X (Twitter) Active Hours & Posting Times Analyzer

Find out when any public X (Twitter) account is active. Enter a username and get a day-by-hour heatmap of their posting times, their peak days and hours, quiet (likely-asleep) hours, and an estimate of the account's time zone — no login or cookies required.

What you get

• Day-by-hour heatmap — a 7×24 grid showing how many posts land in each hour of each weekday, in both the account's local time and UTC
• Most active day and hour — the single peak weekday and hour
• Quiet hours — the hours with little or no activity (the account's likely offline/sleep window)
• Estimated time zone — auto-detected from the quietest posting window, with a confidence rating (or supply your own)
• Weekday vs. weekend split — what share of activity falls on weekdays vs. weekends
• Top time slots — the five busiest day+hour combinations
• Export to JSON, CSV, or Google Sheets directly from the Apify console

Use cases

• Best time to post / reach — schedule outreach, replies, or campaigns for when the account is actually online
• Audience and competitor research — understand a creator's or brand's posting rhythm
• Influencer vetting — spot accounts that post on a flat, automated schedule vs. organic human activity
• Lifestyle and time-zone inference — estimate where in the world an account is based

How it works

The analyzer samples the account's most recent tweets and buckets every timestamp by weekday and hour. Posting time is a strong proxy for when someone is active. The time zone is inferred from the circadian dip — the multi-hour window when the account posts least (its local night) — and you can always override it.

Note: this reflects posting activity, not real-time presence (no platform exposes that publicly). Scheduled/automated posts can flatten the pattern — which itself shows up as a "low confidence" time-zone estimate.

How to use

1. Enter the X username (with or without @; profile URLs work too)
2. Choose whether to include replies (on by default — replies capture casual, real-time activity)
3. Set Tweets to Analyze (default 500; more samples = a sharper pattern)
4. Optionally set a Time Zone Offset (e.g. -5, 1, 5.5) to skip auto-detection
5. Run the actor — the report appears in the Dataset tab

Output format

A single record per run:

{
  "username": "elonmusk",
  "platform": "x",
  "samplesAnalyzed": 500,
  "timeRange": { "earliest": "2024-05-01T...", "latest": "2024-06-03T...", "spanDays": 33 },
  "timezone": { "mode": "inferred", "offsetHours": -6, "label": "UTC-6", "confidence": "high", "note": "..." },
  "mostActiveDay": "Wednesday",
  "mostActiveHour": "20:00",
  "quietHours": ["03:00", "04:00", "05:00", "06:00"],
  "weekdayShare": 0.78,
  "weekendShare": 0.22,
  "topSlots": [{ "day": "Wednesday", "hour": "20:00", "count": 41, "share": 0.082 }],
  "hourHistogram": [/* 24 counts, local */],
  "dayHistogram": [/* 7 counts, Monday-first */],
  "dayNames": ["Monday", "...", "Sunday"],
  "heatmap": { "Monday": [/* 24 */], "...": [] },
  "heatmapUtc": { "Monday": [/* 24 */], "...": [] }
}

Build a visual heatmap straight from heatmap, or chart hourHistogram / dayHistogram for a quick view.

Related X (Twitter) actors

Part of a complete X (Twitter) toolkit — explore the rest of the suite:

• X (Twitter) Profile Scraper — Public profile data — bio, counts, links
• X (Twitter) User Tweets Scraper — Full timeline of any account by username
• X (Twitter) Tweet Scraper — Search and export tweets by query, hashtag, user
• X (Twitter) Tweet Replies Scraper — Every reply under a post for sentiment analysis
• X (Twitter) Quote Tweets Scraper — Every quote tweet of a post with engagement
• X (Twitter) Followers & Following Scraper — Full followers and following lists, no login
• X (Twitter) List Members Scraper — Members or followers of any public list
• X (Twitter) MCP Server — All X tools for AI agents via MCP