How to Get Global Trending Tweets from X

Trend names alone are often too thin for monitoring. If a topic is moving, you need the posts behind it: who is posting, what language they use, which links or media are spreading, and how engagement changes across countries.

TwexAPI's Global Trending Tweets workflow moves from trend labels to tweet objects. You first discover supported countries, topics, and content tags, then request the posts driving a selected segment. The result can feed alerts, BI dashboards, LLM summaries, editorial planning, or social research.

Treat the output as structured trend context, not a complete record of every X conversation. Save the request filters with every crawl so later analysis can explain exactly which country, topic, and content segment produced the data.

Endpoint Flow

TwexAPI separates option discovery from tweet retrieval. In production, call the endpoints in this order instead of hard-coding country, topic, or content labels:

All requests use Bearer Token authentication:

Authorization: Bearer <your_token>

Official API references:

• Get Global Trending Countries
• Get Global Trending Topics
• Get Global Trending Contents
• Get Global Trending Tweets

Global Trending Tweets API

/twitter/global-trending/tweets is the final data endpoint. It requires country and supports optional topic, content, and count filters.

GET https://api.twexapi.io/twitter/global-trending/tweets?country=Japan&topic=Entertainment&content=Music&count=50

Authorization: Bearer <your_token>

Query Parameters

Cache countries and topics first, for example once per hour. Because contents depends on the selected country + topic pair, fetch it when a user changes filters or when a scheduled job adds a new segment.

JavaScript and Python Examples

The examples below show the full workflow: load countries, load topics, fetch content tags, then request trending tweets. In production, keep your token in server-side environment variables and never expose it in browser code.

Response Structure

/twitter/global-trending/tweets returns a standard response object where data is an array of tweets.

1{
2  "code": 200,
3  "msg": "success",
4  "data": [
5    {
6      "tweet_id": "1803006263529541838",
7      "text": "Example trending tweet text",
8      "created_at_datetime": "2024-06-17T03:51:48.000Z",
9      "lang": "en",
10      "favorite_count": 123,
11      "retweet_count": 45,
12      "reply_count": 12,
13      "quote_count": 3,
14      "view_count": "10000",
15      "is_paid_promotion": false,
16      "hashtags": ["AI"],
17      "cashtags": ["$TSLA"],
18      "urls": ["https://example.com"],
19      "media": [],
20      "user": {
21        "id": "1717001045992251392",
22        "name": "Example User",
23        "screen_name": "example"
24      }
25    }
26  ]
27}

Useful fields by workflow:

Build a Trending Tweet Monitor

A practical trending tweet monitor usually has four layers:

1. Discovery layer: Sync countries and topics on a schedule and store valid options.
2. Filtering layer: Generate request queues from the countries, topics, and content tags your business cares about.
3. Collection layer: Call /tweets and use tweet_id as the idempotency key in your database.
4. Analysis layer: Score trend strength, language distribution, media share, brand mentions, and unusual growth.

Here is a simple scoring function:

function trendingScore(tweet) {
  const likes = Number(tweet.favorite_count || 0);
  const reposts = Number(tweet.retweet_count || 0);
  const replies = Number(tweet.reply_count || 0);
  const quotes = Number(tweet.quote_count || 0);
  const views = Number(tweet.view_count || 0);

  return likes + reposts * 2 + replies * 1.5 + quotes * 2.5 + Math.log10(views + 1) * 10;
}

For real-time alerts, persist results for each country + topic + content window, then compare the latest crawl with the previous one. Useful signals include newly appearing tweets, average engagement changes, and shifts in the top authors.

Error Handling

Handle three common cases:

• 422 validation errors: Usually caused by country, topic, or content values that did not come from the official option endpoints. Fetch valid options first instead of hard-coding labels.
• 401/403 authentication errors: Check whether the Bearer Token is valid, whether the Authorization header is present, and whether the token is accidentally used from client-side code.
• Empty results: Some country, topic, and content tag combinations may temporarily have no trending tweets. Treat an empty array as a normal result, not a system failure.

Clamp count on both the client and server side so user input always stays within the documented 1 to 100 range.

Where This Fits

• News monitoring: Detect which posts are carrying a country-level topic, not just that the topic exists.
• Brand and competitor tracking: Watch how a product, company, or category appears inside trending conversations.
• Editorial planning: Feed the top posts into editors or LLM summarizers to find story angles.
• Campaign intelligence: Combine is_paid_promotion with engagement metrics to separate organic conversation from commercial signals.
• Finance and crypto monitoring: Track cashtags, project names, exchanges, and policy-related discussion across regions.

Implementation Notes

1. Cache countries and topics before querying contents.
2. Deduplicate by tweet_id before writing records.
3. Store the raw JSON so you can reprocess fields later.
4. Save request context: country, topic, content, count, and crawl time.
5. Validate user-provided filters against the values returned by the option endpoints.

Summary

The Global Trending Tweets workflow is best used as a collection pipeline: discover valid filters, fetch tweet objects, deduplicate by tweet_id, and store request context with each crawl. That gives you enough structure to compare countries, topics, content tags, authors, media, language, engagement, and promotion signals over time.

Start with one country and one topic. Once the fields, scoring rules, and alert thresholds are useful, expand the scheduler across more segments.