“Popcorn & Politics” — data viz of movies, their audience and their impact on the 2008 US Presidential Race

Personal Blog Writeup

Through visualizing the relationships between a New York Times article’s contextual location, and location of the Twitter users that tweet about the article, a global interest emerges in many stories that pertain to a specific part of the world. I’m fascinated by maps, specifically how boundaries form over time and are consistently being remapped based on regional issues.  Since Twitter has become a prolific tool for disseminating information, yet is so successful because of it’s ephemeral and mobile nature, I wanted to explore how where individual interests lie across the world about articles in the New York Times.

This project involved extensive backend and frontend programming.  The actual data consists of geocoded Tweets and New York Times articles that are stored in a database. Every 10 minutes, a PHP script is run on my server that queries the Backtweets API for any tweets that have occurred regarding specific New York Times articles. For each of the returned tweets, the twitter user’s location is retrieved, and if a valid value is found (there is no standardized system for Twitter user’s location information, Br00k1nz is entirely valid) then the value is geocoded, as well as the New York Time’s article’s geo faceted value using the Google Maps API.  All the information is sanitized, and then inserted in a database.

The front end of the project is written in Java (using the processing.core library). Queries to the database are made based on an article that a user wishes to visualize. The latitude, longitude, and text information is then parsed and mapped in the applet. I decided to create the visualizing using openGL, and create parabolas that showed the  relationships between articles and tweets. A timer is set and displayed at the beginning of each scene, which triggers an connection path each time the timer matches a tweet’s time.

When looking at precedents, I was particularly inspired by both Aaron Koblin’s “Flight Patterns” and Jer Thorp’s “Just Landed” visualizations. Koblin’s visualization elegantly maps air traffic patterns. Some of the images in the series show incredibly intricate networks that are formed by air traffic routes, as well as the airports in a region. Jer Thorp’s processing based visualization shows the locations of twitter users and the places that they fly to, cleverly scraped based on the two tweeted words “just landed”. One of the most compelling aspects of this piece is the 3d translation of data, allowing for an exploration into the intricacies of the paths.

When I first started this project, I set out with a few questions informed my process and methodology. These were:

• What does the distribution of Twitter user’s interests about various topics, locations, and sections from the New York Times look like visually?
• Do current issues in the news effect where Twitter users decide to tweet about?
• Do patterns emerge based on country/region, or are the Tweet/Articles relationships random?
• Are there unseen political/economic/social relationships between countries/regions that are hidden in the data?

After analyzing a few stories, there were a few surprising results.  For instance, an article about Houston electing a gay mayor received mostly tweets from people in the United States. However, another article about the Northwest Airlines terrorist attempt in Detroit attracted tweets from all around the world, the very first actually came from Europe.

Overall, I feel the project was a success. Although it may be hard to discern extensive information as to why person in one location may tweet about another, within a large set of data, it’s quite amazing to see how twitter has been globally espoused, and how diverse many of the tweets are in terms of location.

In journalism, we recognize a kind of hierarchy of fame among the famous. We measure it in two ways: by the length of an obituary and by how far in advance it is prepared.

-Walter Cronkite

The chart:

The grey bars represent the ten longest obituaries of the last 7 months. From left to right, they are:

“A Star Idolized and Haunted, Michael Jackson Dies at 50″ (2839 words)
“Jack Nelson, an Investigative Reporter, Dies at 80″ (1266 words)
“Dominick Dunne, Writer Who Chronicled High-Profile Crime, Is Dead at 83″ (1966 words)
“Robert Rines, Inventor and Monster Hunter, Dies at 87″ (2839 words)
“Howard Unruh, 88, Dies; Killed 13 of His Neighbors in Camden in 1949″ (1304 words)
“Roy DeCarava, Harlem Insider Who Photographed Ordinary Life, Dies at 89″ (1485 words)
“Walter Cronkite, 92, Dies; Trusted Voice of TV News” (2968 words)
“Budd Schulberg, ‘On the Waterfront’ Writer, Dies at 95″ (1855 words)
“Henrich, Yankees Clutch Hitter, Dies at 96″ (1086 words)
“Bela Kiraly Dies at 97; Led Revolt In Hungary” (1136 words)

Notable outliers are:

Michael Jackson, receiving a 2839 word obituary at the age of 50 (Billy Mays is the other 50 year old). Jack Rose, the youngest recipient of a NYTimes obituary. Along with Michael Jackson, Walter Cronkite received a very long obituary that ran 50% longer than the two next longest obituaries.

Nick Hardeman + Bruce Drummond.

Presentation

Demo

Detail description, screen shots and Demo page Link

Clearly they must have seen the brilliant work Steve and I are doing and decided to jump on board. Check out the link to see it at work, pretty simple but effective visualization they’ve got going on.

[ Engadget ]

My final project will explore the relationship between the geographical location of Twitter users and the New York Times articles they tweet about. I’m interested in seeing (geographically) where the interest of Twitter users lie on a daily, monthly, and (possibly) yearly basis. I also plan to implement filters, allowing users to explore where New York Times article topics are being talked about most, and the distribution of tweets about New York Times articles by section.

2. Data

• NYTimes Articles API
• BackTweets API
• Twitter API
• Google Maps API

3. Design Questions

• What does the distribution of Twitter user’s interests about various topics, locations, and sections from the New York Times look like visually?
• Do current issues in the news effect where Twitter users decide to tweet about?
• Do patterns emerge based on country/region, or are the Tweet/Articles relationships random?
• Are there unseen political/economic/social relationships between countries/regions that are hidden in the data?

4. Prior Art / Precedents

Flight Patterns by Aaron Koblin

This visualization elegantly maps air traffic patterns. Some of the images in this series show incredibly intricate networks that are formed by air traffic, as well as the locations of the largest airports.

Just Landed by Jer Thorp

Jer Thorp’s processing based visualization shows the locations of twitter users and the places that they fly to, cleverly scraped based on the two tweeted words “just landed”. One of the most compelling aspects of this piece is the 3d translation of data, allowing for an exploration into the intricacies of the paths.