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.

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.

Our project explores the geographical locations that certain terms an associated with.  The intention for this interactive visualization is to delineate the progression of different location’s significance, as they relate to specific terms. (i.e. what country was talked about most regarding “9/11″ between 2001-2003).  We’re hoping that new narratives will arise from the proliferation of issues and news articles from the New York Times over the past  28 years.

2. Data

We’re still selecting our mapping API – but for now we’re intending to use:

1. NY Times Article Search API
2. Google Maps API
3. NY Times Tags API

3. Design Questions?

The main questions we’re asking thus far are:

• What does the geographical context of breaking issues in the news look like over a period of time?
• Are there patterns in the topics being discussed about specific locations over time?
• What is the best way to represent the significance of locations as they pertain to past issues in the news?
• What are the best ways to filter terms, should the results for each visualization be dynamically generated?

4. Precedents

Our first precedent is a project from Yale called “Geographcially based Economci Data (G-Econ)”.  This visualization shows economic fluctuations on a 3 diminutional geospatial model.  This project is an example of how we might be easily convey the geographical significance of certain terms over a period of time.

The second precedent that we chose is Jer Thorp’s visualization entitled “Is Twitter the New Internet?”. This piece explores the usage of specific terms over time to show the proliferation of internet based services over the past 2 decades.  We selected this piece because we’re also interested in term frequency within the New York Times article databases, but instead looking at the importance of locations within the context of specific issues.

5. Collaboration

We still haven’t worked out the specifics for this yet, but so far I will be working on most of the coding and Thai will be primarily handling the design for the project. These tasks will most likely overlap during certain areas of the project however.

http://parsons.justinblinder.com/datavis09/timeswire/justinblinder_timeswire.html

My visualization initially focused on the “terms” and “catagory tags” associated with each article.  After realizing that most of these terms were very specific, I instead chose to use the section that each article was a part of. I created circles for the articles and made variables for their size, color, and position, that corresponded to the section frequency and the title.  I hoping to give a basic idea of the section frequency within a certain period of time, however partially randomized positioning is still a bit chaotic.

My original design plan was to have a radial positioning for each section that slowly emanated  from the center. I used this process in my second visualization mockup, and showed the frequency of articles for each section within the last 24 hours.

This essay focuses on communication information with appropriate visualization methods. I found this resource to be extremely helpful.  Although there are often obvious choices of graphs that you’d use for certain types of data, its still becomes difficult to refine one that appropriately conveys the information. The examples seem trivial, but by tailoring basic elements to match what you intend to convey, the end results are greatly enhanced.

Tidwell

In this reading, Tidwell delineates the principles for effective visualizations for interactivity  and usability.  All of the concepts the author talks about have all point to a main rule: make it intuitive. Although this might seem obvious, Tidwell elaborates on unique areas that enhance how a user interacts with a visualization. This article immediately made me think back to our class discussion regarding Jonathan Harris’ piece “Whale Hunt”.  The user interface he created seemed to be lacking because it was designed for him, not for the user.  The emotion was almost obscured by the ambiguity of the controls a user is presented with. For me, this detracted from the intensity of the actual content, his photographic journey.

“The Database as System and Cultural Form” by Christine Paul
This essay touches upon the history of databases and the representation of their contents. The area of this essay that I found most interesting was that the previous methods of conveying information haven’t been lost, there is now just a more radical distinction between representation and information. As the author asserts, previous generations passed down information orally, which seems like a much more natural way of conveying it rather than displaying the same stories in text via a web browser.

Although the paint brushes and inscribed anecdotes that have re-imagined the spoken word  for millennia appears distant and antiquated, little has changed in the visual translation of the same story that resides as memory in a database through code. It seems only natural that in order to envision the exponentially growing wealth of data, we turn to iterative tools that can adaquetly process them. The data remains the same, only our methods of conveying it have altered. Creativity and expression exist in the translation of information, not the medium that they are conveyed through.

“A visual exploration of Twitter conversation threads in the days following the Iranian Elections of June 2009 “ By: Gilad Lotan

“Retweet Revolution” visualizes the 372 most popular tweets over the span of 10 days regarding the recent Iranian elections.  The interactive applet allows users to see a comparison of the relative tweet popularity, as well as the transformations and networks of retweets. The result is an activist narrative, facilitated by a simple yet empowering technology.

Also, I came across an interesting article which gives some further info on twitter visualizaitons during the recent Iranian elections.

http://www.readwriteweb.com/archives/evolution_revolution_visualizing_millions_iran_tweets.php