How to Track Google Trends Trending Searches by Country

Google's Trending Now page answers "what is everyone searching for right now?", but only for one country at a time and only while you are looking. If you run content, news or campaigns across several markets, you want that list captured every day, per country, in a format you can sort and compare.

Doing that by hand means visiting the page for each country and copying rows. Unofficial libraries break when Google changes things and leave you to handle storage and deduplication. The Google Trends actor reads Google's official Trending Now RSS feed for any of 125 countries and returns one structured row per trend, with traffic parsed to a number and the news articles attached.

This guide sets up a country list, schedules a daily run, and uses pandas to keep a history, drop repeats and compare today with yesterday.

Step by step

1. Pick your countries

   Decide which markets you want to watch and list their two-letter codes in geos, for example US, GB, DE, FR and BR. The input offers all 125 countries the official feed accepts; Google answers unknown codes with an error, so pick from that list. Each country is one request, so a long list only adds a second or so per country.

2. Set how many trends to keep per country

   The feed currently lists up to about 10 of the newest trends per country, fewer for small countries. Leave maxItemsPerGeo at 10 to keep everything, or lower it if you only care about the top few. Leave keywords empty for this workflow: the keyword explore part is separate, rate-limited by Google and not needed to track trending searches.

3. Turn on only-new mode with a monitor name

   Set onlyNewSinceLastRun to true and give the schedule its own monitorName. The actor then remembers what it already returned (per country, keyword and start day) in the key-value store trends-seen-<name> and skips those trends on later runs without charging for them. Use a different monitor name for each schedule so they do not share memory.

   {
     "geos": ["US", "GB", "DE", "FR", "BR"],
     "maxItemsPerGeo": 10,
     "onlyNewSinceLastRun": true,
     "monitorName": "daily-5-markets",
     "keywords": []
   }

4. Schedule a daily run

   In Apify Console, save the input above as a task (or use the actor directly) and create a schedule with a cron expression such as 0 7 * * * for 07:00 every day in your chosen timezone. If you need fresher alerts, an hourly expression like 0 * * * * works the same way, and only-new mode keeps the cost tied to trends that are genuinely new. Optionally add a webhook on run success to trigger the next step automatically.

5. Collect each run into a history file

   After each scheduled run, pull its dataset and append the trend rows to a CSV, stamped with the snapshot date. The dataset can also contain other row types if you ever add keywords, so filter on type == "trend".

   import os
   from datetime import date
   import pandas as pd
   from apify_client import ApifyClient
   
   client = ApifyClient(os.environ["APIFY_TOKEN"])
   
   # Read the dataset of the most recent finished scheduled run
   runs = client.actor("actor-forge/google-trends").runs().list(desc=True, limit=1, status="SUCCEEDED").items
   run = runs[0]
   rows = list(client.dataset(run["defaultDatasetId"]).iterate_items())
   
   df = pd.DataFrame([r for r in rows if r.get("type") == "trend"])
   df["snapshotDate"] = date.today().isoformat()
   
   # Append to a local history file
   path = "trends_history.csv"
   df.to_csv(path, mode="a", index=False, header=not os.path.exists(path))
   print(len(df), "trend rows added")
   

6. Dedupe and compare days with pandas

   Normalize keywords, drop duplicates on country, keyword and start day, then compare the latest snapshot with the one before it. The first output lists trends that are new today per country, the second shows topics trending in more than one country, often the most useful signal for a global team. You need at least two snapshot days in the file before the comparison runs.

   import pandas as pd
   
   hist = pd.read_csv("trends_history.csv", parse_dates=["startedAt"])
   hist["keywordNorm"] = hist["keyword"].str.strip().str.lower()
   hist["startDay"] = hist["startedAt"].dt.date
   
   # One row per country + keyword + start day (the same key the actor uses),
   # keeping the snapshot in which each trend was first seen
   hist = hist.sort_values("snapshotDate")
   dedup = hist.drop_duplicates(subset=["geo", "keywordNorm", "startDay"], keep="first")
   
   days = sorted(dedup["snapshotDate"].unique())
   today, yesterday = days[-1], days[-2]
   t = dedup[dedup["snapshotDate"] == today]
   y = dedup[dedup["snapshotDate"] == yesterday]
   
   # Trends first seen today whose keyword did not trend yesterday in that country
   merged = t.merge(y[["geo", "keywordNorm"]], on=["geo", "keywordNorm"],
                    how="left", indicator=True)
   new_today = merged[merged["_merge"] == "left_only"]
   print(new_today[["geo", "rank", "keyword", "approxTraffic", "topNewsTitle"]])
   
   # Topics trending in several countries at once
   multi = (t.groupby("keywordNorm")["geo"].nunique()
              .sort_values(ascending=False))
   print(multi[multi > 1])
   

7. Act on the rows

   Each row carries topNewsTitle, topNewsUrl and the full newsArticles list, so an editor or marketer can see why a term is trending without opening Google. Sort by approxTraffic to rank within a country, but remember it is Google's bucketed estimate (200+, 2K+, 1M+), not an exact count. trendsUrl is a link you can open to explore the term further on trends.google.com.

Sample input

{
  "geos": [
    "US",
    "GB",
    "DE",
    "FR",
    "BR"
  ],
  "maxItemsPerGeo": 10,
  "onlyNewSinceLastRun": true,
  "monitorName": "daily-5-markets",
  "keywords": []
}

Sample output

Each run returns rows like these, one per trend and country, with traffic as a number and the top news headline:

Run it from code

cURL

curl -X POST "https://api.apify.com/v2/acts/actor-forge~google-trends/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"geos": ["US", "GB", "DE", "FR", "BR"], "maxItemsPerGeo": 10, "onlyNewSinceLastRun": true, "monitorName": "daily-5-markets", "keywords": []}'

JavaScript (Node.js)

// Node.js 18+ (built-in fetch). Set APIFY_TOKEN in your environment.
const input = {
  "geos": [
    "US",
    "GB",
    "DE",
    "FR",
    "BR"
  ],
  "maxItemsPerGeo": 10,
  "onlyNewSinceLastRun": true,
  "monitorName": "daily-5-markets",
  "keywords": []
};

const res = await fetch(
  "https://api.apify.com/v2/acts/actor-forge~google-trends/run-sync-get-dataset-items",
  {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.APIFY_TOKEN}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify(input),
  }
);
const items = await res.json();
console.log(items.length, items[0]);

Python

# pip install apify-client   (set APIFY_TOKEN in your environment)
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run_input = {
    "geos": [
        "US",
        "GB",
        "DE",
        "FR",
        "BR"
    ],
    "maxItemsPerGeo": 10,
    "onlyNewSinceLastRun": True,
    "monitorName": "daily-5-markets",
    "keywords": []
}

run = client.actor("actor-forge/google-trends").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

The run-sync-get-dataset-items endpoint waits for the run (up to 5 minutes) and returns the dataset rows directly. For longer runs, start the run asynchronously and read the dataset when it finishes, as the Python example does. Replace APIFY_TOKEN with your own token from the Apify console.

What it costs

Trending Now rows cost $0.0005 each, so 5 countries × 10 trends is at most 50 rows or $0.025 per daily run, about $0.75 over 30 days before only-new mode skips repeats. The platform start fee at 256 MB adds $0.0000125 per run.

Tips and pitfalls

• The feed shows the newest trends only, about 10 per country. It is not the full Trending Now page with its 24 and 48-hour filters, so schedule frequently if you do not want to miss short-lived spikes.
• Traffic values are buckets, not counts. Two trends both at 2000 may differ a lot in reality; use the value to rank, not to forecast volume.
• Keep one monitorName per schedule. Reusing a name across an hourly and a daily schedule makes one of them skip trends the other already delivered.
• If you later add keywords for interest-over-time data, expect Google to rate-limit that part. Keywords it could not finish are free, and the Trending Now rows are unaffected.
• Set a maximum total charge on the run if you are cost-sensitive; the actor stops cleanly at the cap.

FAQ

Actors used in this guide

Related guides

All guides