What 312 ChatGPT posts on Threads reveal about Indonesian creators
Between January and March 2026, Indonesian Threads users published over 312 posts explicitly about ChatGPT — covering everything from office productivity hacks to creative side hustles using AI image generation. The median post earned 59 likes, but the mean hit 460 — a 7.8× gap that tells the real story: a small number of posts earn most of the attention, and the patterns behind those outliers are learnable and repeatable.
Posts analyzed
- 312 ChatGPT-related Threads posts, Q1 2026
- Median likes: 59
- Mean likes: 460
- Peak post likes: 7,444
How to read this report. Each section follows the same structure: a plain-English finding, the chart or table backing it up, a callout with the practical implication, and a "What to do" action step. The report is divided into four analytical clusters — posting hours, verified account signal, thread length, and topic clusters — each validated independently using quantile-based (qcut) binning so one viral post cannot skew the results.
1. Posting Hour · When Should You Post?
Two windows are stable. The rest shift. Build your calendar around the stable ones.
The charts below show average likes (orange bars, left axis) and number of posts (blue dots, right axis) for each 3-hour WIB window — separately for January, February, and March. Averages use Q5–Q95 trimming, meaning the top and bottom 5% of likes per bin are excluded before calculating the mean. This removes viral outliers that would otherwise inflate a single time window.
Key observations:
- January (n=269): Late afternoon (16–18) and early morning (0–3) were standout windows.
- February (n=320): Strong morning and daytime pattern observed.
- March (n=678): Volume explosion (+112% vs February) seen, with higher engagement at the 4–6 and 19–21 windows.
| Hour bin (WIB) | Jan | Feb | Mar | Avg rank | Stable? | Note |
|---|---|---|---|---|---|---|
| 0–3:00 | 7 | 7 | 2 | 5.3 | shifts | Volatile — strong in March but weak in Jan/Feb. Not reliable as a default slot. |
| 4–6:00 | 2 | 6 | 8 | 5.3 | shifts | Pre-dawn slot. Consistently performs above average in isolation months but shifts wildly. |
| 7–9:00 | 6 | 8 | 5 | 6.3 | ~stable | Morning commute slot. Ranks 5–8 every month. Strong secondary publishing window. |
| 10–12:00 | 4 | 1 | 3 | 2.7 | ~stable | Consistently top-3 across all three months. Mid-morning is the safest primary window. |
| 13–15:00 | 3 | 4 | 1 | 2.7 | ~stable | Post-lunch peak. Matches 10–12 on stability. Use this as your co-primary window. |
| 16–18:00 | 8 | 2 | 6 | 5.3 | shifts | End-of-workday spike in January only. Unreliable across months — use as a test slot. |
| 19–21:00 | 1 | 5 | 7 | 4.3 | shifts | Evening slot is polarized: dominant in January, weak in March. Context-dependent. |
| 22–24:00 | 5 | 3 | 4 | 4.0 | ~stable | Late-night slot holds steady mid-rank. Useful for reposts/reminders or second-wave content. |
Key finding: The 10:00–12:00 and 13:00–15:00 windows both hold an average rank of 2.7/8 across three months.
2. Verified Account Signal · Does the Blue Check Still Matter?
Verified accounts are 63% overrepresented in the viral quintile
Of the 312 posts analyzed, 46 (14.7%) came from verified accounts. Posts from verified accounts generated 75% higher average likes than non-verified accounts.
- Avg likes for verified accounts: 196
- Median likes: 103
- Avg replies: 26
- Avg likes for non-verified accounts: 112
- Median likes for non-verified: 51
What this means: Verification shifts the probability distribution upward, helping content distribution.
3. Thread Length · How Long Should Your Post Be?
321–560 characters earns 129% more likes than posts under 110 characters
The pattern shows engagement rises as posts get longer — but only up to a point.
- Q1 (0–110 chars): Avg likes 72
- Q4 (321–560 chars): Avg likes 165
- Q5 (>560 chars): Avg likes 131
Practical rule: Write posts that land in the 321–560 character window.
4. Topic Clusters · What Are People Actually Posting About?
6 distinct conversation clusters — each with different engagement dynamics
Each cluster has its own peak timing and content format. Understanding these clusters helps tailor content for better engagement.
| Topic Clusters | Avg likes | Share of voice |
|---|---|---|
| Productivity & Work Automation | 192 | 26.9% |
| Education & Learning Support | 155 | 22.8% |
| Business & Entrepreneurship | 218 | 18.6% |
| Career & Job Search | 143 | 15.1% |
| Creative Content & Side Hustles | 312 | 10.3% |
| Critical & Skeptical Perspectives | 389 | 6.4% |
5. Key Findings · The 5 Most Important Signals
The strengths of evidence suggest where to focus posting strategies:
- Two stable posting windows provide optimal engagement.
- Verification correlates with higher engagement rates.
- Longer posts perform better up to a threshold.
- Separate topic clusters have distinctive dynamics that should shape content.
6. Playbook · Actionable Recommendations
- Publish primarily at 10:00–12:00 WIB and secondarily at 13:00–15:00 WIB.
- Seek verification to capitalize on engagement shifts.
- Target length between 321–560 characters for optimal engagement.
Research Approach & Methodology:
- Dataset: 312 Threads posts matching ChatGPT-related keywords, published January–March 2026 by Indonesian users.
- Platform: Threads (Meta).
- Engagement score: calculated based on likes and replies.