Twitter Trends Scraper

Twitter Trends Scraper

The Twitter Trends Scraper collects trending topics and tweet volumes from public trend listings in near real time. It solves the manual refresh grind by automating structured extraction of what’s hot on X/Twitter by region and time snapshot. Built for marketers, developers, data analysts, and researchers, this Twitter trending topics scraper doubles as a real-time Twitter trends scraper and Twitter trends monitoring tool so you can discover, compare, and track trends at scale.

What data / output can you get?

This actor saves results to the Apify dataset row by row while it runs. Each record represents a single trend at a given snapshot.

Data type	Description	Example value
time	UTC timestamp when the item was captured	2026-04-02T11:19:15.733Z
timePeriod	Snapshot label derived from your input (e.g., Live, 1 hour ago, Yesterday)	Live
trend	The trend/topic/hashtag text as listed on the source page	#LaCasaDeLosFamososMx3
volume	Tweet volume text if available on the source page	35.2K tweets

Notes:

• Results can be downloaded from the dataset in CSV, JSON, or Excel.
• The run also stores a summary file named RUN_SUMMARY.json in the key‑value store with runStartedAt, proxyUsed, and stats (including per_timePeriod counts and failed_urls) for quick diagnostics.

Key features

• 🚀 Real-time and historical snapshots — Toggle Live, hourly (1–23 hours ago), and daily summaries (Yesterday, Week Ago, Month Ago, Year Ago, Day 3) to build time-based trend histories.
• 🌍 Worldwide and country-specific coverage — Choose Country (e.g., US, GB, IN, JP, etc.) or leave empty for worldwide trends; the scraper adjusts URLs accordingly.
• 🧭 Resilient parsing — Uses multiple XPath strategies to adapt to layout changes and reliably extract trend names and volumes.
• 🔁 Built-in retries — Automatic HTTP retries with exponential backoff to minimize transient failures and improve success rates.
• 🧪 Proxy reliability with fallback — Accepts standard Apify proxy configuration; if a selected proxy is rejected, it automatically falls back to Apify RESIDENTIAL for continuity.
• 🗃️ Live row-by-row saving — Streams records to the dataset as they’re parsed so you can start consuming results immediately.
• 💾 Flexible exports — Export datasets to CSV, JSON, or Excel for analytics, BI dashboards, or downstream pipelines.

How to use Twitter Trends Scraper - step by step

1. Sign in to Apify

   • Open the actor in Apify and click Try for free to launch a new run.

2. Select your time snapshots

   • Toggle Live for the current trends.
   • Toggle any hourly flags (hour1 … hour23) for 1–23 hours ago.
   • Use Yesterday, Week Ago, Month Ago, Year Ago, or Day 3 for historical summaries.

3. Choose a country (optional)

   • Set Country to a supported code (e.g., US, GB, IN) to get country‑specific trends, or leave it empty for worldwide.

4. Configure proxy (optional)

   • In Proxy configuration, you can enable Apify Proxy or keep the default (no proxy). If a selected proxy is rejected, the actor automatically falls back to Apify RESIDENTIAL.

5. Start the run

   • Click Start. The run log will show which URLs and time periods are being scraped and whether a proxy is in use.

6. Monitor progress

   • Items are pushed to the dataset as they are extracted. Logs will show item counts per timePeriod.

7. Export results

   • Open the Dataset tab to download your results as CSV, JSON, or Excel. Check the Key‑Value Store for RUN_SUMMARY.json with run statistics.

Pro tip: Schedule recurring runs on Apify to build a rolling feed for your Twitter trends tracker or to power a Twitter trends analytics tool.

Use cases

Use case name	Description
Marketing + content planning	Identify trending hashtags and topics to inform campaigns and editorial calendars with fresh, regional insights.
Newsroom trend monitoring	Track breaking topics hourly or daily to prioritize coverage and run timely social segments.
Brand & product intelligence	Monitor conversations related to your industry or brand across countries to spot spikes and shifts in attention.
Competitive & influencer watch	Observe topics amplified by competitors and creators to benchmark attention and discover gaps.
Academic & media research	Collect structured time-series trend snapshots for studies in media effects, diffusion, and public discourse.
Data engineering pipelines	Feed a Twitter trends dataset scraper into ETL and BI workflows for dashboards and alerts.
Automated reporting	Build an automated Twitter trends tracker that exports CSV/JSON into downstream analytics tools.

Why choose Twitter Trends Scraper?

This actor is engineered for reliability, scale, and straightforward integration on the Apify platform.

• 🎯 Accurate extraction: Multi-selector parsing handles minor layout shifts and keeps trend capture consistent.
• 🕒 Time-series ready: Hourly and daily snapshots enable longitudinal comparison without manual effort.
• 🌐 Country-aware: Supports worldwide plus a broad set of country codes for localized insights.
• 🛡️ Robust networking: Automatic retries and proxy fallback to Apify RESIDENTIAL reduce run failures.
• ⚙️ Developer-friendly: Runs on Apify with dataset outputs that export easily to CSV/JSON/Excel for pipelines.
• 🔒 No login required: Scrapes from public trend listings—no cookies or accounts needed.
• 💸 Cost-effective at scale: Schedule runs to automate a real-time Twitter trends tracker without maintaining custom infrastructure.

Bottom line: a production-ready Twitter trends crawler that prioritizes stability and clean outputs over brittle, ad-hoc scripts.

Is it legal / ethical to use Twitter Trends Scraper?

Yes, when used responsibly. This actor collects public trend listings and does not access private data.

• Public vs. private: Stick to public trend pages and avoid private or authenticated content.
• Compliance guidelines: Ensure your usage complies with applicable laws (e.g., GDPR/CCPA), website terms, and organizational policies.
• Data boundaries: The actor does not access private profiles, protected content, or DMs.
• Due diligence: For edge cases or commercial redistribution, consult your legal team.

Input parameters & output format

Example input

{
  "live": true,
  "hour1": true,
  "yesterday": true,
  "weekAgo": false,
  "monthAgo": false,
  "yearAgo": false,
  "day2": false,
  "day3": false,
  "country": "US",
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": [],
    "apifyProxyCountry": "US",
    "proxyUrls": []
  }
}

Input parameters (from schema)

• live (boolean): Scrape the current live trends. Default: not specified. Required: no.
• hour1 (boolean): Scrape trends from 1 hour ago. Default: not specified. Required: no.
• hour2 (boolean): Scrape trends from 2 hours ago. Default: not specified. Required: no.
• hour3 (boolean): Scrape trends from 3 hours ago. Default: not specified. Required: no.
• hour4 (boolean): Scrape trends from 4 hours ago. Default: not specified. Required: no.
• hour5 (boolean): Scrape trends from 5 hours ago. Default: not specified. Required: no.
• hour6 (boolean): Scrape trends from 6 hours ago. Default: not specified. Required: no.
• hour7 (boolean): Scrape trends from 7 hours ago. Default: not specified. Required: no.
• hour8 (boolean): Scrape trends from 8 hours ago. Default: not specified. Required: no.
• hour9 (boolean): Scrape trends from 9 hours ago. Default: not specified. Required: no.
• hour10 (boolean): Scrape trends from 10 hours ago. Default: not specified. Required: no.
• hour11 (boolean): Scrape trends from 11 hours ago. Default: not specified. Required: no.
• hour12 (boolean): Scrape trends from 12 hours ago. Default: not specified. Required: no.
• hour13 (boolean): Scrape trends from 13 hours ago. Default: not specified. Required: no.
• hour14 (boolean): Scrape trends from 14 hours ago. Default: not specified. Required: no.
• hour15 (boolean): Scrape trends from 15 hours ago. Default: not specified. Required: no.
• hour16 (boolean): Scrape trends from 16 hours ago. Default: not specified. Required: no.
• hour17 (boolean): Scrape trends from 17 hours ago. Default: not specified. Required: no.
• hour18 (boolean): Scrape trends from 18 hours ago. Default: not specified. Required: no.
• hour19 (boolean): Scrape trends from 19 hours ago. Default: not specified. Required: no.
• hour20 (boolean): Scrape trends from 20 hours ago. Default: not specified. Required: no.
• hour21 (boolean): Scrape trends from 21 hours ago. Default: not specified. Required: no.
• hour22 (boolean): Scrape trends from 22 hours ago. Default: not specified. Required: no.
• hour23 (boolean): Scrape trends from 23 hours ago. Default: not specified. Required: no.
• yesterday (boolean): Scrape yesterday's full-day summary. Default: not specified. Required: no.
• weekAgo (boolean): Scrape trends from 1 week ago. Default: not specified. Required: no.
• monthAgo (boolean): Scrape trends from 1 month ago. Default: not specified. Required: no.
• yearAgo (boolean): Scrape trends from 1 year ago. Default: not specified. Required: no.
• day2 (boolean): Day 2 (Yesterday) - Legacy (same as 'yesterday'). Default: not specified. Required: no.
• day3 (boolean): Scrape the summary from 2 days ago. Default: not specified. Required: no.
• country (string): Select a country to scrape country-specific Twitter trends. Leave empty for worldwide trends. Default: "" (worldwide). Required: no.
• proxy (object): Proxy configuration. Defaults to no proxy. If the selected proxy is rejected, the actor falls back to Apify RESIDENTIAL automatically. Required: no.

Example dataset output

[
  {
    "time": "2026-04-02T11:19:15.733Z",
    "timePeriod": "Live",
    "trend": "#LaCasaDeLosFamososMx3",
    "volume": "35.2K tweets"
  },
  {
    "time": "2026-04-02T11:19:15.733Z",
    "timePeriod": "1 hour ago",
    "trend": "Chiefs",
    "volume": "78.7K tweets"
  },
  {
    "time": "2026-04-02T11:19:15.733Z",
    "timePeriod": "Yesterday",
    "trend": "Giants",
    "volume": "100.8K tweets"
  }
]

Run summary file (saved to key‑value store as RUN_SUMMARY.json)

{
  "runStartedAt": "2026-04-02T11:19:15.733Z",
  "proxyUsed": "APIFY_PROXY(auto)",
  "stats": {
    "total_items": 120,
    "per_timePeriod": {
      "Live": 50,
      "1 hour ago": 40,
      "Yesterday": 30
    },
    "failed_urls": []
  }
}

Fields that may be empty: volume may be an empty string if the source page does not display a count for a given trend.

FAQ

Does this Twitter Trends Scraper pull data directly from Twitter/X?

Yes, it captures Twitter/X trends via public trend listings on getdaytrends.com, without needing Twitter login or API access. It extracts the trend text and available volume as displayed publicly.

What countries are supported?

You can leave Country empty for worldwide or select from a broad list including US, GB, IN, JP, BR, and many others defined in the Country input. The actor maps these to the correct country pages automatically.

What outputs do I get?

Each dataset item includes time, timePeriod, trend, and volume. A RUN_SUMMARY.json file is also saved with runStartedAt, proxyUsed, and stats to help you audit the run.

Do I need a login or cookies to run it?

No. The scraper works without authentication, relying on publicly accessible pages.

Can I use it as a real-time Twitter trends tracker?

Yes. Enable Live and the hourly flags (hour1 … hour23) and schedule runs on Apify to build a real-time Twitter trends tracker with historical snapshots.

Is there an API or Python integration?

Yes. This is an Apify actor, so you can trigger runs and fetch datasets via the Apify API, or integrate with your Python pipelines using standard HTTP requests to the dataset endpoints.

How reliable is it at scale?

The actor includes automatic retries with exponential backoff and a proxy reliability mechanism that falls back to Apify RESIDENTIAL if the selected proxy is rejected. It also saves results row by row to minimize data loss.

Is it legal to scrape these trends?

Yes, when used responsibly. The actor collects public trend data and does not access private or authenticated content. Ensure compliance with relevant laws and terms for your use case.

Closing CTA / Final thoughts

This Twitter Trends Scraper is built for dependable, structured extraction of trending topics and volumes across time and regions. With real-time and historical snapshots, resilient parsing, and proxy fallback, it delivers clean datasets for marketers, analysts, researchers, and developers. Export to CSV/JSON/Excel, orchestrate runs via the Apify API, and automate your Twitter trends data extraction pipeline with confidence. Start tracking smarter social insights today.