Skip to main content
← Back to blog
•by Data Team
TutorialsCryptotrending

Tracking Trending Topics & Viral Tweets

Why Trending Topics Matter

In crypto, narratives drive price action. Trends like "AI tokens," "Solana summer," and "RWA" emerge on X/Twitter days before they hit mainstream crypto media. The traders who spot them early capture the most alpha.

XCROP's Trending endpoint gives you real-time access to what's trending across 62 countries, while the Search endpoint lets you drill into any topic with powerful filters.

Using the Trending Endpoint

Fetch Trending Topics

PYTHON
import requests
import os

response = requests.get(
    "https://xcrop.io/api/v2/trending",
    headers={"Authorization": "Bearer " + os.environ["XCROP_API_KEY"]},
    params={"country": "United States"}
)

trends = response.json()["data"]
for trend in trends:
    print(trend["name"])
    print("  Tweet volume: " + str(trend.get("tweet_count", "N/A")))
    print()

Filter by Country

The endpoint supports 62 countries. Some popular ones for crypto:

JAVASCRIPT
const countries = [
  "United States",
  "United Kingdom",
  "Japan",
  "South Korea",
  "Singapore",
  "United Arab Emirates"
];

for (const country of countries) {
  const response = await fetch(
    "https://xcrop.io/api/v2/trending?" + new URLSearchParams({ country }),
    { headers: { "Authorization": "Bearer " + apiKey } }
  );

  const { data: trends } = await response.json();
  console.log("\n" + country + " (" + trends.length + " trends):");
  trends.slice(0, 5).forEach(t => console.log("  - " + t.name));
}

Each call returns up to 50 trending topics for the specified country.

Deep-Diving with Search

When you spot an interesting trend, use the Search endpoint to find the most relevant tweets about it:

PYTHON
import requests
import os

API_KEY = os.environ["XCROP_API_KEY"]
HEADERS = {"Authorization": "Bearer " + API_KEY}
BASE_URL = "https://xcrop.io/api/v2"

# Search for tweets about a trending topic
response = requests.post(
    BASE_URL + "/search",
    headers={**HEADERS, "Content-Type": "application/json"},
    json={
        "query": "Solana ETF",
        "count": 50,
        "sort": "popular",
        "min_likes": 100
    }
)

tweets = response.json()["data"]
for tweet in tweets[:10]:
    author = tweet["author"]["username"]
    likes = str(tweet["metrics"]["likes"])
    print("@" + author + " (" + likes + " likes)")
    print("  " + tweet["text"][:150])
    print()

Search Filters

The Search endpoint supports powerful filters:

FilterDescriptionExample
`query`Search terms (max 200 chars)`"Solana ETF"`
`sort`Sort order`latest`, `popular`, `engagement`
`min_likes`Minimum like count`100`
`min_retweets`Minimum retweet count`50`
`lang`Language filter`"en"`
`since` / `until`Date range`"2026-03-01"`
`exclude_replies`Skip replies`true`
`exclude_retweets`Skip retweets`true`

Using sort: "popular" leverages Twitter's Top search results, which is ideal for finding the most impactful tweets on a topic.

Building a Narrative Monitor

Here's a complete script that monitors trending topics, detects crypto-related trends, and searches for detailed tweets:

PYTHON
import requests
import time
import os

API_KEY = os.environ["XCROP_API_KEY"]
HEADERS = {
    "Authorization": "Bearer " + API_KEY,
    "Content-Type": "application/json"
}
BASE_URL = "https://xcrop.io/api/v2"

# Crypto keywords to watch for in trending topics
CRYPTO_KEYWORDS = [
    "bitcoin", "btc", "ethereum", "eth", "solana", "sol",
    "defi", "nft", "airdrop", "etf", "sec", "binance",
    "coinbase", "memecoin", "altcoin", "crypto", "web3"
]

def is_crypto_trend(trend_name):
    """Check if a trending topic is crypto-related."""
    name_lower = trend_name.lower()
    return any(kw in name_lower for kw in CRYPTO_KEYWORDS)

def check_trends(country="United States"):
    """Fetch trends and filter for crypto topics."""
    response = requests.get(
        BASE_URL + "/trending",
        headers=HEADERS,
        params={"country": country}
    )

    if response.status_code != 200:
        return []

    trends = response.json()["data"]
    return [t for t in trends if is_crypto_trend(t["name"])]

def search_trend(topic, count=20):
    """Search for top tweets about a trending topic."""
    response = requests.post(
        BASE_URL + "/search",
        headers=HEADERS,
        json={
            "query": topic,
            "count": count,
            "sort": "popular",
            "min_likes": 50,
            "exclude_retweets": True
        }
    )

    if response.status_code != 200:
        return []

    return response.json()["data"]

# Monitor loop
print("Monitoring trending topics for crypto narratives...")
seen_trends = set()

while True:
    crypto_trends = check_trends()

    for trend in crypto_trends:
        name = trend["name"]
        if name in seen_trends:
            continue
        seen_trends.add(name)

        print("\n[NEW TREND] " + name)
        volume = trend.get("tweet_count")
        if volume:
            print("  Tweet volume: " + str(volume))

        # Deep-dive into the trend
        tweets = search_trend(name, count=5)
        for tweet in tweets:
            author = tweet["author"]["username"]
            likes = tweet["metrics"]["likes"]
            print("  @" + author + " (" + str(likes) + " likes)")
            print("    " + tweet["text"][:120])

    time.sleep(300)  # Check every 5 minutes

Combining Trending with KOL Tracking

The most powerful signal is when a trending topic is also being discussed by top KOLs:

PYTHON
def cross_reference_with_kols(trend_name, kol_usernames):
    """Check if KOLs are tweeting about a trending topic."""
    # Pull each KOL's recent tweets
    tweets = []
    for kol in kol_usernames.split(","):
        response = requests.get(
            f"{BASE_URL}/users/{kol}/tweets",
            headers=HEADERS,
            params={"count": 20}
        )
        if response.status_code == 200:
            tweets += response.json().get("data", [])

    if not tweets:
        return []

    trend_lower = trend_name.lower()

    # Find KOL tweets mentioning the trend
    matching = []
    for tweet in tweets:
        if trend_lower in tweet["text"].lower():
            matching.append(tweet)

    return matching

# Example usage
kols = "VitalikButerin,CryptoHayes,DefiIgnas"
matches = cross_reference_with_kols("Solana ETF", kols)

if matches:
    print("[STRONG SIGNAL] KOLs discussing trending topic!")
    for tweet in matches:
        print("  @" + tweet["author"]["username"] + ": " + tweet["text"][:100])

Best Practices

1. Monitor multiple countries: Crypto trends often start in Asia before hitting the US
2. Use popular sort for search: Twitter's Top results surface the most impactful tweets
3. Combine trending + search + KOL: Multiple signals = higher confidence
4. Set minimum engagement thresholds: Use min_likes to filter out noise
5. Poll trending every 5 minutes: Topics are cached for 1 minute, so faster polling wastes credits

Credit Usage

• Trending endpoint: 100 credits per call (flat rate, cached 1 min)
• Search: 15 credits per tweet returned
• Polling trending every 5 minutes = ~28,800 credits/day
• Pro plan ($9.9/mo, 2M credits) covers continuous monitoring with room to spare
Build this with the XCROP API

One API for X/Twitter data: profiles, tweets, followers, search and real-time streams. Start free with 5,000 credits/month, no card required.

Get your free API keyRead the docsSee pricing