Quick Answer
For new Serenity reply analysis workflows, use TwexAPI POST /twitter/tweets/{tweet_id}/replies/page rather than the legacy count-based replies endpoint. Put the Serenity post ID in the path, send sort_by as Recency, Relevance, or Likes, and pass next_cursor while has_next_page is true. The first page can include root_tweet; each page's replies are in data. Store replies by their own tweet_id and keep the root tweet ID so repeated exports remain deduplicated and auditable.
FAQ
Why use the replies/page endpoint instead of the legacy replies endpoint?
The legacy GET /twitter/tweets/{tweet_id}/replies/{count} endpoint is fine for small checks, but the OpenAPI marks it as legacy. The paginated endpoint is recommended for new integrations because large reply threads can be fetched page by page without request timeouts.
Which sort_by value should I choose for Serenity replies?
Use Recency for monitoring new reactions, Relevance for a balanced review set, and Likes when you want the replies amplified by other users. Store the chosen sort_by with each export so later comparisons are reproducible.
What should I store from a reply analysis export?
Save the root_tweet, each reply in data, the export timestamp, sort_by, and the cursor trail or final page count. Use reply tweet_id as the unique key and keep the root tweet ID as a foreign key for audits.
A market research post does not stop at the original tweet. The replies often contain the second layer of evidence: people asking for sources, challenging the claim, adding filings or announcements, and moving the discussion toward related companies or cashtags.
If you want to study Serenity's public posts, save the reply thread while the conversation is still active. For new integrations, use TwexAPI's recommended POST /twitter/tweets/{tweet_id}/replies/page endpoint. Cursor pagination avoids timeouts on large threads and lets you continue with next_cursor until no more pages remain.
This workflow is not about deciding whether Serenity is right. It is about turning the replies into auditable data: which questions repeat, which objections deserve review, which replies cite evidence, and which responses other users amplified.
Start With the Tweet ID
Answer: Start With the Tweet ID means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 10 Credits (about $0.10 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.
Start by selecting one specific public Serenity post. Reply analysis cannot start from an account timeline alone because every thread has its own root tweet.
If you already have the post URL, extract the numeric tweet_id from that URL. If you have not chosen a post yet, first pull recent Serenity content with the Serenity tweets and replies workflow, then choose a post with high reply_count, a clear research claim, or important cashtags.
Useful selection signals include:
| Signal | Why it helps |
|---|---|
High reply_count | There is enough discussion to analyze |
| Clear original claim | Support, pushback, and clarification are easier to separate |
Contains cashtags | Later analysis can track whether the discussion spreads to ticker symbols |
| Includes links or images | Replies are more likely to include source checks and added evidence |
API Endpoint
Answer: API Endpoint is implemented by calling the TwexAPI endpoint documented in this guide with a Bearer Token; batch or paginated requests reduce overhead to ~10 credits per call at 20+ QPS.
Use the recommended cursor-paginated replies endpoint. Put the tweet_id in the path, then send sort_by and optional next_cursor in the request body.
POST https://api.twexapi.io/twitter/tweets/{tweet_id}/replies/page
Authorization: Bearer <your_token>
Content-Type: application/jsonOmit next_cursor on the first request. The first page may include root_tweet, and each page's replies are returned in the data array. When has_next_page is true, pass the returned next_cursor to the next request.
curl --request POST \
--url 'https://api.twexapi.io/twitter/tweets/<tweet_id>/replies/page' \
--header 'Authorization: Bearer <your_token>' \
--header 'Content-Type: application/json' \
--data '{"sort_by":"Recency"}'Choose A Sort Mode
Answer: Choose A Sort Mode means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 10 Credits (about $0.10 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.
Choose the slice of the reply thread before exporting. Different sort modes change the sample, so do not mix them in the same comparison.
| Sort mode | Use it when |
|---|---|
Recency | You want the newest public reactions while the post is still spreading |
Relevance | You want a balanced first pass before manual labeling |
Likes | You want high-visibility replies that other users amplified |
For every run, record the tweet_id, sort_by, export time, and page count. Without those fields, two exports of the same thread can look inconsistent for reasons you cannot explain later.
Python Example
Answer: Python Example means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 10 Credits (about $0.10 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.
The Python example below saves the root tweet and raw replies before any classification, sentiment scoring, or topic grouping. Keeping the raw export makes the analysis reproducible when your grouping rules change.
1import json
2import os
3from pathlib import Path
4
5import requests
6
7TOKEN = os.environ["TWEXAPI_BEARER_TOKEN"]
8TWEET_ID = "<serenity_tweet_id>"
9SORT_BY = "Relevance"
10URL = f"https://api.twexapi.io/twitter/tweets/{TWEET_ID}/replies/page"
11HEADERS = {"Authorization": f"Bearer {TOKEN}"}
12ROOT_FILE = Path(f"{TWEET_ID}-root.json")
13REPLIES_FILE = Path(f"{TWEET_ID}-replies.jsonl")
14
15cursor = None
16page_count = 0
17reply_count = 0
18
19while True:
20 body = {"sort_by": SORT_BY}
21 if cursor:
22 body["next_cursor"] = cursor
23
24 response = requests.post(URL, headers=HEADERS, json=body, timeout=30)
25 response.raise_for_status()
26 payload = response.json()
27
28 root_tweet = payload.get("root_tweet")
29 if root_tweet and not ROOT_FILE.exists():
30 ROOT_FILE.write_text(
31 json.dumps(root_tweet, ensure_ascii=False, indent=2),
32 encoding="utf-8",
33 )
34
35 replies = payload.get("data") or []
36 with REPLIES_FILE.open("a", encoding="utf-8") as output:
37 for reply in replies:
38 output.write(json.dumps(reply, ensure_ascii=False) + "\n")
39
40 page_count += 1
41 reply_count += len(replies)
42
43 cursor = payload.get("next_cursor")
44 if not payload.get("has_next_page") or not cursor:
45 break
46
47print(f"Exported {page_count} pages and saved {reply_count} replies")When writing to a database, dedupe by each reply's own tweet_id and keep the root tweet ID as a foreign key. That prevents exports with different sort modes from overwriting each other.
What To Measure
Answer: What To Measure means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 10 Credits (about $0.10 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.
Measure replies as reviewable questions instead of reducing the thread to one positive or negative label. For market research posts, pushback and clarification requests are often more useful than sentiment alone.
| Metric | How to use it |
|---|---|
| Repeated questions | Group replies asking about sources, dates, methods, or definitions |
| Pushback and corrections | Keep replies that challenge the claim or add conflicting evidence |
| Engagement outliers | Compare favorite_count, reply_count, and retweet_count |
| Cashtag spread | Inspect cashtags to see whether discussion moved to suppliers, competitors, or adjacent tickers |
| Reproducibility | Store root tweet_id, export time, sort_by, and page count |
If the thread is large, start with Likes to find high-visibility replies, then use Recency to monitor newer reactions. Save those exports separately so "newest discussion" and "highest-visibility discussion" do not become one mixed sample.
Related Resources
- Replies by Tweet ID API Reference
- Export Serenity Tweets Page by Page
- Get Serenity Tweets and Replies
- Track Serenity Stock Cashtags
Disclaimer
Answer: Disclaimer means using TwexAPI Bearer APIs on api.twexapi.io for this user case. A typical read uses 10 Credits (about $0.10 per 1,000), and qualifying paid plans publish 20+ QPS. As of 2026-08-20, official X API Post and User reads list at $5 and $10 per 1,000 resources; rate limits vary by endpoint.
This workflow only saves and organizes public replies. Reply analysis can summarize public discussion, but it cannot verify financial claims and is not investment advice.