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.

Detail description, screen shots and Demo page Link

Overview:

The story of the stars (working title) explores the mutual influence of pop music and news media. The intention of this interactive visualization is to find correlation between rise and fall and popularity of the musicians over time. This visualization will compare the musicians’ success or fail on billboard chart and social, personal events on news articles related to the musicians regardless of their reputation. ex, after Michael Jackson died, album sales goes up)

Data:

Billboard open API

To use names of the artists from the billboard chart

+

NYT article search API

To get frequency of the musician’s names from the article

+
sentimental analysis API(evri) + NYT community API

to see positive/ negative opinions from people’s comment

Precedent :

Twitter data viz from MTV VMA

Michael Jackson billboard chart timeline from NYT

idea 2

Overview
This project will visualize the change of ranking history  of bestselling books, and which publisher and author makes more/ less bestsellers, how long it takes for their books to become bestsellers after they are published( some books don’t get attention when they are published, but after certain events, suddenly they become top sellers. On the other hand, there are steady sellers. )

Data
NYT Best sellers api :
use ranking history, author, publisher, publish date

Precedents
360/365 Jer Thorp

idea 3

In the intrest of making data viz that can used in mobile platform, we thought about tracking hotspots in nyc ara. this idea is kind of broad as well, but might be worth exploring further. using nyt real estate api and other source like twitter( some place people casually talk about things ), how some area is more popular( sold well, get more attention) than others, which can be used as a tool to predict future rise in real estate market.

so with mobile phone, the visual will be changed based on the user’s location and the popularity of the spot.

2. Data
NY Times Article Search API, TimesTags API, http://icasualties.org/

3. Design Questions

The shape of a graph of casualties in Iraq and Afghanistan will obviously be one that increases over time – sharply at first, then more slowly as time passes. My question is how much and until what point the number of
articles regarding the wars in the two regions follows the same slope. I would probably track these numbers month by month. I would also like to compare Iraqi civilian casualties, but this will be much more difficult. It would also be interesting to see the first mentions of an insurgency, as well as terms relating to withdrawal from the conflict.

4. Prior Art / Precedents

Most of the data visualizations I’ve seen about the war in Iraq have  plotted the casualty count data against date, where the deaths took place, where the soldiers were from, and how their number compared to estimated Iraqi civilian deaths. I have not seen any that address the coverage of the conflict in the media or how it changed over time. I feel that this pattern makes the stories told by other data visualizations about the war that much more powerful and moving.

*Edit: Lie by Lie is an amazing interactive timeline of the events leading to the war in Iraq and asks who knew what when: our leaders, journalists, commentators, etc.

Our project, CongressTweets, looks at the relationship between your Congressmen’s political activity in Congress vs his/her activity on twitter. We are trying to find out if twitter activity is a good measure of political activity and good way for communicating with constituents. We were also interested in looking at voting trends relating to party, gender, and geography.

For this project we used the NYTimes Congress API as well as several Twitter related APIs.

Please find a zip of our code here

Opening Page

Missed Votes % vs Voted with Party % (Representatives)

Missed Votes % vs Voted with Party % (Senators)

Someone tweets a lot!

So does this guy!

Midterm Project by Rupa & Bobby.

This project started with a curiosity stemming from the standardized English Language Arts tests taken by all elementary school children in the NYCity public school system. When I was in 3rd grade, the highest percentile of test takers were told that they had a level of reading comprehension that enabled them to read the NYTimes. This experience led to questions:

1. how is reading level measured
2. are nytimes articles truly challenging
3. how does the NYTimes compare to other news publications with comparatively lower reputations
4. how might such a  comparison be visualized

Research indicated that many methods to calculate reading level exist and that most rely upon counting syllables (due to the complexity of polysyllabic words).  Our tests showed that the NYTimes is consistently more difficult to read than Newsday, USA Today, the Wall Street Journal, and the Washington Post.

Looking at stuff like this all day….

really makes me appreciate stuff like this.

By the late conspiracy theorist / artist Mark Lombardi. Check out this great piece about him on NPR here: The ‘Conspiracy’ Art of Mark Lombardi