Drop #137 (2022-11-15): Quick Drop → Finding Needles in Your Twitter Archive Haystack

jq One-liner; Observable Tweets; twitter-archive-parser

Short newsletter today, as I spent too much time on the Day 09 #30DayMapChallenge entry (I'm cataloging all the entries in this Observable notebook).

The 🐦 ship appears to be rapidly sinking, so this drop is dedicated to some tools to help you dig through all your historical hot-takes (you have downloaded your Twitter Archive, right? If not, login to desktop Twitter and head to More > Settings and privacy > Your account > Download an archive of your data. This is called "Your Twitter Data" in the mobile app, and going to it just takes you to the web, but they boy billionaire may have had them assign random pet names to things, now, so YMMV. This link may work for you as well.)

jq One-liner

Ian Wootten has a quick post on how to sift through your Twitter Archive using the dreamy jq utility.

I'd elaborate, or post the script here, but Ian deserves the 👀, and it's a 1-minute read.

Observable Tweets

Photo by Nadine Shaabana on Unsplash

Observable notebooks can do pretty much anything, including handle your Twitter Archive, thanks to a cool notebook by Ian Johnson (@enjalot). All the processing is done locally, so you aren't giving up your Tweets to anyone just to see how much time you've burnt over the years.

You'll get:

• Monthly tweets, replies and retweets

• Popular tweets: Likes & Retweets

• Time of day and day of week

• Months and years

• Days of the month

• Days of the year

• Besties

• Find your friends

• Followers

and some handy idioms for data processing.

twitter-archive-parser

Photo by Hitesh Choudhary on Unsplash

If you're more comfy with Python at the CLI, then Tim Hutton (@tim_hutton) has you covered with this twitter-archive-parser utility, which:

• Converts the tweets to markdown with embedded images, videos and links.

• Replaces t.co URLs with their original versions.

• Copies used images to an output folder, to allow them to be moved to a new home.

• Afterwards, it asks if you want to try downloading the original size images using download_better_images.py.

Tim also provides links to scores of (well, ~12) other tools you might want to take a look at.

FIN

Drop a note if you've got your own analysis tool you'd like to share! ☮