More, June 24^th!

Tagxedo turns words — famous speeches, news articles, slogans and themes, even your love letters — into a visually stunning word cloud, words individually sized appropriately to highlight the frequencies of occurrence within the body of text.

Yeah, visually stunning. Who wouldn’t want to turn an Obama speech in a banana shaped word cloud?

The Joe and Rika Mansueto Library’s ASRS will shelve materials underground by size rather than library classification, in racks 50 feet high, with a capacity to hold 3.5 million volumes in one-seventh of the space of conventional shelves.

Just take a look at the pictures to be amazed about this project.

Read more about the project here.

The content level features a lot of information of which a lot is irrelevant or too detailed to be put into a summary. To choose for the right information to display, I will be approaching the content level from a user experience direction. What does the user want to know? A quick query among readers lead me to generalize the most important cornerstones in three categories: context, critics and identity.

Context
The context is about the books contents and its immediate surrounding (in the public information bubble). It should feature information about related/similar books, the key subject(s) of the book and (last but not least) the salient subject(s) of the book. Salient subjects define why this book is special within its context (added value).

Critics
Apart from the fact that future-readers want to know what the book is about, they’d also like to know what other people think of it. Critical acclaim is an important factor. But not only professional opinions are used to base a decision on (hence the popularity of reviews at amazon.com & bol.com). News, reviews and time intertwine within this block.

Identity
The two previous categories are still lacking one thing, the connection with the user, the reader. Users want to read reviews that fit your mindset, they’d like to see books within their interest field (and not just a book someone unknown has read too). Adding specific user information to the model helps users to relate with the book (and hopefully, makes the eventual buying decision a little tat easier). In short, the identity category bridges the context and critics of the book to the users personal liking.

The three essential blocks described above are in this explanation related to a book level perspective. When descending one story, entering the chapter level, I noticed the three blocks still perpetuate. A chapter shows similar features like a book. It is likely to be more focussed concerning content, but the content itself is too large (still) to neglect generalization. That is why I clustered both layers in the content level. My approach to the content of the book and chapter layers will therefore be similar (context, critics and identity).

These layers all have their own specification, but some are also very similar. To generalize these clusters of layers I defined four levels:

• Constructor level
  The constructor level consists out of characters and syllables. These are the blocks that are used to create words and phrases. Concerning interpretation (literally), they do not have that much to tell a reader, but they are very important for automatic analysis of a text.
• Emotion level
  Within the emotion level, things start to get meaning. A word or phrase can have a semantic value. Some words change their semantic orientation based on its preceding or succeeding words. Still, this level is more important for automatic analysis than it is to the reader.
• Quote level
  For a reader, this is the most interesting level. The quote level is the most detailed level. This is where phrases come together in sentences and paragraphs which in turn, can be interpreted by its reader. It is called the quote level since often this is the level readers refer to if their explaining their thought on the text.
• Content level
  The content level is the most general level as it combines all paragraphs into a coherent picture. It uses several steps of interpretation (or for a computer, aggregation) to come to this generalized image of text.

The user interest barrier is a devision I based upon the deepest level readers are interested in. In other words, this is the level where stuff starts to make sense to a reader.

The creating of these levels help me to define a scope within the vast amount of information within a book. Within every level, I’m experimenting with several visual outputs that represent the inhabited amount of information. These experiments are the building blocks for the eventual visual language that will occupy information from all the defined levels within a book.

These levels, on their turn, can be devised into several groups that define what this level should represent (from a user experience perspective). More on this, later (enough theory for now, at least today that is).

David B. Sparks thought similar about this. He used people’s Twitter behavior to estimate the political ideology of Senators, Challengers, Media and others.

No details on how exactly he estimated this ideology based on Twitter behavior but he’ll be posting a paper about this soon. In the meantime, I advice you to take a look at his results and resolve what you think about this approach. (To me, thinks look promisingly good).

In the meantime we’re reading Think Quarterly. Google regularly updates their partners with what there up to lately. On this occasion they published a book/magazine on the subject of data.

Our first issue is dedicated to Data – amongst a morass of information, how can you find the magic metrics that will help transform your business? We hope that you find inspiration, insights, and more, in Think Quarterly.

You can read the book/magazine here (thinkquarterly.co.uk).

Figure 1. Kung Fu Panda

Figure 2. A Single Man

Figure 3. Slumdog Millionaire

Within my own model I want to try to use similar ways to use statistical book data (word count etc.) to define the main shape of the visualization. The eventual fulfillment of this main shape will be done with semantic data, context data and user data. I’ll be describing this model in the forthcoming days.

First, let’s enjoy the nice collection of movie barcodes.

Meet Nelson, Coupland, and Alice — the faces of tomorrow’s book. Watch global design and innovation consultancy IDEO’s vision for the future of the book. What new experiences might be created by linking diverse discussions, what additional value could be created by connected readers to one another, and what innovative ways we might use to tell our favorite stories and build community around books?

Especially the Nelson concept is very interesting. This is however fully depended on user input data, which in my future vision, will be enriched with the auto generated semantic data of the book itself.

Editorial note: I changed the vertical scrolling lay-out. Yes, I surrender.