tanykim/twitter-year-analysis — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2018-01-06
Analyze your downloaded Twitter archive to see how many tweets you posted in a given year.
Find out what time of day or season you tweet the most.
Feed Twitter activity insights into a personal 'Quantify Your Year' style project.
Explore how your tweeting habits changed throughout the year from raw archive data.
| tanykim/twitter-year-analysis | a-bissell/unleash-lite | abhiinnovates/whatsapp-hr-assistant | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | Python | Python | Python |
| Last pushed | 2018-01-06 | — | — |
| Maintenance | Dormant | — | — |
| Setup difficulty | moderate | hard | hard |
| Complexity | 2/5 | 4/5 | 3/5 |
| Audience | general | researcher | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires manually downloading your Twitter archive and editing year/timezone settings in the script.
This is a tool that takes your personal Twitter history and generates year-in-review statistics about your tweeting habits. If you've ever wondered how many tweets you posted in 2023, what time of day you tweet most, or how your activity changes throughout the year, this script analyzes your archive to show you those patterns. To use it, you download your complete Twitter archive directly from Twitter's settings (which gives you a CSV file of all your tweets), then drop that file into the same folder as this script. After editing a couple of settings, your desired year and timezone, you run the code and it processes your tweet data and generates analysis. The timezone setting matters because it ensures the dates and times are calculated correctly for your location. The project is designed for a service or project called "Quantify Your Year," which appears to be about creating personal statistics and insights from your online activity. Someone working on that project might use this to automatically pull Twitter insights, or an individual curious about their own tweeting patterns could run it themselves. For instance, if you wanted to know whether you tweet more in summer or winter, or whether you're a morning tweeter or a night owl, this would give you those answers by crunching the raw data from your archive. The code is written in Python 3.6, a common programming language for data analysis. The README is quite minimal and doesn't explain what specific statistics it generates or how to interpret the output, so you'd need to look at the actual code to see exactly what kind of analysis it performs.
A Python script that reads your downloaded Twitter archive and generates year-in-review stats on when and how often you tweeted.
Mainly Python. The stack also includes Python.
Dormant — no commits in 2+ years (last push 2018-01-06).
License is not stated in the available content.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly general.
This repo across BitVibe Labs
double-check against the repo, no cap.