Topic Trends Tracker: Google Trends Alternative, Real Numbers

Compare public interest in any topic, brand, product or person with real numbers instead of a 0-100 sample. Topic Trends Tracker measures how many people open each topic's Wikipedia article every day and gives you daily and monthly views, 30-day and year-over-year change, peak day, spike days, a rising / falling trend and a 0-100 interest index, with a Google-Trends-style Excel export (one column per topic). Apify's free plan includes $5 of monthly platform credit, enough for about 2,500 topics at $0.002 each (pricing below).

No proxies, no captchas, no "429 Too Many Requests": the data comes from the free, official Wikimedia pageviews API.

Google Trends is the go-to for "is this getting more popular?", but it only gives relative 0-100 values, re-scales every time you change the comparison, limits you to 5 terms and blocks automated access. Topic Trends Tracker answers the same question with an open data source:

• 📈 Real counts: daily and monthly views of each topic's Wikipedia article, people only (bots and crawlers excluded) by default
• 🔁 Change: last 30 days vs the prior 30 days, and vs the same 30 days a year ago
• 🚀 Trend label: surging / rising / flat / falling from the last six months
• ⛰️ Peaks and spikes: the busiest day and up to 5 spike days (handy for spotting news events, launches and seasonality)
• 🧮 Interest index 0-100: compare any number of topics (not just 5) on one scale
• 🔎 Smart matching: plain words are matched to the right article, redirects followed (e.g. Ozempic → Semaglutide), disambiguation pages skipped; or paste an exact Wikipedia link
• 🌍 Any Wikipedia language: en, de, fr, es, ja, pt… and desktop / mobile split
• 📊 Excel + CSV: Topics sheet, Monthly and Daily sheets with one column per topic, ready to chart
• 💸 $0.002 per topic. 100 topics cost $0.20, whatever the period. Topics with no article cost nothing

Who it's for

• Content and SEO teams: pick topics with rising interest, time content around seasonal peaks
• Marketers and brand managers: track brand and competitor awareness month by month
• Product, e-commerce and dropshipping researchers: validate product and category trends before you invest
• Investors and analysts: alternative data on public attention for companies, drugs, technologies and assets
• Journalists and researchers: measure attention around events with transparent, citable public data
• AI agents: a reliable "how popular is X and is it growing?" tool that never gets blocked

How to use it

1. Click Try for free.
2. Enter topics, one per line (words or Wikipedia links).
3. Choose how many months back (default 12) and, optionally, a Wikipedia language and device.
4. Click Start. 50 topics take under a minute. Download TRENDS_XLSX, TOPICS_CSV or DAILY_CSV from Storage → Key-value store, or use the dataset via API.

Input example

{
  "topics": ["ChatGPT", "Ozempic", "Pickleball", "https://en.wikipedia.org/wiki/Stanley_(drinkware_company)"],
  "months": 24,
  "language": "en"
}

Task examples

• AI assistant race: ["ChatGPT", "Claude (language model)", "Gemini (chatbot)", "Perplexity AI", "Microsoft Copilot"], 24 months.
• Product research: candidate products or categories ("Air fryer", "Walking pad", "Matcha"); keep the ones with trend: rising and positive yoyChangePct.
• Brand vs competitors, monthly: your brand and 5 competitors, scheduled on the 1st of every month.
• Seasonality check: one topic over 60 months; read the Monthly sheet to see when interest peaks every year.
• Local market: set language to de or ja to measure interest in another country's Wikipedia.

Output example

One row per topic. A real row from a test run:

{
  "topic": "Pickleball",
  "input": "Pickleball",
  "article": "Pickleball",
  "language": "en",
  "description": "Racket sport",
  "wikidataId": "Q866224",
  "url": "https://en.wikipedia.org/wiki/Pickleball",
  "trend": "falling",
  "totalViews": 2689295,
  "avgDailyViews": 3679,
  "medianDailyViews": 3575,
  "last30dViews": 71247,
  "prior30dViews": 82104,
  "change30dPct": -13.2,
  "sameWindowLastYearViews": 102317,
  "yoyChangePct": -30.4,
  "peakDate": "2026-07-10",
  "peakViews": 7746,
  "spikes": [
    "2026-07-10 (7,746)",
    "2025-03-05 (7,500)"
  ],
  "monthlyViews": [
    {
      "month": "2024-11",
      "views": 122779
    },
    {
      "month": "2024-12",
      "views": 117177
    },
    "…",
    {
      "month": "2026-10",
      "views": 74196
    },
    {
      "month": "2026-11",
      "views": 2148
    }
  ],
  "daysTracked": 731,
  "from": "2024-11-01",
  "to": "2026-11-01",
  "status": "tracked",
  "interestIndex": 3.8,
  "agentSummary": "Pickleball (Wikipedia: Pickleball): interest index 3.8/100, falling; 3,679 views/day on average (2024-11-01 to 2026-11-01); last 30 days 71,247 (-13.2% vs prior 30); -30.4% vs a year ago; peak 7,746 on 2026-07-10"
}

Fields

Field	What it is
topic, article, url, description, wikidataId, language	What you asked for and the Wikipedia article it matched
interestIndex	0-100: average daily views scaled so the most-viewed topic in the run = 100
trend	surging (last 30 days ≥ 2× prior 30), rising, flat or falling (last 6 months)
totalViews, avgDailyViews, medianDailyViews	Over the chosen period
last30dViews, prior30dViews, change30dPct	Recent momentum
sameWindowLastYearViews, yoyChangePct	Last 30 days vs the same 30 days a year earlier
peakDate, peakViews, spikes	Busiest day and up to 5 spike days (well above normal)
monthlyViews	List of {month, views}
dailyViews	List of {date, views} (when Include the daily series is on)
from, to, daysTracked, status	Period covered; no Wikipedia article found rows are free
agentSummary	One plain-English line per topic

Files in the key-value store

Key	What it is
TRENDS_XLSX	Excel workbook: Topics, Monthly and Daily (one column per topic), Run log, Read me
TOPICS_CSV	Topics table as CSV
DAILY_CSV	Daily views, one column per topic
SUMMARY	JSON: ranking (topics by interest with trend and 30-day change), notFound, a one-line takeaway and nextStep

For AI agents

Use this Actor to answer "how much interest is there in X, and is it rising?" or to compare several topics, brands or products. Call it with Apify's MCP server (https://mcp.apify.com, tool call-actor, Actor pandadad0214/topic-trends-tracker) or the API (POST https://api.apify.com/v2/acts/pandadad0214~topic-trends-tracker/run-sync-get-dataset-items).

Minimal input:

{"topics": ["ChatGPT", "Ozempic"]}

Each dataset item includes:

• agentSummary: one line, e.g. Ozempic (Wikipedia: Semaglutide): interest index 2/100, falling; 1,820 views/day on average (2025-11-02 to 2026-11-01); last 30 days 34,356 (-12.1% vs prior 30); -52.2% vs a year ago; peak 4,044 on 2025-12-23
• interestIndex, trend, change30dPct, yoyChangePct: the verdict
• monthlyViews: the series

The run's key-value store has a SUMMARY record with a ranking, a one-line takeaway and nextStep. Topics without an article come back with a status and are not charged.

Pricing

Apify paid-plan discounts: Bronze members pay 10% less, Silver 20% less and Gold 30% less per topic. Free-plan prices are shown below; the discount applies automatically.

$0.002 per topic tracked (pay-per-event), plus a tiny Actor-start fee of $0.00005. Any period up to 10 years, monthly and daily series, Excel, CSV and summary are included. Topics with no Wikipedia article or no pageviews cost nothing. Your maximum charge per run is respected.

FAQ

Is this the same as Google Trends? No, and that is the point. Google Trends shows relative search interest; this shows how many people actually read about a topic on Wikipedia, as real numbers. The two usually move together for news, products and brands, and Wikipedia data is open, stable and not rate-limited.

Where does the data come from? The Wikimedia Foundation's public pageviews API (data from July 2015, updated daily). The last full day available is yesterday (UTC).

My topic matched the wrong article. Paste the exact Wikipedia link (e.g. https://en.wikipedia.org/wiki/Mercury_(planet)) or a more specific name. The matched article is shown in every row.

Can a brand without a Wikipedia article be tracked? No; such topics are listed with no Wikipedia article found and are free. Most notable brands, products, drugs, technologies, people and places have one.

Can I schedule it? Yes. Save your input as a task and schedule it weekly or monthly; each run is a fresh snapshot.

Can I use it from code or an AI agent? Yes, via the Apify API, the Python/JS clients, or as a tool through Apify's MCP server.

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Questions or ideas? Open an issue on this Actor's Issues tab.

Changelog

• 2026-11-02: First public release: topic-to-article matching (redirects, disambiguation, links, any language), daily and monthly pageviews, 30-day and year-over-year change, trend label, peaks and spikes, interest index, Google-Trends-style Excel and CSV, agent-friendly agentSummary and takeaway, pay-per-event pricing with Bronze/Silver/Gold discounts.