Disaster Preparedness

Humanity Road Inc. uses TweetTracker to monitor emerging disasters, and Quicknets partnered with us to test their system in a disaster simulation game on the Arizona State University Campus.

Cutting-Edge Research

ARTIS Research and University of Michigan used data collected through TweetTracker to analyze the factors responsible for Arab Spring, and Carnegie Mellon University and the School of Social Transformation at ASU use TweetTracker for their research as well.

TweetTracker has analyzed over 130,000,000 tweets!

Step 1. Track

TweetTracker collects tweets according to hash tags, search terms, and location, allowing you to filter through billions of posts to Twitter in real-time. Select an event below to see a live feed of tweets on the map.

Step 2. Analyze

TweetTracker allows you to compare activity across different hash tags, search terms, and locations.

Step 3. Understand

TweetTracker provides visualizations across a variety of metrics that can bring light to important trends.

Demo: Tweet Maps

To the right, you can see a demonstration of one of TweetTracker's most powerful features: the ability to visualize tweets in real-time on a map that leverages geotagging and profile locations.

Record, Rewind, and Replay!

TweetTracker lets you record events, go back in time, and replay them as they occurred on Twitter. TweetTracker opens up new opportunities to understand the cause and development of different events in real-time.

How does it work?

TweetTracker collects the tweet ID, username, time, location, tweet text, hashtags, urls, other users mentioned, and replies for every tweet it tracks. The system also allows a variety of visualizations based on this information, including streaming geospatial maps, tag cloud summarizations, post-event investigations in pseudo real-time, automatic translation of Non-English tweets, and keyword trending and comparison.


Watch TweetTracker in action in the following video

Download a PDF Guide to TweetTracker here

TweetTracker is built at the Data Mining and Machine Learning Lab at Arizona State University.

Shamanth Kumar is a graduate student at the Data Mining and Machine Learning Lab.
Fred Morstatter is a graduate student at the Data Mining and Machine Learning Lab.
Justin Sampson is a graduate student at the Data Mining and Machine Learning Lab.
Dr. Huan Liu is the director of the Data Mining and Machine Learning Lab.

Contact Us: tweettracker@asu.edu | (480) 727-7349 | 699 S. Mill Ave. Suite 501, Tempe, AZ 85281


This project is funded by the Office of Naval Research.