archive.Joe Lamantia.com

A meme is emerging for the use text clouds as visualization for – and a source of insight into – political speeches and speakers.
Text clouds of the Republican Presidential candidates’ debate appear front and center on the DNC blog democrats.org, in Tag Clouds Can Tell Us a Lot…. (sourced from media analysis firm Upstream Analysis via Pollster.com).


As you can see in the quote from the writeup below, we’re quickly developing sophisticated readings of the (comparatively) simple visualization methods used to generate text clouds.

But sometimes a cloud also reflects concerns that voters share about a candidate. This is because the candidate gets asked about the issue–a lot–and then has to talk about it.

Check out the large “Pro-Life” tag in flip-flopping Romney’s cloud, or the large “Think” tag in Giuliani’s cloud–the candidate notorious for leaping first and thinking later.

Political interpretations aside, this is a nuanced reading of the resulting clouds: it recognizes the dynamic feedback link between intentions and responses that becomes visible in the rendered clouds. For a visualization geek, these clouds show the differing agendas of candidates and audience as they played out, a nice example of social mechanisms in action.

Note to the tool builders of the world

How about putting together a visualization toolset that shows evolving text clouds as the debate progresses? I’m imagining a timeline plus transcript plus cloud view of the accumulating text cloud for each candidate, with options for moving forward or back in the stream of words.

What could be better than watching words and ideas bloom over time, the same way we see flowers in a garden blossom, open, and close in time lapse photography. I’d like to think we can grow something poetic and beautiful, as well as useful, from the (sadly debased) soil of politicized sound bites surrounding us.

Or, with a nod to the brutal competition built into most natural systems, you may choose to watch the struggle of waterlillies for sunlight, in this clip from The Amazing Life of Plants.

Thanks to Datamining, for posting a writeup and screenshot of a prototype of Community Buzz, which features a text cloud. Community Buzz is a Microsoft Research project, and this is a perfect use of a text cloud to visualize concepts and further comprehension in a body of text.

More interesting than the text cloud is the space in the screenshot that looks like a placeholder for advertising driven by the contents of the text cloud. The annotation reads “Contextual ads based on the Buzz cloud keywords”, implying an advertising based revenue mechanism driven by creation and analysis of a text cloud.

Community Buzz Screenshot

The description of Community Buzz posted on the TechFest 2007 page, includes the following, making the connection to an advertising model explicit:

Community Buzz combines text mining, social accounting (Netscan/MSR-Halo), and new visualization techniques to study and present the content of communication threads in online discussion groups. The merging of these research technologies results in a system that gives great value to community participants, enables highly directed advertising, and supplies rich metrics to product managers.

Assuming it’s possible to provide highly directed advertising and rich metrics based on text clouds, I can see the benefits of for advertisers and product managers, and researchers of many kinds. Yet I’m not convinced of the benefits for community participants. Where will the text clouds come from, and how will their content reflect the needs of the community? How will social dynamics shape or affect these text clouds, to make it possible for them to leverage network effects, differential participation, and the scale benefits of connected social systems?

Text clouds – at least at this stage of development – support rapid but shallow comprehension: maybe this is perfect for advertising purposes…

Like a pile of dry bones that used to make up a skeleton, text clouds lack the specific structure and context of their source, and so cannot replace comprehension. Text clouds deconstruct the word elements that make up a body of text the same way spectrum analysis identifies the different wavelengths of light from a distant star. It’s a bit like using statistical analysis to read King Lear, instead of using a variety of tools to learn more about what Lear might have to say.

A better use of text clouds, or any other type of deconstructive method (a variant of semiotics) is as a tool for enhancing comprehension. Text clouds seem to bypass distinctions between high context and low context that present barriers to understanding deep context, by focusing on the raw content of the source, on the level of it’s constituent elements.

The goal of examining the fundamental or essential makeup of something we’re exploring – as a way of better understanding that thing overall – is an epistemological method pursued by Plato and a host of other Western philosophers and natural scientists. We should be cautious with new tools, however, as the urge to illuminate and dissect the fundamental makeup of that which is complex and nuanced can go too far, crossing from the insightful to the sterile domain of soulless reductivism. Witness the responses of corrupt officials to Javier Bardem’s character Agustín, in John Malkovich’s directorial debut The Dancer Upstairs.
Agustín is a police hero who saves his country from a criminal and oppressive government, social disintegration, and guerilla takeover. He then surrenders all prospects of winning the presidency and leading his struggling nation to prosperity for the unrequited love of a woman who aided the same guerilla leader he helped capture. Agustín strikes a secret bargain to secure her freedom with the corrupt powers that be, on condition that he withdraw from public life. His choice is incomprehensible to the soulless officials in power. To these people, who buy, sell, and execute hundreds without a thought, Agustín’s lover “…is just a girl – 70% water.”

For reference, the overview of Community Buzz:

• Community Buzz combines analysis of the content of online discussions and social structure of the communities to identify hot topics and visualize how they evolve over time.
• Through search and Buzz cloud users can access relevant discussion threads and adverts linked to the search results and Buzz keywords.
• Visualization of keyword trends enables the users to monitor the popularity of selected topics. Mesasages can be filtered based on the ‘social status’ of the author in the community.

And the complete description of the demo mentioned by Datamining:

Community Buzz is a new window into online communities! Interesting and useful conversations, authors, and groups are discovered easily using this tool, jointly developed by Microsoft Research Redmond’s Community Technologies group and Microsoft Research Cambridge’s Integrated Systems team, with sponsorship from Live Labs. Community Buzz combines text mining, social accounting (Netscan/MSR-Halo), and new visualization techniques to study and present the content of communication threads in online discussion groups. The merging of these research technologies results in a system that gives great value to community participants, enables highly directed advertising, and supplies rich metrics to product managers.

During 2006, tag clouds moved beyond their well-known role as navigation mechanisms and indicators of activity within social media experiences, emerging as a standard visualization technique for texts and textual data in general.

This use of tag clouds does not commonly involve tags, social networks, emergent architectures, folksonomies, or metadata.
“Text cloud” might be a more accurate label for these visualizations than tag cloud. In addition to recognizing fundamental differences – text clouds differ from tag clouds in composition (no tags at all) and purpose (predominantly comprehension, rather than access or navigation) – distinguishing the two types of clouds will make it much easier to assess their abilities to support user experience needs and business goals.

The emergence of this new form of text cloud looks like a good example of speciation in action (though it’s too early to tell whether the end result will be cladogenesis or anagenesis).

Major and minor publications feature(d) text clouds as visualizations in 2006, both permanently and temporarily:

• The New York Times cloud of most searched items
• The Economist topics cloud
• The Vancouver Sun feature on real estate prices by neighborhood
• discussion on Read/Write Web
• even a book in progress

The Economist’s Text cloud

In 2006, several free and public tools for generating text clouds locally on the desktop or via a service available through the Web were released.  The increase in the number and variety of specific text cloud tools reflects embrace and enthusiasm for text clouds in communities of interest for information visualization, language processing, and semantics.

Some of the better known examples of text cloud tools include:

• Chirag Mehta’s tool Tagline
• Daniel Steinbock’s tool Tagcrowd
• Content Statistics by Ryan Grimm and Andrew Bruno of O’Reilly Research
• RhNav/Text Mining
• TextTagCloud
• IBM’s data visualization service – Many Eyes which I saw demo’d at IDEA 2006

The Many Eyes Cloud

The text clouds created with these tools range across a wide spectrum of speeches and writing:

• U.S. presidential speeches as tag clouds
• The Seattle Post Intelligencer’s analysis of speeches by Bill Gates, Steve Jobs, Michael Dell, and others, or their retrospective analysis of 30 years of Microsoft communications
• a Ph.D Thesis

Text clouds are meant to facilitate rapid understanding and comprehension of a body of words, links, phrases, etc. Any block of information composed of text is open to analysis as a text cloud, as these screen captures of text clouds for restaurant menus, ingredients, wikipedia, magazine covers, and even poems demonstrate.
Tim O’Reilly uses text clouds for a number of purposes:

We used them a bunch to analyze the topics, companies and people at the last FOO Camp, and they were the most useful of the visualizations we did. They helped us see where we were under- and over-represented in terms of companies and particular technologies we were wanting to explore. …So they have many uses beyond just showing what we normally think of as tags.

Non-linear Access

The emergence of text clouds shows continuing exploration and refinement of cloud style displays as a new form of user interface, adapted to specific contexts. Continued refinement of text clouds in this direction may indicate an expanding role for commonly available and sophisticated text visualization tools to support specialized goals for information display and understanding.

Remember that Google is busy right now scanning thousands of books per day from several of the world’s major academic libraries, as part of it’s self-appointed labor of organizing the world’s information. That’s a lot of new text. How will people work with effectively with such an overwhelming amount of text, of so many different kinds, from so many different sources?

Consider the following, from Ulysses’ Without Guilt by Stacy Schiff (in the New York Times):
Recently Cathleen Black, president of Hearst Magazines, urged a group of publishing executives to think of their audience as consumers rather than readers. She’s onto something: arguably the very definition of reading has changed. So Google asserts in defending its right to scan copyrighted materials. The process of digitizing books transforms them, the company contends, into something else; our engagement with a text is different when we call it up online. We are no longer reading. We’re searching – a function that conveniently did not exist when the concept of copyright was established.

On a larger scale, the growing use of text clouds hints at a (potential) deeper cultural shift in the way we go about reading and comprehension: a shift from linear modes based on reading words and sentences, to nonlinear modes based on viewing summaries of content in aggregate as a way of discovering concepts and patterns. (Finally, a legitimate use for Twitter…) Experimenting with text clouds for non-linear reading and comprehension (now that’s a sexy term…) is a natural evolution of the role cloud style displays play as an alternative / compliment / supplement to the list based navigation now dominant in user experiences.

A Text Cloud of Twitter Posts (A TwitterCloud?)

ago applied assuming bad briela classes clean coke decompressing dhowell dinner drinkin eisenbear full ga god guy half happy house ibterri impressing issues jedi joanna knows less lhalff lost minute moment nybble ohhhhh rfk rum ryanjames scholarship seconds sites skiing status summer twitterrific txt tyguy umpteenth vlu77 watched water web created at TagCrowd.com

I’m not predicting the end of reading as we know it, nor the end of navigation as we know it: both will be with us for a long, long time. But I do believe that text clouds might constitute an emerging method for augmenting comprehension and display of text, with broad potential uses.

Enterprising Clouds

What about someone lacking time to fully read a Shakespeare play, or a faddish business book, but who needs to understand something about that book’s meaning and substance? A text cloud creation tool could extract the most commonly mentioned terms, and otherwise profile the words that make up the text. It would be risky to rely on a shallow text cloud (and Tim O’Reilly mentions this specifically) for deep comprehension, but it would be enough to understand the concepts that appear, and allow polite conversation at a networking event, or lunch with that certain manager who recommended the book.

If I were entrepreneurial, I’d source a set of free electronic versions of classic texts, process them with one of the free text cloud tools, apply some XSLT and other transformations to generate consistent readable formatting, and sell the results as a line of ebooks called “Cloud Notes”. Of course, someone’s beaten me to it already…

What’s in store for the future?

In this fashion, text clouds may become a generally applied tool for managing growing information overload by using automated synthesis and summarization. In the information saturated future (or the information saturated present), text clouds are the common executive summary on steroids and acid simultaneously; assembled with muscular syntactical and semantic processing, and fed to reading-fatigued post-literates as swirling blobs of giant words in wild colors, it consists of signifiers for reified concepts that tweak the eye-brain-language conduit directly.