Google Trends Scraper: Rising & Breakout Keywords

Google Trends Scraper — Rising Keywords, Spikes and Momentum

Google Trends Scraper: Rising & Breakout Keywords fetches Google Trends interest-over-time for any keyword list and layers on statistical spike detection, momentum, and velocity — computed directly from the fetched series, at no extra request cost. Each result row carries the keyword's full timeline, its detected anomaly points (spikes), a rising/falling momentum direction, and an isSpiking flag. It's built for SEO and content strategists, marketing agencies tracking client keyword sets, and market researchers who need to know not just what's trending, but exactly when a keyword broke out of its normal range.

What is Google Trends Scraper: Rising & Breakout Keywords?

Google Trends Scraper: Rising & Breakout Keywords is an Apify Actor that pulls Google Trends' public interest-over-time index for one or more keywords and derives spike, momentum, and velocity metrics from that series in the same run. No Google account or API key is required — Google Trends is a free, public tool. Key capabilities:

• Monitor keywords by typing them directly, pasting full Google Trends URLs, or loading a list from a public Google Sheet
• Compare up to 5 keywords together as one group when group comparison mode is on
• Flag anomaly points using a rolling-baseline z-score and percent-change test, tunable via three sensitivity presets
• Compute momentum (rising/falling/flat direction and slope) and velocity (change per time step) over a configurable recent window
• Filter by region, viewed-from country, history window or an exact date range, and Trends topic category
• Turn the run into an alert feed with alertOnlySpikes, so only keywords that actually spiked are returned and charged

What data can I extract with Google Trends Scraper: Rising & Breakout Keywords?

Each row covers one keyword group's full interest timeline plus the derived anomaly analysis:

Field	Example Value	Use Case
searchTerm	"chatgpt"	Identify which keyword or comparison group the row covers
timeRange	"today 12-m"	Confirm the analyzed history window
geo	"US"	Confirm the region the interest data reflects
category	"5"	Confirm the Trends topic category applied, if any
interestOverTime_timelineData	[{"date": "2026-06-01", "chatgpt": 62}, ...]	The full raw interest-index series behind every derived metric
spikes	[{"date": "2026-07-02", "keyword": "chatgpt", "value": 94, "baseline": 58.1, "zScore": 3.8, "pctChange": 61.8, "severity": "major"}]	List every detected anomaly point with its statistical evidence
isSpiking	true	Check whether the latest data point is itself a spike
spikeCount	4	Gauge how volatile a keyword has been over the window
momentum	{"direction": "rising", "slope": 1.42, "changePercent": 18.6}	See the recent trend direction and its strength
velocity	1.42	Get the average interest-point change per time step
peakValue / peakDate	94 / "2026-07-02"	Find the single highest point in the analyzed window
latestValue	81	Read the most recent interest-index value
baselineMean	58.1	See the "normal" level the latest value is compared against
perKeywordMetrics	[{...}]	Get spike/momentum/velocity broken out per keyword when comparing a group
spikeSensitivity / appliedZThreshold / appliedPctThreshold	"medium" / 2.5 / 50.0	Confirm exactly which thresholds produced this row's spike flags
resultType	"spike"	Identify the row type for downstream filtering
scrapedAt	"2026-07-26T14:02:11Z"	Record when the run fetched this data

Spike and momentum analytics

The spikes array is the decision-driving data: each entry names the anomalous date, the raw value, the rolling baseline mean it was compared against, its z-score, its percent change, and a severity bucket (minor/major/extreme based on the z-score). This is a statistical anomaly test the Actor computes itself from the fetched interest-over-time series — a rolling mean/standard-deviation baseline over the preceding baselineWindow points, flagged when the z-score or percent-change crosses the threshold set by spikeSensitivity. It is not Google's own "Breakout" label from the Related Queries panel (Google's proprietary >5000%-growth designation) — this Actor never fetches that panel at all. momentum and velocity give the recent slope and per-step change, useful for telling a keyword that spiked once from one that is steadily climbing.

Region, timeframe and category targeting

geo/regionCode scope results to a single country (or worldwide if left empty), historyWindow picks a preset lookback (from the last hour to 5 years or all-time), and exactDateWindow overrides that with a precise YYYY-MM-DD YYYY-MM-DD range. topicCategory narrows to one of Google Trends' own categories (e.g. Business, Health) instead of all categories. Combine region and category to isolate, for example, US-only "Health" search interest rather than global all-category volume.

Why not build this yourself?

Google Trends has no official, scriptable bulk API — it is a free public dashboard, not a REST product with documented endpoints or a Terms-of-Service rate card. Any programmatic access relies on Google's undocumented internal widget endpoints, which aggressively rate-limit and CAPTCHA-block requests from datacenter IPs and share no published quota. This Actor's source code carries a three-tier proxy escalation ladder (none → datacenter → residential) that automatically retries a blocked or rate-limited request on a different connection method, plus a configurable retry count for transient failures — logic a self-built script would need to write and maintain from scratch, with no official documentation to build against. On top of that, the spike/momentum math (rolling baseline, z-score, severity buckets) is derived entirely from the fetched series in the same request, so building an equivalent tool yourself means writing and testing that statistics layer too. Use Google Trends' own website directly for a one-off, single-keyword check; use this Actor when you need the same data for many keywords, on a schedule, with anomaly detection already computed.

How to use data extracted from Google Trends?

SEO and content strategists

Feed a content calendar's target keyword list into monitorKeywords, run on a schedule, and use isSpiking plus momentum.direction to decide which topics deserve a new article or a refresh right now rather than next quarter. The spikes array's date and severity fields let you correlate a traffic spike in your own analytics with a real, dated search-interest surge — useful evidence when prioritizing a backlog of draft topics against genuinely rising demand instead of guessing from gut feel.

Marketing agencies benchmarking client keyword sets

Agencies running keyword monitoring for multiple clients can pass each client's tracked terms as a separate monitorKeywords list (or groupComparisonMode to compare a client's brand term against competitors in one row), then use alertOnlySpikes so the run only returns — and only charges for — keywords that actually moved, turning a recurring scheduled run into a lightweight client alert feed rather than a full report to manually re-scan every time.

Market and consumer research

Track momentum.direction and velocity across a category's keyword set (using topicCategory) to see which sub-topics are accelerating versus flattening within a region, using regionCode to compare interest patterns market to market. The full interestOverTime_timelineData array supports building a longer-horizon chart rather than relying on the derived summary fields alone.

AI agents and automated pipelines

An agent can call this Actor on a schedule with alertOnlySpikes enabled to get a clean, pre-filtered signal — only keyword groups with a genuine anomaly are pushed — for feeding into an automated content-brief generator, a trading-signal pipeline, or a RAG knowledge base that needs to know when public interest in a topic has structurally shifted.

🔼 Input sample

Parameter	Required	Type	Description	Example Value
monitorKeywords	No	array	Keywords/topics to watch for rising interest and spikes. Required unless trendsUrls or sheetId is supplied.	["chatgpt", "ozempic"]
spikeSensitivity	No	string (enum)	How aggressively to flag spikes: low (z≥3.0 or +75%), medium (z≥2.5 or +50%, default), high (z≥2.0 or +35%)	"medium"
alertOnlySpikes	No	boolean	When true, only keyword groups with at least one detected spike are returned and charged	false
groupComparisonMode	No	boolean	When true, commas inside one keyword string split it into a compared group (up to 5 keywords)	false
historyWindow	No	string (enum)	Preset lookback: now 1-H, now 4-H, now 1-d, now 7-d, today 1-m, today 3-m, today 12-m, today 5-y, all	"today 12-m"
regionCode	No	string (enum)	Country/region ISO code to localize trend data; empty for Worldwide	"US"
baselineWindow	No	integer (min 2)	Preceding points used to compute the rolling spike baseline	7
momentumWindow	No	integer (min 2)	Most-recent points used to compute momentum and velocity	7
viewerCountry	No	string (enum)	Country context used when viewing Trends pages	"us"
disableSnapshots	No	boolean	Kept for compatibility with the base actor; has no effect (this variant uses an HTTP engine, no browser)	false
trendsUrls	No	array	Full Google Trends URLs to reuse their keyword/geo/date/category parameters	[]
sheetId	No	string	Public Google Sheet ID to load keywords from (single column, row 1 = header)	""
topicCategory	No	string (enum)	Google Trends category ID to narrow results to	""
maxKeywordRows	No	integer (min 0)	Maximum output rows (keyword groups); 0 = unlimited	0
exactDateWindow	No	string	Exact YYYY-MM-DD YYYY-MM-DD range, overrides historyWindow	""
fetchConcurrency	No	integer (min 1)	Kept for compatibility with the base actor; has no effect (fetching is sequential)	10
requestRetryLimit	No	integer (min 1)	Retry attempts per request before escalating the proxy tier	7
requestTimeoutSecs	No	integer (min 30)	Timeout for auxiliary fetches (e.g. loading a Google Sheet)	180
proxyConfiguration	No	object	Apify proxy configuration; residential proxies help when Google rate-limits datacenter IPs	{"useApifyProxy": true}

{
  "monitorKeywords": ["chatgpt", "ozempic"],
  "spikeSensitivity": "medium",
  "historyWindow": "today 12-m",
  "regionCode": "US",
  "alertOnlySpikes": true
}

Common pitfall: fetchConcurrency and disableSnapshots are accepted for compatibility with the base Google Trends scraper's input shape but have no effect in this variant — fetching always runs sequentially through trendspy, and there is no browser/screenshot layer to disable. Setting them does not change run behavior or speed.

🔽 Output sample

Output is typed, normalized JSON — one row per keyword group, delivered to the Actor's dataset (exportable as JSON, CSV, Excel, or via API).

{
  "inputUrlOrTerm": "chatgpt",
  "searchTerm": "chatgpt",
  "timeRange": "today 12-m",
  "geo": "US",
  "category": "0",
  "interestOverTime_timelineData": [
    {"date": "2026-06-01", "chatgpt": 58},
    {"date": "2026-06-08", "chatgpt": 61},
    {"date": "2026-07-02", "chatgpt": 94}
  ],
  "spikes": [
    {
      "date": "2026-07-02",
      "keyword": "chatgpt",
      "value": 94,
      "baseline": 58.1,
      "zScore": 3.8,
      "pctChange": 61.8,
      "severity": "major"
    }
  ],
  "isSpiking": false,
  "spikeCount": 1,
  "momentum": {"direction": "rising", "slope": 1.42, "changePercent": 18.6},
  "velocity": 1.42,
  "peakValue": 94,
  "peakDate": "2026-07-02",
  "latestValue": 81,
  "baselineMean": 58.1,
  "perKeywordMetrics": [
    {
      "keyword": "chatgpt",
      "spikeCount": 1,
      "isSpiking": false,
      "momentum": {"direction": "rising", "slope": 1.42, "changePercent": 18.6},
      "velocity": 1.42,
      "peakValue": 94,
      "peakDate": "2026-07-02",
      "latestValue": 81,
      "baselineMean": 58.1
    }
  ],
  "spikeSensitivity": "medium",
  "appliedZThreshold": 2.5,
  "appliedPctThreshold": 50.0,
  "resultType": "spike",
  "scrapedAt": "2026-07-26T14:02:11Z"
}

The dataset's default table view surfaces 11 of these fields (searchTerm, isSpiking, spikeCount, momentum, velocity, spikeSensitivity, peakValue, peakDate, spikes, interestOverTime_timelineData, scrapedAt); the remaining fields shown above (inputUrlOrTerm, timeRange, geo, category, latestValue, baselineMean, perKeywordMetrics, appliedZThreshold, appliedPctThreshold, resultType) are still written to every row and available via the API or a custom dataset view.

How do you filter and target specific keywords?

There are three ways to supply what to monitor, and each fits a different workflow: type keywords directly into monitorKeywords for ad hoc tracking; paste one or more trendsUrls to reuse an existing Google Trends URL's exact keyword, region, date, and category configuration; or point sheetId at a public Google Sheet to drive a recurring run off a maintained list without editing the Actor's input each time. For scope precision, combine regionCode (or leave it empty for worldwide) with topicCategory to isolate, say, US-only "Health" interest rather than all-category global volume, and use historyWindow for a preset lookback or exactDateWindow for a precise custom range. The closest thing to a quality threshold here is spikeSensitivity: low only flags strong surges (z≥3.0 or +75% over baseline), while high catches smaller, earlier moves (z≥2.0 or +35%) at the cost of more false positives on noisy series. Combine that with alertOnlySpikes as the volume control — turned on, quiet keywords with zero detected spikes are dropped from the output (and not charged) entirely, so a large watchlist returns only the subset that's actually moving.

{ "monitorKeywords": ["ozempic", "wegovy", "mounjaro"], "spikeSensitivity": "high", "alertOnlySpikes": true }

{ "monitorKeywords": ["bitcoin, ethereum"], "groupComparisonMode": true, "regionCode": "US", "historyWindow": "today 3-m" }

{ "trendsUrls": ["https://trends.google.com/trends/explore?q=chatgpt&geo=US&date=today%2012-m"], "spikeSensitivity": "low" }

▶️ Want to try other research scrapers?

No other Google Trends actor currently exists in this catalogue. If you're tracking trend momentum on other platforms, these cover a comparable rising/momentum-analytics angle:

Scraper Name	What it extracts
TikTok Trending Hashtags With Momentum Analytics Scraper	Trending TikTok hashtags with momentum/velocity analytics
TikTok Trending Videos Insights With Hashtag Trend Ad Matcher	Trending TikTok video and ad-creative insights matched to hashtag trends
Google Patents Scraper With Legal Status & Ownership Details	Patent records with legal-status and ownership research data
Google Hotels — Location Details Scraper	Hotel listings with rates and location details

How to extract Google Trends data programmatically

Every run is a standard Apify Actor call — one API request with your token, JSON input, JSON results back. No Google account or API key needed.

Python example

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_API_TOKEN>")

run_input = {
    "monitorKeywords": ["chatgpt", "ozempic"],
    "spikeSensitivity": "medium",
    "regionCode": "US",
    "alertOnlySpikes": True,
}

run = client.actor("<YOUR_USERNAME>/google-trends-scraper-rising-breakout-keywords").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["searchTerm"], item["isSpiking"], item["momentum"]["direction"])

Export to spreadsheets or CRM

Export the dataset directly as CSV or Excel from the Actor run, then map searchTerm to a keyword column, isSpiking/spikeCount to an alert-status column, and momentum.direction/velocity to trend-direction columns in a content calendar or CRM import template.

Is it legal to scrape Google Trends?

Yes. Google Trends is itself a free, public tool that publishes aggregated, anonymized search-interest data with no login wall and no personal data attached to any keyword or timeline point — there are no reviewer names, contact details, or individual users in the output. The applicable framework here is Google's own Terms of Service on automated querying rather than GDPR/CCPA, since nothing in the output identifies an individual. Consult legal counsel for commercial applications involving high-volume or bulk automated querying of Google's services.

❓ FAQ

Does "Breakout" in the title mean this returns Google's own Breakout label?

No. Google Trends' Related Queries panel has its own proprietary "Breakout" designation (roughly >5000% growth), but this Actor never fetches that panel. It computes its own statistical anomaly detection — a rolling z-score and percent-change test — directly from the interest-over-time series, which is a different (and fully documented, in-source) mechanism.

How accurate is the spike detection?

It's a transparent statistical test, not a black box: each spike shows its baseline mean, z-score, and percent change, so you can verify why a point was flagged. Accuracy for your use case depends on spikeSensitivity — low misses smaller genuine moves to avoid false positives, high catches more moves but flags more noise.

Can I get the full raw interest-over-time series, not just the spikes?

Yes — every row includes interestOverTime_timelineData, the complete date/value series behind the derived spike, momentum, and velocity fields, so you can build your own charts or run different analysis alongside the Actor's.

How many keyword groups can I get per run?

Set maxKeywordRows to cap the number of output rows; 0 (the default) means unlimited, and the run processes every keyword group you supply via monitorKeywords, trendsUrls, or sheetId.

What does alertOnlySpikes actually change?

When enabled, keyword groups with zero detected spikes are skipped entirely — no row is pushed and no charge occurs for them — so a large watchlist returns (and bills for) only the keywords that are actually moving.

Does Google Trends Scraper: Rising & Breakout Keywords work with Claude, ChatGPT, and AI agent frameworks?

It has no dedicated MCP server registration in this Actor's current setup, but it is callable as a standard Apify API endpoint by any agent framework that can make an authenticated HTTP request and parse JSON.

How does this compare to other Google Trends scrapers?

No independently verifiable competitor listing could be confirmed at the time of writing, so no specific comparison is made here. In general, this Actor's distinguishing feature relative to a raw interest-over-time fetch is the built-in spike/momentum/velocity layer computed at no extra request cost.

Can I use this without a Google account or API key?

Yes. Google Trends is a public tool with no login requirement, so no Google credentials, API key, or developer account are needed — only your Apify account to run the Actor.

Can I compare multiple keywords against each other in one row?

Yes — set groupComparisonMode to true and separate keywords with commas in one monitorKeywords entry (up to 5 per group); the row's perKeywordMetrics array then breaks out spike, momentum, and velocity per keyword within that comparison.

What happens if a keyword has too little search volume?

Google Trends returns no data for very low-volume terms, and the Actor logs a warning and skips that keyword group rather than pushing an empty or fabricated row — it will not appear in the output dataset.

Conclusion

Google Trends Scraper: Rising & Breakout Keywords turns Google's free public interest-over-time data into a structured, anomaly-flagged feed — spike detection, momentum, and velocity computed in the same run, with no extra Google requests. It's built for SEO strategists, marketing agencies, and researchers who need to know exactly when and how much a keyword's interest shifted, not just its current trend line. Start a run with your keyword list, a sensitivity level, and alertOnlySpikes if you only want the signal, not the noise.