Tag: tagclouds

New Books: 'Tagging' and 'Mental Models'

March 12th, 2008 — 11:00am

If you’re inter­ested in tag­ging and social meta­data, social book­mark­ing, or infor­ma­tion man­age­ment, be sure to check out Gene Smith’s Tag­ging: People-Powered Meta­data for the Social Web recently pub­lished by from New Rid­ers. I reviewed some of the early drafts of the book, and it’s come together very nicely.
Tag­ging takes a very prac­ti­cal approach, and pro­vides an ample set of exam­ples in sup­port of the insight­ful analy­sis. After an overview of tag­ging and its value, the book addresses tag­ging sys­tem design, tags in rela­tion to tra­di­tional meta­data and clas­si­fi­ca­tion sys­tems, and cov­ers the user expe­ri­ence of cre­at­ing and nav­i­gat­ing tag clouds.
Gene likes to build things, so Tag­ging includes a chap­ter on tech­ni­cal design com­plete with sug­gested tools and tuto­ri­als for cre­at­ing your own tag­ging apps.
All in all, Tag­ging is a wor­thy intro­duc­tion to the sub­ject, and a guide for deeper explo­ration.
While we’re talk­ing books, kudos to Rosen­feld Media on the pub­li­ca­tion of their first book, Men­tal Mod­els; Align­ing Design Strat­egy with Human Behav­ior, by the very tal­ented Indi Young!
Men­tal Mod­els is richly illus­trated, filled with exam­ples, lucid, and accom­pa­nied by a con­sid­er­able amount of addi­tional con­tent from the Rosen­feld Media web­site.
Indi has con­sid­er­able expe­ri­ence teach­ing oth­ers the tech­niques and meth­ods behind cre­at­ing insight­ful men­tal mod­els for audi­ences and cus­tomers. Cog­ni­tive / frame­worky meth­ods can feel a bit heady at times (espe­cially how-to’s on those meth­ods), but Men­tal Mod­els is straight­for­ward read­ing through­out, and an emi­nently prac­ti­cal guide to using this impor­tant tool for user expe­ri­ence design and strat­egy.
Men­tal Mod­els is avail­able elec­tron­i­cally as a .pdf for indi­vid­ual and group licenses, or in hard copy; it’s choose your own medium in action.

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Joining the Tag Team At Tagsonomy.com

July 22nd, 2007 — 3:11pm

I’ll be writ­ing about tag­ging, tag clouds, folk­sonomies, and related top­ics over at Tagsonomy.com going for­ward. As Chris­t­ian Crum­lish observed, it’s been quite at Tagsonomy.com for a while, but that doesn’t mean that tag­ging is any­where close to being fully fig­ured out.
To help kick­start the con­ver­sa­tion, I’ve put up two posts since offi­cially join­ing the Tag Team; The Tag­ging Hype Cycle, and Is Tag­ging a Dis­rup­tive Inno­va­tion?.
Com­ments are already flow­ing in — be sure to join the discussion.

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Watching Ideas Bloom: Text Clouds of the Republican Debate At Democrats.org

May 4th, 2007 — 8:07pm

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.

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Text Clouds and Advertising: Microsoft's Community Buzz Project

April 28th, 2007 — 2:47pm

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.

Comment » | Tag Clouds

Text Clouds: A New Form of Tag Cloud?

March 15th, 2007 — 12:04am

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 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:

The Many Eyes Cloud

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

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?)

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.

12 comments » | Tag Clouds

2.3% Of Chinese Internet Users Tag, Baidu Reports

March 4th, 2007 — 7:54pm

A post­ing from China Web2.0 Review shared results of a report on Chi­nese tag­ging rates released by Baidu, China’s lead­ing search engine.
I was not able to locate a trans­la­tion of the orig­i­nal report from Baidu, so I’ll quote the sum­mary from China Web2.0 Review:

Accord­ing to the report, only 2.3% of inter­net users have ever used tag, they mainly use tags in social book­mark­ing and blogs. I don’t know the meth­ods of data col­lec­tion, but the report said about 15 mil­lion Chi­nese web­pages were book­marked by users, on aver­age each user has saved 40 online book­marks. Among them, over 90% users add less than two tags for a book­mark.
And based on the tags of user saved book­marks, the most used tags are “soft­ware down­load”, “BBS”, “enter­tain­ment”, “game” and “learn­ing”.

We don’t know which ser­vices are included for analy­sis in the report, so I have no idea to which extent I can trust it. But based on my obser­va­tion, I agree with the basic find­ing of the report, even though more and more ser­vices have embod­ied tag­ging fea­ture, only a very small part of early-adopters in China indeed use it.
Two things come to mind right away:

  1. The matu­rity, struc­ture, and usage pat­terns of the Inter­net in China are not directly com­pa­ra­ble to the matu­rity matu­rity, struc­ture, and usage pat­terns of the Inter­net else­where (largely due to sub­stan­tial restric­tions and cen­sor­ship by the Chi­nese government)
  2. Offi­cial Chinse posi­tions are not fully reli­able, and so the num­bers, con­text, and usage described could be very dif­fer­ent from real practices

Still, even with the absence of solid qual­i­fy­ing, cor­rob­o­rat­ing, or con­tex­tual infor­ma­tion, this rate of adop­tion for tag­ging seems con­sis­tent with the rest of the very rapid pace of mod­ern­iza­tion in China.
And as the First Prin­ci­ple of Tag Clouds — “Where there’s tags, there’s a tag cloud” — says, this means there are quite a few tag clouds on the way in China.

2 comments » | Tag Clouds

10 Best Practices For Displaying Tag Clouds

February 25th, 2007 — 1:31am

This is a short list of best prac­tices for ren­der­ing and dis­play­ing tag clouds that I orig­i­nally cir­cu­lated on the IXDG mail­ing list, and am now post­ing in response to sev­eral requests. These best prac­tices are not in order of pri­or­ity — they’re sim­ple enumerated.

  1. Use a sin­gle color for the tags in the ren­dered cloud: this will allow vis­i­tors to iden­tify finer dis­tinc­tions in the size dif­fer­ences. Employ more than one color with dis­cre­tion. If using more than one color, offer the capa­bil­ity to switch between sin­gle color and mul­ti­ple color views of the cloud.
  2. Use a sin­gle sans serif font fam­ily: this will improve the over­all read­abil­ity of the ren­dered cloud.
  3. If accu­rate com­par­i­son of rel­a­tive weight (see­ing the size dif­fer­ences amongst tags) is more impor­tant than over­all read­abil­ity, use a mono­space font.
  4. If com­pre­hen­sion of tags and under­stand­ing the mean­ing is more impor­tant, use a vari­ably spaced font that is easy to read.
  5. Use con­sis­tent and pro­por­tional spac­ing to sep­a­rate the tags in the ren­dered tag cloud. Pro­por­tional means that the spac­ing between tags varies based on their size; typ­i­cally more space is used for larger sizes. Con­sis­tent means that for each tag of a cer­tain size, the spac­ing remains the same. In html, spac­ing is often deter­mined by set­ting style para­me­ters like padding or mar­gins for the indi­vid­ual tags.
  6. Avoid sep­a­ra­tor char­ac­ters between tags: they can be con­fused for small tags.
  7. Care­fully con­sider ren­der­ing in flash, or another vector-based method, if your users will expe­ri­ence the cloud largely through older browsers / agents: the font ren­der­ing in older browsers is not always good or con­sis­tent, but it is impor­tant that the cloud offer text that is read­ily digestible by search and index­ing engines, both locally and publicly
  8. If ren­der­ing the cloud in html, set the font size of ren­dered tags using whole per­cent­ages, rather than pixel sizes or dec­i­mals: this gives the dis­play agent more free­dom to adjust its final rendering.
  9. Do not insert line breaks: this allows the ren­der­ing agent to adjust the place­ment of line breaks to suit the ren­der­ing context.
  10. Offer the abil­ity to change the order between at least two options — alpha­bet­i­cal, and one vari­able dimen­sion (over­all weight, fre­quency, recency, etc.)

For fun, I’ve run these 10 best prac­tices through Tagcrowd. The major con­cepts show up well — font, color, and size are promi­nent — but obvi­ously the specifics of the things dis­cussed remain opaque.
Best Prac­tices For Dis­play as a Text Cloud

12 comments » | Tag Clouds

PEW Report Shows 28% Of Internet Users Have Tagged

February 1st, 2007 — 2:30pm

The Pew Inter­net & Amer­i­can Life Project just released a report on tag­ging that finds
28% of inter­net users have tagged or cat­e­go­rized con­tent online such as pho­tos, news sto­ries or
blog posts. On a typ­i­cal day online, 7% of inter­net users say they tag or cat­e­go­rize online con­tent.

The authors note “This is the first time the Project has asked about tag­ging, so it is not clear exactly how fast the trend is grow­ing.“
Wow — I’d say it’s grow­ing extremely quickly. Though I am on record as a believer in the bright future of tag clouds, I admit I’m sur­prised by these results. The fact that 7% of inter­net users tag daily is what’s most sig­nif­i­cant: it’s an indi­ca­tion of very rapid adop­tion for the prac­tice of tag­ging in many dif­fer­ent con­texts and many dif­fer­ent kinds of audi­ences, given it’s brief his­tory.
I’d guess this adop­tion rate com­pares to the rates of adop­tion for other new network-dependent or emer­gent archi­tec­tures like P2P music shar­ing or on-line music buy­ing.
You’re cor­rect if you’re think­ing there is a dif­fer­ence between tag­ging and tag clouds. And if you’ve read the report and the accom­pa­ny­ing inter­view with Dr. Wein­berger, you’ve likely real­ized that nei­ther Dr. Weinberger’s inter­view nor the report specif­i­cally addresses tag cloud usage. But remem­ber the First Prin­ci­ple of Tag Clouds: “Where there’s tags, there’s a tag cloud.” By def­i­n­i­tion, any item with an asso­ci­ated col­lec­tion of tags has a tag cloud, regard­less of whether that tag cloud is directly vis­i­ble and action­able in the user expe­ri­ence. So that 7% of inter­net users who tag daily are by default cre­at­ing and work­ing with tag clouds daily.
It might be time for tag clouds to look into get­ting some sun­glasses.

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Moving announcement, recent work, events...

August 6th, 2006 — 9:00pm

I’m pleased to announce an impend­ing plat­form change for JoeLamantia.com. No, this isn’t a switch in blog­ging pack­ages. It’s a move to New York (city!) that’s been in-progress for a while, and will take place at the end of the month (just a few weeks!). To make it seem a hard-won prize, I should note that we sur­vived sev­eral heat waves and tor­ren­tial down­pours, in addi­tion to fac­ing the cus­tom­ary inten­sity of the New York real estate mar­ket. Tales of brav­ery aside, all the adven­ture leaves us very much look­ing for­ward to unpack­ing and set­tling in soon. Look for an impromptu side­walk sale Brook­lyn while the truck is unloaded, as we real­ize exactly what will and will not fit into our new (smaller!) home in lovely Brook­lyn…
Beyond mov­ing, lots of good stuff is hap­pen­ing. Some of the things I’ll try to catch up on and post on in more detail when life set­tles down:

  • Recent work with topic maps
  • Recent work design­ing a faceted clas­si­fi­ca­tion sys­tem and faceted brows­ing experience
  • Poten­tial ways to quickly refine and eval­u­ate a facet sys­tem by involv­ing users — includ­ing cus­tomers — in iter­a­tive facet design and prototyping
  • Plan­ning for the first stages of an enter­prise meta­data effort
  • A tag cloud related project that will (I hope, pend­ing my travel sched­ule…) launch soon. Call to action: I’ve recruited one brave soul to help with this effort already, but there are many oth­ers with very inter­est­ing things to say on the sub­ject of tag clouds — please drop me a line if you’d like to be involved.

There are some very inter­est­ing IA-related events com­ing up: Oz-IA, EURO IA, and IDEA 2006 (I’ll be at this one). Too many good events for my travel bud­get, but hav­ing many good choices is a much bet­ter dilemma than hav­ing none at all…

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Cartograms, Tag Clouds and Visualization

May 22nd, 2006 — 10:56pm

I was enjoy­ing some of the engag­ing car­tograms avail­able from Worldmap­per, when I real­ized tag clouds might have some strong par­al­lels with car­tograms. After a quick sub­sti­tu­tion exer­cise, I’ve come to believe tag clouds could be to lists of meta­data what car­tograms are to maps; attempted solu­tions to sim­i­lar visu­al­iza­tion prob­lems dri­ven by com­mon and his­tor­i­cally con­sis­tent infor­ma­tion needs.
Here’s the train of thought behind the anal­ogy. Car­tograms are the dis­torted but cap­ti­vat­ing maps that change the famil­iar shapes of places on a map to visu­ally show data about geo­graphic loca­tions. Car­tograms change the way loca­tions appear to make a point or com­mu­ni­cate rel­a­tive dif­fer­ences in the under­ly­ing data; for exam­ple, by mak­ing coun­tries with higher GDP (gross domes­tic prod­uct) big­ger, and those with lower GDP smaller. In the exam­ple below, Japan’s size is much larger than it’s geo­graphic area, because it’s GDP is so high (it’s the dark green blob on the far right, much larger than China or India), while Africa is nearly invis­i­ble.
Gross Domes­tic Prod­uct

Tag clouds pur­sue the same goal: to enhance our under­stand­ing by com­mu­ni­cat­ing con­tex­tual mean­ing through changes in the way a set of things are visu­al­ized, rely­ing addi­tional dimen­sions of infor­ma­tion to make con­text explicit. Where car­tograms change geo­graphic units, tag clouds change the dis­play of a list of labels (the end point of a chain of link­ages con­nect­ing con­cepts to focuses) to com­mu­ni­cate the seman­tic impor­tance or con­text of the under­ly­ing con­cepts shown in the list.
Visu­ally, the rela­tion­ship of clouds to lists is sim­i­lar to that of maps and car­tograms; com­pare these two ren­der­ings of the most pop­u­lar search terms recorded by nytimes.com, one a sim­ple list and the other a tag cloud.
List Ren­der­ing of Search Terms

Cloud Ren­der­ing of Search Terms

This expla­na­tion of car­tograms from Car­togram Cen­tral a site sup­ported by the U.S. Geo­log­i­cal Sur­vey and tional Cen­ter for Geo­graphic Infor­ma­tion and Analy­sis makes the par­al­lels clearer, in greater detail.
“A car­togram is a type of graphic that depicts attrib­utes of geo­graphic objects as the object’s area. Because a car­togram does not depict geo­graphic space, but rather changes the size of objects depend­ing on a cer­tain attribute, a car­togram is not a true map. Car­tograms vary on their degree in which geo­graphic space is changed; some appear very sim­i­lar to a map, how­ever some look noth­ing like a map at all.“
Now for the cut and paste. Sub­sti­tute ‘tag cloud’ for car­togram, ‘seman­tic’ for geo­graphic, and ‘list’ in for map, and the same expla­na­tion reads:
“A tag cloud is a type of graphic that depicts attrib­utes of seman­tic objects as the object’s area. Because a tag cloud does not depict seman­tic space, but rather changes the size of objects depend­ing on a cer­tain attribute, a tag cloud is not a true list. Tag Clouds vary on their degree in which seman­tic space is changed; some appear very sim­i­lar to a list, how­ever some look noth­ing like a list at all.“
This is a good match for the cur­rent under­stand­ing of tag clouds.
Div­ing in deeper, Car­togram Cen­tral offers an excerpt from Car­tog­ra­phy: The­matic Map Design, that goes into more detail about the spe­cific char­ac­ter­is­tics of car­tograms.
Erwin Raisz called car­tograms ‘dia­gram­matic maps.’ Today they might be called car­tograms, value-by-area maps, anamor­phated images or sim­ply spa­tial trans­for­ma­tions. What­ever their name, car­tograms are unique rep­re­sen­ta­tions of geo­graph­i­cal space. Exam­ined more closely, the value-by-area map­ping tech­nique encodes the mapped data in a sim­ple and effi­cient man­ner with no data gen­er­al­iza­tion or loss of detail. Two forms, con­tigu­ous and non-contiguous, have become pop­u­lar. Map­ping require­ments include the preser­va­tion of shape, ori­en­ta­tion con­ti­gu­ity, and data that have suit­able vari­a­tion. Suc­cess­ful com­mu­ni­ca­tion depends on how well the map reader rec­og­nizes the shapes of the inter­nal enu­mer­a­tion units, the accu­racy of esti­mat­ing these areas, and effec­tive leg­end design. Com­plex forms include the two-variable map. Car­togram con­struc­tion may be by man­ual or com­puter means. In either method, a care­ful exam­i­na­tion of the logic behind the use of the car­togram must first be under­taken.“
Doing the same sub­sti­tu­tion exer­cise on this excerpt with the addi­tion of ‘rel­e­vance’ for value, ‘size’ for area, and ‘term’ for shape, yields sim­i­lar results:
“Erwin Raisz called tag clouds ‘dia­gram­matic lists.’ Today they might be called tag clouds, relevance-by-size lists, anamor­phated images or sim­ply spa­tial trans­for­ma­tions. What­ever their name, tag clouds are unique rep­re­sen­ta­tions of seman­tic space. Exam­ined more closely, the relevance-by-size list­ing tech­nique encodes the listed data in a sim­ple and effi­cient man­ner with no data gen­er­al­iza­tion or loss of detail. Two forms, con­tigu­ous and non-contiguous, have become pop­u­lar. List­ing require­ments include the preser­va­tion of term, ori­en­ta­tion, con­ti­gu­ity, and data that have suit­able vari­a­tion. Suc­cess­ful com­mu­ni­ca­tion depends on how well the list reader rec­og­nizes the terms (of the inter­nal enu­mer­a­tion units), the accu­racy of esti­mat­ing these sizes, and effec­tive leg­end design. Com­plex forms include the two-variable list. Tag cloud con­struc­tion may be by man­ual or com­puter means. In either method, a care­ful exam­i­na­tion of the logic behind the use of the tag cloud must first be under­taken.“
The cor­re­spon­dence here is strong as well.
Sta­ble Need
The fact that car­tograms and tag clouds show close par­al­lels means that while the tag cloud may be a new user inter­face ele­ment emerg­ing for the Web (and major desk­top appli­ca­tions like Out­look, in the case of Tagloc­ity), tag clouds as a type of visu­al­iza­tion have strong prece­dents in other much more mature user expe­ri­ence con­texts, such as the dis­play of mul­ti­ple dimen­sions of infor­ma­tion within geo­graphic or geospa­tial frames of ref­er­ence. Instances of strong cor­re­spon­dence of prob­lem solv­ing approach in both mature and emerg­ing con­texts could indi­cate sim­ple appli­ca­tion of par­al­lel fram­ing (from the mature con­text to the emerg­ing con­text) as an untested con­di­tional, until the true extent of diver­gence sep­a­rat­ing the two con­texts is under­stood. This is very com­mon new media.
Instead, in the case of tag clouds, I think it points at sta­ble needs dri­ving struc­turally sim­i­lar solu­tions to the basic prob­lem of how to visu­ally com­mu­ni­cate impor­tant rela­tion­ships and addi­tional dimen­sions of mean­ing under the lim­i­ta­tions of inher­ent flat­ness. The par­al­lels between car­tograms and tag clouds place the appear­ance of the tag cloud within the larger his­tory of con­tin­u­ing explo­ration of new ways of visu­al­iz­ing infor­ma­tion. In this view, tag clouds are a recent man­i­fes­ta­tion of the sta­ble need to cre­ate strong and effec­tive visual ways of con­vey­ing more than mem­ber­ship in a one-dimensional set (the list), or loca­tion and extent within a two-dimensional coör­di­nate sys­tem (the map).

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