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.
Mark Blumenthal, of Pollster.com, recently posted a set of text clouds showing the words used by each candidate in the Democratic presidential debate Thursday night. The clouds were generated from transcripts of the debate, using Daniel Steinbock’s Tag Crowd tool. Candidates’ Text Clouds
In the screenshot of Mark’s posting, it’s easy to see this is a great example of a collection of text clouds used for comparative visualization and interpretation. The goal is to enhance understanding of the meaning and content of the candidate’s overall conversations during the debate, an idea I explored briefly last year.
Just a month ago, in a post that identified text clouds as a new and distinct tag cloud variant, I suggested:
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
Supporting the comparison and interpretation of political speeches is an inventive, timely, and resourceful application that could make text clouds a regular part of the new personal and professional toolkit for effectively handling the torrents of information overwhelming people in important situations like vetting political candidates.
I especially like the way this use of text clouds helps neatly sidestep the disheartening ubiquity of the soundbite, by aggregating, distilling, and summarizing all the things the candidates said. I suspect few — if any — of the campaigns realize the potential for text clouds, but they definitely know the detrimental power of soundbites:
“It’s a mess,” said an exasperated-sounding Mr. Prince, Mr. Edwards’s deputy campaign manager. “Debates are important, but in these big multicandidate races they end up not being an exchange of ideas, but just an exchange of sound bites. They have become a distraction.“
The value of a collection of soundbites over an insightful dialog is — apologies for the pun — debatable. But even if a simple exchange of soundbites is what the new shortened formats of many debates yields us, text clouds may help derive some value and insight from the results. The combined deconstructive and reconstructive approach that text clouds employ should make it possible to balance the weight of single remarks of candidates by placing them in a larger and more useful context. History Repeats Itself
In the longer term view of the history of our responses to the problems of information overload, the appearance of text clouds may mark the emergence of a new general puprose tool for visualizing ever greater quantities of information to support some qualitatively beneficial end (like picking a good candidate for President, which we sorely need).
The underlying pattern — a consistent oscillation between managing effectively and ineffectively coping, depending on the balance between information quantity and tool quality — remains the same. Yet there is also value in knowing the cycles that shape our experience of handling the information crucial to making decisions, especially decisions as important as who leads the country.
The NY Times transcript of the debate is available here.
Thanks to all who made the 2007 IA Summit in Las Vegas this year both worthwhile and memorable, by organizing, presenting, volunteering, or attending. Thanks especially to everyone who participated in our panel Lessons From Failure: Or How IAs Learn to Stop Worrying and Love the Bombs, and brought with them a willingness to share, laugh, and think differently about a normally taboo subejct.
We limited our presentations to 10 minutes to encourage audience involvement, and reduce the talking-head with a microphone quotient typical of panel formats. This worked well, but meant setting aside quite a bit of material that’s worth bringing out: the abbreviated version of my talk on how states of mind affect failure is available directly from the conference site.
The full version of my slides on state of mind, self-definition, and parallels between individual and societal responses to failure is available from Slideshare here, and appears below.
The full version includes:
additional discussion of societies in crisis
a major figure in Buddhist philosophy
a personal tale of business venture gone wrong
leverage points potentially useful for averting failure