# Find rising related queries for a keyword in the US

**Use case:** 

Runs Google Trends Data API for one keyword in the United States over the last 12 months and returns the top and rising related queries. It uses the related_queries data type only, so each dataset row is one related query with its rank type, value and scale label (0-100 index for top queries, growth percent for rising ones). Change the keyword, region or time range for your own research.

## Input

```json
{
  "mode": "keywords",
  "keywords": [
    "air fryer"
  ],
  "startUrls": [],
  "geo": "US",
  "timeRange": "today 12-m",
  "dataTypes": [
    "related_queries"
  ],
  "compareKeywords": false,
  "includeKeywordInsights": false,
  "searchType": "",
  "category": "0",
  "language": "en-US",
  "timezoneOffsetMinutes": 0,
  "trendingHours": "24",
  "trendStatus": "all",
  "maxItems": 50,
  "maxRunSeconds": 3600,
  "maxRetriesPerRequest": 8,
  "rotateAfterRequests": 24,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

## Output

```json
{
  "keyword": {
    "label": "Keyword"
  },
  "term": {
    "label": "Trending term"
  },
  "dataType": {
    "label": "Dataset"
  },
  "date": {
    "label": "Date"
  },
  "dateLabel": {
    "label": "Date (Google label)"
  },
  "geoName": {
    "label": "Region"
  },
  "geoCode": {
    "label": "Region code"
  },
  "query": {
    "label": "Related query"
  },
  "rankType": {
    "label": "Top / Rising"
  },
  "value": {
    "label": "Value"
  },
  "formattedValue": {
    "label": "Formatted"
  },
  "valueScale": {
    "label": "Scale"
  },
  "searchVolume": {
    "label": "Search volume"
  },
  "growthPercent": {
    "label": "Growth %"
  },
  "isActive": {
    "label": "Active"
  },
  "breakdownQueries": {
    "label": "Breakdown queries"
  },
  "trendsUrl": {
    "label": "Trends URL"
  },
  "link": {
    "label": "Link"
  },
  "hasData": {
    "label": "Has data"
  },
  "geo": {
    "label": "Geo"
  },
  "timeRange": {
    "label": "Time range"
  },
  "categoryIds": {
    "label": "Category IDs"
  },
  "comparisonGroup": {
    "label": "Comparison group"
  },
  "startTimestamp": {
    "label": "Started (epoch)"
  },
  "endTimestamp": {
    "label": "Ended (epoch)"
  },
  "timestamp": {
    "label": "Epoch"
  },
  "newsArticleCount": {
    "label": "News articles"
  },
  "hours": {
    "label": "Window (h)"
  }
}
```

## About this Actor

This example demonstrates how to use [Google Trends Scraper and Data API](https://apify.com/danthedataman/google-trends-data-api.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/danthedataman/google-trends-data-api.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/danthedataman/google-trends-data-api.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).