown for a Single Tweet

Combining all three endpoints gives you a complete picture of how a tweet performed - not just the numbers, but the people and content behind them:

def full_engagement_report(tweet_link):
    """Generate a complete engagement report for a single tweet."""

    # Get tweet metrics
    tweet = get_tweet(tweet_link)
    print(f"Tweet by @{tweet['user']['username']}:")
    print(f"  \"{tweet['full_text'][:100]}...\"")
    print(f"  Likes: {tweet.get('likes_count', 0):,} | "
          f"Views: {tweet.get('view_count', 0):,}")
    print()

    # Comments
    comments = get_all_comments(tweet_link, max_pages=10)
    print(f"Comments: {len(comments)}")
    if comments:
        top_comment = max(comments, key=lambda c: c.get("likes_count", 0))
        print(f"  Most liked: @{top_comment['user']['username']} "
              f"({top_comment['likes_count']} likes)")
        print(f"  \"{top_comment['full_text'][:80]}...\"")
    print()

    # Quotes
    quotes = get_all_quotes(tweet_link, max_pages=10)
    print(f"Quotes: {len(quotes)}")
    if quotes:
        biggest_quoter = max(quotes, key=lambda q: q["user"].get("followers_count", 0))
        print(f"  Highest reach: @{biggest_quoter['user']['username']} "
              f"({biggest_quoter['user']['followers_count']:,} followers)")
        print(f"  \"{biggest_quoter['full_text'][:80]}...\"")
    print()

    # Retweeters
    retweeters = get_all_retweeters(tweet_link, max_pages=10)
    total_reach = sum(u.get("followers_count", 0) for u in retweeters)
    print(f"Retweeters: {len(retweeters)}")
    print(f"  Combined reach: {total_reach:,} followers")
    if retweeters:
        top_rt = max(retweeters, key=lambda u: u.get("followers_count", 0))
        print(f"  Biggest amplifier: @{top_rt['username']} "
              f"({top_rt['followers_count']:,} followers)")

    return {
        "tweet": tweet,
        "comments": comments,
        "quotes": quotes,
        "retweeters": retweeters,
    }


report = full_engagement_report("https://x.com/brand/status/1234567890")

Sample Output

Tweet by @brand:
  "We're excited to announce our Series B funding round of $50M..."
  Likes: 2,847 | Views: 892,000

Comments: 156
  Most liked: @tech_journalist (89 likes)
  "Congrats! What's the plan for international expansion?..."

Quotes: 43
  Highest reach: @vc_partner (284,000 followers)
  "This team has been on our radar for two years. Well deserved...."

Retweeters: 312
  Combined reach: 4,218,000 followers
  Biggest amplifier: @industry_leader (892,000 followers)

Analyzing Multiple Tweets (Batch Pattern)

When you need engagement data for a set of tweets (e.g., all posts from a campaign), fetch the tweet list first via /user-tweets or /search-tweets, then drill into each one:

def compare_tweet_engagement(tweet_links):
    """Compare engagement breakdown across multiple tweets."""
    results = []

    for link in tweet_links:
        tweet = get_tweet(link)
        comments = get_all_comments(link, max_pages=3)
        quotes = get_all_quotes(link, max_pages=3)
        retweeters = get_all_retweeters(link, max_pages=3)

        rt_reach = sum(u.get("followers_count", 0) for u in retweeters)

        results.append({
            "text": tweet["full_text"][:60],
            "likes": tweet.get("likes_count", 0),
            "comments": len(comments),
            "quotes": len(quotes),
            "retweets": len(retweeters),
            "retweet_reach": rt_reach,
        })
        time.sleep(0.5)

    # Print comparison
    print(f"{'Tweet':<62} {'Likes':>6} {'Cmts':>5} {'Qts':>4} {'RTs':>4} {'RT Reach':>10}")
    print("-" * 100)
    for r in results:
        print(f"{r['text']:<62} {r['likes']:>6} {r['comments']:>5} "
              f"{r['quotes']:>4} {r['retweets']:>4} {r['retweet_reach']:>10,}")

    return results

This is useful for identifying which tweets in a series performed best and understanding why - did success come from comments (conversation), quotes (endorsement/debate), or retweets (amplification)?

Exporting Engagement Data to CSV

import csv

def export_comments_to_csv(comments, output_file="comments.csv"):
    fields = [
        "comment_id", "created_at", "full_text", "likes", "retweets",
        "author_username", "author_followers", "author_verified",
    ]
    with open(output_file, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=fields)
        writer.writeheader()
        for c in comments:
            u = c.get("user", {})
            writer.writerow({
                "comment_id": c["id"],
                "created_at": c["created_at"],
                "full_text": c["full_text"],
                "likes": c.get("likes_count", 0),
                "retweets": c.get("retweet_count", 0),
                "author_username": u.get("username", ""),
                "author_followers": u.get("followers_count", 0),
                "author_verified": u.get("verified", False),
            })
    print(f"Exported {len(comments)} comments to {output_file}")

The same pattern works for quotes (they are also tweet objects). For retweeters, export user fields instead of tweet fields.

Next Steps

Search Tweets - find tweets by keyword, then drill into their engagement.
Track Mentions - monitor mentions of your brand, then analyze engagement on the most-discussed ones.
Competitor Analysis - compare engagement patterns across competitor content.
Historical Data - retrieve old tweets and analyze how their engagement played out.
Campaign Verification - verify that specific users commented on or retweeted a tweet.
API Reference - full specification for /comments, /quotes, /retweets, /tweet-info, /tweet-info-bulk.

Getting started

Core Mechanics

Solution Guides

Tweet Engagement

Tweet Engagement: Comments, Quotes, and Retweets

Starting Point: Get a Tweet’s Metrics

Simplest Example

Comments (Replies)

Simplest Example

Parameters

Paginating Through All Comments

What You Can Do with Comment Data

Quote Tweets

Simplest Example

Parameters

Paginating Through All Quotes

Why Quote Tweets Matter More Than Retweets

Retweets (Who Retweeted)

Simplest Example

Parameters

Response Format Difference

Paginating Through All Retweeters

Analyzing Retweeter Profiles

Full Engagement Breakdown for a Single Tweet

Sample Output

Analyzing Multiple Tweets (Batch Pattern)

Exporting Engagement Data to CSV

Next Steps

Getting started

Core Mechanics

Solution Guides

​Tweet Engagement: Comments, Quotes, and Retweets

​Starting Point: Get a Tweet’s Metrics

​Simplest Example

​Comments (Replies)

​Simplest Example

​Parameters

​Paginating Through All Comments

​What You Can Do with Comment Data

​Quote Tweets

​Simplest Example

​Parameters

​Paginating Through All Quotes

​Why Quote Tweets Matter More Than Retweets

​Retweets (Who Retweeted)

​Simplest Example

​Parameters

​Response Format Difference

​Paginating Through All Retweeters

​Analyzing Retweeter Profiles

​Full Engagement Breakdown for a Single Tweet

​Sample Output

​Analyzing Multiple Tweets (Batch Pattern)

​Exporting Engagement Data to CSV

​Next Steps

Tweet Engagement: Comments, Quotes, and Retweets

Starting Point: Get a Tweet’s Metrics

Simplest Example

Comments (Replies)

Simplest Example

Parameters

Paginating Through All Comments

What You Can Do with Comment Data

Quote Tweets

Simplest Example

Parameters

Paginating Through All Quotes

Why Quote Tweets Matter More Than Retweets

Retweets (Who Retweeted)

Simplest Example

Parameters

Response Format Difference

Paginating Through All Retweeters

Analyzing Retweeter Profiles

Full Engagement Breakdown for a Single Tweet

Sample Output

Analyzing Multiple Tweets (Batch Pattern)

Exporting Engagement Data to CSV

Next Steps