Automated accounts can turn your Twitter timeline into a live data feed for discovery.
Following the right automated accounts can turn your Twitter timeline into a live data feed for discovery. Below are six rigorously built bots, how they work under the hood, and the simple steps to start drawing insight from them.
1 EQBOT / LA QuakeBot – instant seismic alerts
Step 1 Follow the handle
Head to @earthquakesLA or any of EQBOT’s regional spin‑offs – they tweet the moment the US Geological Survey publishes a new event. eqbot.com
Step 2 Understand the engine
A cron job polls the USGS GeoJSON feed every ~60 seconds, parses latitude, longitude, depth and magnitude, then formats a status via the Tweepy Python library. Magnitude thresholds are configurable per region, so @bigQuakesLA only posts M 3.5+. eqbot.com
Step 3 Why it feels serendipitous
Because the latency from sensor report to tweet is typically under two minutes, you often see the notification before mainstream news picks it up. Power users pipe the tweets into IFTTT or Home Assistant to trigger smart‑home safety routines.
2 ISSLocatorBot – where the Station is right now
Step 1 Follow @ISSLocatorBot
The bot posts a map snapshot and the current lat/long of the International Space Station every five minutes. Twitter
Step 2 How the math works
It queries the open‐source Where The ISS At? REST API, converts TLE orbital elements into ground traces, then renders a static PNG with Basemap in Python before pushing it to Twitter. A simple modulo schedule ensures exactly 288 updates per day. Twitter
Step 3 Use‑case sparks
Amateur radio operators time overhead passes; night‑sky photographers get heads‑up windows for DSLR long exposures; classrooms plot positions on wall maps for real‑time geography lessons.
3 Newfound Planets (@I_Find_Planets) – exoplanet thought experiments
Step 1 Hit follow and say “please”
Tweeting “please” at the bot yields a procedurally generated planet with orbital period, stellar type and a whimsical surface description. Developed by astrophysicist Colin B. Quist, the project turns raw exoplanet statistics into human‑scale stories. X
Step 2 Data sources and logic
The script taps NASA’s Exoplanet Archive API nightly, stores confirmed‑planet parameters in SQLite, then runs a Markov‑style text generator seeded with planetary science keywords to craft short, plain‑English descriptions.
Step 3 Why it delights
The randomness means you never know if you’ll get a sizzling lava giant or a temperate super‑Earth. It’s an elegant gateway drug into astrophysics and database querying. A 2015 Popular Mechanics feature captured the charm early on, and the codebase has since been upgraded for Twitter v2 API compliance. Popular Mechanics
4 GensoKanji Bot – the periodic table in Chinese characters
Step 1 Follow @gensokanji_bot
Every few hours the bot tweets one chemical element rendered in traditional Chinese script, with atomic number and reading. GitHub
Step 2 Behind the curtain
Built by Japanese developer @utf8sjis, the bot holds a CSV of IUPAC element data plus the matching Hanzi names. A simple Python random‑choice function selects a row, then Tweepy posts the formatted tweet.
Step 3 Learning angle
Chemistry teachers use the feed to discuss etymology and periodic trends; language learners get steady, low‑stress Kanji exposure; trivia fans pick up atomic masses passively.
5 Preprint Bot (@PreprintBot) – hottest BioRxiv and MedRxiv papers
Step 1 Follow the bot
It surfaces life‑science manuscripts whose Altmetric Attention Scores land in the top decile for the week. GitHub
Step 2 Pipeline specifics
A GitHub Actions workflow triggers hourly, scrapes RSS feeds from the two repos, queries the Altmetric API, filters by score, and tweets title, DOI and subject tags. Everything is open‑source Node.js; secrets are stored as encrypted repo variables.
Step 3 Why scientists swear by it
It trims hundreds of daily uploads down to a handful of high‑impact studies, perfect for journal‑club fodder or grant‑writing inspiration.
6 ArxivGPT – one‑sentence AI‑paper digests
Step 1 Follow @arxivgpt (or similar forks)
The account posts a daily thread summarizing trending arXiv machine‑learning papers in plain English. Medium
Step 2 How it runs
A scheduled Python script fetches the arXiv API for the cs.LG and stat.ML categories, ranks by recent citation velocity, then calls the GPT‑4‑Turbo model for a 30‑word summary. Rate limits are kept below Twitter’s 1 500‑tweet/month cap by batching papers into threaded replies.
Step 3 Practical payoff
Staying current in AI moves from hours of PDF skimming to a one‑minute scroll. Researchers can click through to the original PDF, while non‑specialists still grasp the gist.
How to make the most of science bots
1 Create a private Twitter List named Science Bots and add all six handles; this prevents timeline noise.
2 Enable mobile notifications for the real‑time feeds (EQBOT, ISSLocatorBot) but leave digests (Preprint Bot, ArxivGPT) as list‑only reads.
3 Archive standout tweets to a read‑later tool such as Matter; every tweet is a permalinked data point you can cite.
4 If you run your own classroom or newsletter, embed selected tweets via Twitter’s oEmbed script—no extra API work required.
5 For developers: clone any of the open‑source repos above and swap in your data source of choice—earth‑observing satellites, environmental sensors, even citizen‑science CSVs.
With a few carefully chosen follows, Twitter turns from doom‑scroll to discovery engine—one automated tweet at a time.
