posted by Zeke Shore on Mar 1st, 2010
The last design iteration I wrote about a couple weeks ago started to take a departure from earlier iterations by exploring the idea of representing the personality of every comment on the New York Times website (that relates to any given topic) as it’s own entity, and visually describing it’s sentiment or personality.
After reflecting back on our original reasons for wanting to visualize online discussions, our thesis question really centers around how can the ‘Vox Populi‘ still be heard as reader participation in the journalistic process scales to hundreds of thousands of comments spread across hundreds of articles and blog posts for even just one news source.
So this resulted in a design prototype that involved rendering comments for a given topic as balls swarming around the article that seeded the conversation, representing sentiment with color, opacity, and speed of movement, describing each comment’s polarity (how positive or negative), strength (strong or weak) and activity (active or passive) respectively.
While this iteration was both readable and interesting to look at, it suffered in terms of scalability. We could realistically only look at a couple conversations at a time for any given topic. So the next phase of the design process involved trying pull some of the more successful aspects of this iteration into a more real estate friendly composition. The logical progression of this involved breaking conversation into a linear organization (all of the following mock ups are not visualizing real data, but rather serving as design explorations).
Of course horizontal flows of information are rarely web-friendly, despite it being a logical way to organize content chronologically. So this quickly evolved into a vertical orientation, and opened the door for exploring the concept of possibly showing when commenters reference each other within a conversation.
Read the rest of this entry »
posted by Zeke Shore on Mar 1st, 2010
Slide deck for our midterm presentation in Dave Carroll’s Thesis Studio.
posted by Zeke Shore on Feb 2nd, 2010
After evaluating some of the issues we observed in the first design iteration for the VoxPop visualization model, we were able to establish some more criteria as the design evolved.
One idea that I explored early on was the concept of these emotive forces “pulling” the conversation in various directions. While the metaphor seemed interesting, data quantity vs. real estate would be even more of an issue than with the first design iteration. Another big take away from the first prototype was the non-representational and homogenizing side effect of grouping words at a full conversation level, as one abnormally “colorful” comment could dramatically swing the visual representation of the entire conversation.
Additionally, breaking words into these six groups unnecessarily abstracts the way in which these semantic classifications are actually describing attitudes. Going back to Charles Osgood’s Semantic Differential theory, his studies revealed that the Evaluative scale (good to bad) is the primary axis by which study participants classified affective meaning, followed by Potency and Activity as the two other universal characteristics of affective classification. rather than thinking of these three axises as separate scales, when trying visualize emotional qualities of these comments it makes more sense to have these metrics somehow layering on top of each other so that in conjunction, they draw the full “personality” of each comment.
So the idea of representing each comment as its own entity developed. In order for these three emotive characteristics to be able to layer on top of each other, representing each of the six poles as a different color (as done in design iteration 1) would not work.
In this next design iteration, The Evaluative scale is the only metric represented across a color spectrum, ranging from blue (negative) to yellow (positive) where the number of positive words minus the number negative words dictates the comment’s place along the color spectrum. Comments with mostly positive words would be closer to pure yellow, and comments with mostly negative words would be closer to pure blue, with more neutral and balanced comments being various shades of green.
The Potency scale (strong to weak) could then be represented with opacity, where the number of “strong” words minus the number of “weak” words would dictate where the comment lives on the opacity spectrum between fully opaque and almost completely transparent.
Our last axis is Activity (active to passive). While the color-inspired evaluative scale requires a somewhat subjective color scheme choice which of course will involve cultural and personal influences, opacity actually serves as a conveniently clear metaphor for our ’strong to weak’ continuum. The ‘Activity’ axis presented a similarly convenient direct metaphor that could be exploited. The more active a comment is, it could actually be moving faster and farther, and likewise the more passive a comment is, the more stagnant it would be.
The final characteristics of this design iteration was that the scale of each comment could be determined by the number of affective words it has. This could arguably be thought of as how “loud” the comment was (although this is open to some debate, a really long comment with lots of weak and passive words isn’t really “loud”… more “verbose”).
So we created a new prototype exploring how this design iteration might look with real data. We started with just three articles that the comments would “swarm” around. To help keep the composition balanced and as easy to read as possible, we had comments on the ‘positive’ side of the evaluative scale swarm on top of the article’s title, and comments on the ‘negative’ side of the evaluative scale swarm bellow the comment title. (The blue and yellow evaluative colors were accidentally reversed in this version of the prototype, with blue at the ‘positive’ end and yellow at the ‘negative’ end).
Evaluation
This design iteration was beginning to show a lot of promise. With a quick glace a viewer might more successfully read the tone of the entire conversion surrounding the article, while still preserving the ‘personality’ of each individual comment. This was also starting to become more interesting to look at. Active comments would quickly nestle their way in towards the article title while passive comments float aimlessly at the perimeter.
A major flaw with this design iteration was that we were moving in wrong direction with maximizing out screen real estate, with only three articles being viewable at a time. While the readability of each article’s conversion had improved, we were even further from being able to see any sort of trends or evolution in discourse surrounding a topic over time.
Coming soon: Design Iteration 3.
posted by Zeke Shore on Jan 27th, 2010
Back in November we had the honor of presenting our progress to the R&D department at the New York Times, followed by a similar presentation to our upcoming thesis advisor, Dave Carroll for the final semester of the project. Feel free to check out the slide deck, however links to the working prototypes may be down (running servers is expensive!)
VoxPop Midpoint Presentation
posted by Zeke Shore on Jan 26th, 2010
While we have been good about posting research progress as it comes, progress on the design front has been a bit too quite. Here are some early iterations of the User Interface design process, and what we are learning as we go.
Working off of the data that we were beginning to generate, our starting point was a collection of user comments for all of the New York Times articles that would be returned for any given query. By parsing through the comments, we could match words against the General Inquirer Dictionary across Charles Osgood’s three-axis theory of Semantic Differentiation. You can read more about the first version of our emotive analysis process in my previous post on the subject.
So the initial output we decided to shoot for was essentially six lists of words from the comments for each article that is retrieved for a given query. Along the evaluative axis we would have a list of ‘positive’ words (shown above in green) and ‘negative’ words (in red), along the activity axis we would have a list of ‘active’ words (in orange) and passive words (in brown), and along the potency axis we would have a list of ’strong’ words (in blue) and ‘weak’ words (in gray).
Flushing out the design of this model included four “states.” Collapsing the emotive word lists for each article would yield colored bars extending above and bellow a base line of articles. Theoretically, this would reveal trends in the quantities of these emotively charged words over time for discussions surrounding any keyword. Clicking on a specific article would reveal the actual list of words that are being described by the colored bars of the collapsed view. Extending the idea, hovering over any word could potentially show the sentence from which that word was retrieved, and hovering over the article title could reveal the abstract of the article, and clicking either would bring the user through to the article or the specific comment on the New York Times website, all in an effort to provide easy contextual access as a validation tool.
So we built a prototype of this visualization. We did not build out all of the interaction levels spec’ed in the initial mockups, but even just getting a list of articles with their corresponding lists of emotively classified words from the discussions surrounding them seemed like a good starting point for exploring the data.
This prototype revealed a lot. The first obvious conclusion is that we are dealing with way more data than could be meaningfully expressed as ‘lists of words’. Even scaling the text size down bellow legibility did not allow most lists to be viewed in their entirety in a normal web browser window.
Another problem is that the data is really hard to read if you don’t already have a strong understanding of what was going on behind the scenes. This organization does not show the three clear axises that the discussions are being mapped against. Furthermore, this model gives equal weight to all of our emotive axises, despite Osgood’s conclusion that evaluative distinction (positive/negative) carries the most emotive weight, which is then supported by activity and potency as the second two most significant factors.
One more problem that this prototype revealed is the homogenizing effect that results from extracting lists of words at the level of the entire conversation rather than specific comments. One really long nasty comment could skew the negative word count for an entire conversation when looking at the data at this level of abstraction, and that sort of misrepresentation could be a serious cause for concern. The project is called VoxPop stemming from the Latin term Vox Populi, meaning “voice of the people.” This visualizing attempt was not yet showing the voices of any ‘people’… rather averaging out the ebbs and flows of entire conversations.
More to come on our newer design iterations soon.
