Joe Lamantia.com

I’ve been focused on under­stand­ing future direc­tions in the land­scape of dig­i­tal expe­ri­ences recently (which nicely par­al­lels some of the work I’ve been doing on design and futures in gen­eral), so I’m shar­ing a sum­mary of the analy­sis that’s come out of this research.
This pre­sen­ta­tion shares an overview of all the major waves of change affect­ing dig­i­tal expe­ri­ences, some of the espe­cially forward-looking insights around shifts in our iden­ti­ties, and the impli­ca­tions for those cre­at­ing dig­i­tal expe­ri­ences.
The 8 waves dis­cussed here (are there more? let me know!)

• Dig­i­tal = Social
• Co-Creation
• Dig­i­tal Natives
• Itʼs All a Game
• Take Away
• Every­ware
• Con­ver­gence
• See­ing Is Believing

Jung posited the idea of the col­lec­tive uncon­scious (later refined, but a good point of depar­ture). Do Daylife and sim­i­lar stream aggre­ga­tors / visu­al­iz­ers (I’m reach­ing for a han­dle to describe these enti­ties) like Uni­verse, point at what a col­lec­tive con­scious could be?
Uni­verse

Some pre­cur­sors might be Yahoo’s Taglines and TagMaps, Google Zeit­geist / Trends, and the var­i­ous cloud style visu­al­iza­tions like clouda­li­cious, etc.
Plainly, the num­ber and vari­ety of tools and des­ti­na­tions for visu­al­iz­ing what’s on the mind of groups is grow­ing rapidly.
If the par­al­lelism holds, mean­ing Daylife and kin are them­selves points of depar­ture, where is this going? I’m not think­ing of col­lec­tive intel­li­gence — just the visu­al­iza­tion aspect, and how that may evolve.

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?)

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.

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

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).

Though you may not have noticed it at first (I didn’t — they’re located a few steps off the front page), the recently launched design of NYTimes.com includes tag clouds. After a quick review, I think their ver­sion is a good exam­ple of a cloud that offers some increased capa­bil­i­ties and con­tex­tual infor­ma­tion that together fall in line with the likely direc­tions of tag cloud evo­lu­tion we’ve con­sid­ered before.
Specif­i­cally, the New York Times tag cloud:

1. allows users to change the cloud’s con­text — and thus its con­tent — with a set of con­trols (vis­i­ble as tabs, run­ning across the top)
2. lets cloud con­sumers change the dis­play behav­ior of the cloud by switch­ing modes from list to cloud in-line, not out­side the user’s area of activity
3. sup­ports the chain of under­stand­ing for cloud con­sumers by pro­vid­ing clear indi­ca­tion of the time period cov­ered (the note about update frequency)
4. offers [lim­ited] capa­bil­i­ties to work with / share tag cloud con­tent out­side the cloud via email — though the mes­sage con­tains only a link to the cloud page, and not a full rendering

NYtimes.com Tag Cloud

The NYTimes.com tag cloud shows the most pop­u­lar search terms used by read­ers within three time frames: the last 24 hours, the last 7 days, and the last 30 days. Choos­ing search terms as the makeup for a cloud is a bit curi­ous — but it may be as close to socially gen­er­ated meta­data as seemed rea­son­able for a first explo­ration (one that doesn’t require a sub­stan­tial change in the busi­ness or pub­lish­ing model).
Given the way that clouds lend them­selves to show­ing mul­ti­ple dimen­sions of mean­ing, such as change over time, I think the Times tag cloud would be more valu­able if it offered the option to see all three time frames at once. I put together a quick cut and paste of a con­cept screen that shows this sort of lay­out:
Screen Con­cept: 3 Clouds for Dif­fer­ent Time Frames

In an exam­ple of the rapid mor­ph­ing of memes and def­i­n­i­tions to fit shift­ing usage con­texts (as in Thomas Vanderwal’s obser­va­tions on the shift­ing usage of folk­son­omy) the NYTimes.com kept the label tag cloud, while this is more prop­erly a weighted list: the tags shown are in fact search terms, and not labels applied to a focus of some kind by tag­gers.
It’s plain from the lim­ited pres­ence and vis­i­bil­ity of clouds within the over­all site that the staff at NYTimes.com are still explor­ing the value of tag clouds for their spe­cific needs (which I think is a mature approach), oth­er­wise I imag­ine the new design con­cept and nav­i­ga­tion model would uti­lize and empha­sized tag clouds to a greater degree. So far, the Times uses tag clouds only in the new “Most Pop­u­lar” sec­tion, and they are offered as an alter­na­tive to the default list style pre­sen­ta­tion of pop­u­lar search terms. This posi­tion within the site struc­ture places them a few steps in, and off the stan­dard front page-to-an-article user flow that must be one of the core sce­nar­ios sup­ported by the site’s infor­ma­tion archi­tec­ture.
NYTimes.com User Flow to Tag Cloud

Still, I do think it’s a clear sign of increas­ing aware­ness of the poten­tial strength of tag clouds as a way of visu­al­iz­ing seman­tic infor­ma­tion. The Times is an estab­lished entity (occa­sion­ally serv­ing as the def­i­n­i­tion of ‘the estab­lish­ment’), and so is less likely to endan­ger estab­lished rela­tion­ships with cus­tomers by chang­ing its core prod­uct across any of the many chan­nels used for deliv­ery.
Ques­tions of risk aside, tag clouds (here I mean any visu­al­iza­tion of seman­tic meta­data) couLd be a very effec­tive way to scan the head­lines for a sense of what’s hap­pen­ing at the moment, and the shift­ing impor­tance of top­ics in rela­tion to on another. With a tag cloud high­light­ing “immi­gra­tion”, “duke”, and “judas”, vis­i­tors can imme­di­ately begin to under­stand what is news­wor­thy — at least in the minds of NYTimes.com read­ers.
At first glance, low­er­ing the amount of time spent read­ing the news could seem like a strong busi­ness dis­in­cen­tive for using tag clouds to stream­line nav­i­ga­tion and user flow. With more con­sid­er­a­tion, I think it points to a new poten­tial appli­ca­tion of tag clouds to enhance com­pre­hen­sion and find­abil­ity by giv­ing busy cus­tomers pow­er­ful tools to increase the speed and qual­ity of their judg­ments about what to devote their atten­tion to in order to acheive under­stand­ing greater depth. In the case of pub­li­ca­tions like the NYTimes.com, tag clouds may be well suited for con­vey­ing snap­shots or sum­maries of com­plex and deep domains that change quickly (what’s the news?), and offer­ing rapid nav­i­ga­tion to spe­cific areas or top­ics.
A new user expe­ri­ence that offers a vari­ety of tag clouds in more places might allow dif­fer­ent kinds of move­ment or flow through the larger envi­ron­ment, enabling new behav­iors and sup­port­ing dif­fer­ing goals than the cur­rent infor­ma­tion archi­tec­ture and user expe­ri­ence.
Pos­si­ble Screen Flow Incor­po­rat­ing Clouds

Step­ping back from the specifics of the design, a broader ques­tion is “Why tag clouds now?” They’re cer­tainly timely, but that’s not a busi­ness model. This is just spec­u­la­tion, but I recall job post­ings for an Infor­ma­tion Archi­tect posi­tion within the NYTimes.com group on that appeared on sev­eral recruit­ing web­sites a few months ago — maybe the new team mem­bers wanted or were directed to include tag clouds in this design? If any of those involved are allowed to share insights, I’d very much like to hear the thoughts of the IAs / design­ers / prod­uct man­agers or other team mem­bers respon­si­ble for includ­ing tag clouds in the new design and struc­ture.
And in light of Mathew Patterson’s com­ments here about cus­tomer accep­tance of mul­ti­ple clouds in other set­tings and con­texts (price­line europe), I’m curi­ous about any usabil­ity test­ing or other user research that might have been done around the new design, and any the find­ings related to tag clouds.

I believe the value of sec­ond gen­er­a­tion clouds will be to offer ready nav­i­ga­tion and access to deep, com­plex land­scapes of mean­ing built up from the cumu­la­tive seman­tic infor­ma­tion con­tained in many inter­con­nected tag clouds. I’d like share some thoughts on this idea; I’ll split the dis­cus­sion into two posts, because there’s a fair amount of mate­r­ial.
In a pre­vi­ous post on tag clouds, I sug­gested that the great value of first gen­er­a­tion tag clouds is their abil­ity to make con­cepts and meta­data — seman­tic fields — broadly acces­si­ble and easy to under­stand and work with through visu­al­iza­tion. I believe the shift in the bal­ance of roles and value from first to sec­ond gen­er­a­tion reflects nat­ural growth in cloud usage and aware­ness, and builds on the two major trends of tag cloud evo­lu­tion: enhanced visu­al­iza­tion and func­tion­al­ity for work­ing with clouds, and pro­vi­sion of exten­sive con­tex­tual infor­ma­tion to accom­pany tag clouds.
Together, these two growth paths allow cloud con­sumers to fol­low the indi­vid­ual chains of under­stand­ing that inter­sect at con­nected clouds, and bet­ter achieve their goals within the infor­ma­tion envi­ron­ment and out­side. Fun­da­men­tally, I believe the key dis­tinc­tions between first and sec­ond gen­er­a­tion clouds will come from the way that clouds func­tion simul­ta­ne­ously as visu­al­iza­tions and nav­i­ga­tion mech­a­nisms, and what they allow nav­i­ga­tion of — land­scapes of mean­ing that are rich in seman­tic con­tent of high value.
For exam­ples of both direc­tions of tag cloud evo­lu­tion com­ing together to sup­port nav­i­ga­tion of seman­tic land­scapes, we can look at some of the new fea­tures del.icio.us has released in the past few months. I’ve col­lected three ver­sions of the infor­ma­tion archi­tec­ture of the stan­dard del.icio.us URL details page from the past seven months as an exam­ple of evo­lu­tion hap­pen­ing right now.
The first ver­sion (screen­shot and break­down in Fig­ure 1) shows the URL details page some­time before August 15^th, 2005, when it appeared on Matt McAlister’s blog.
Fig­ure 1: Del.icio.us URL Page — August 2005

The lay­out or infor­ma­tion archi­tec­ture is fairly sim­ple, offer­ing a list of the com­mon tags for the url / focus, a sum­mary of the post­ing his­tory, and a more detailed list­ing of the post­ing his­tory that lists the dates and tag­gers who book­marked the item, as well as the tags used for book­mark­ing. There’s no cloud style visu­al­iza­tion of the tags attached to this sin­gle focus avail­able: at this time, del.icio.us offered a ren­dered tag cloud visu­al­iza­tion at the aggre­gate level for the whole envi­ron­ment.
Envi­ron­ment and sys­tem design­ers know very well that as the scope and com­plex­ity of an envi­ron­ment increase — in this case, the num­ber of tag­gers, focuses, and tags, plus their cumu­la­tive his­to­ries — it becomes more impor­tant for peo­ple to be explic­itly aware of the con­text of any item in order to under­stand it prop­erly. Explicit con­text becomes more impor­tant because they can rely less and less on implicit con­text or assump­tions about con­text based on the uni­ver­sal aspects of the envi­ron­ment. This is how cloud con­sumers’ needs for clearly vis­i­ble and acces­si­ble chains of under­stand­ing dri­ves the fea­tures and capa­bil­i­ties of tag clouds. Later ver­sions of this page addresses these needs in dif­fer­ing ways, with dif­fer­ing lev­els of suc­cess.
Fig­ure 2 shows a more recent ver­sion of the del.licio.us his­tory for the Ma.gnolia.com ser­vice. This screen­shot taken about ten days ago in early March, while I was work­ing on a draft of this post.
Fig­ure 2: Del.icio.us URL Page — Early March 2006

Key changes from the first ver­sion in August to this sec­ond ver­sion include:

1. Chang­ing visu­al­iza­tion of the Com­mon Tags block to a cloud style rendering
2. Remov­ing the indi­vid­ual tags cho­sen by each tag­ger from the Post­ing His­tory block
3. The addi­tion of a large and promi­nent block of space devoted to “User Notes”
4. Mov­ing the Post­ing His­tory block to the right column
5. Chang­ing visu­al­iza­tion of the Post­ing His­tory block to a proto-cloud style rendering

The most impor­tant change in this sec­ond ver­sion is the removal of the indi­vid­ual sets of tags from the Post­ing His­tory. Sep­a­rat­ing the tags applied to the focus from asso­ci­a­ton with the indi­vid­ual tag­gers that chose them strips them of an impor­tant layer of con­text. Remov­ing the nec­es­sary con­text for the tag cloud breaks the chain of under­stand­ing (Fig­ure 3) link­ing tag­gers and cloud con­sumers, and obscures or increases the costs of the social con­cep­tual exchange that is the basic value of del.icio.us to its many users. In this ver­sion, cloud con­sumers con­sumers read­ing the URL details page can only find spe­cific tag­gers based on the con­cepts they’ve matched with this focus by vis­it­ing or nav­i­gat­ing to each indi­vid­ual tag­gers’ area within the larger del.icio.us envi­ron­ment one at a time.
Fig­ure 3: Chain of Under­stand­ing

The switch to ren­der­ing the Com­mon Tags block as a tag cloud is also impor­tant, as an indi­ca­tor of the con­sis­tent spread of clouds to visu­al­ize seman­tic fields, and their grow­ing role as nav­i­ga­tion tools within the larger land­scape.
The User Notes are a good exam­ple of an attempt to pro­vide addi­tional con­tex­tual infor­ma­tion with (poten­tially) high value. User Notes are cre­ated by users exclu­sively for the pur­pose of pro­vid­ing con­text. The other forms of con­text shown in the new lay­out — the Post­ing His­tory, Related Items — serve a con­tex­tual func­tion, but are not cre­ated directly by users with this goal in mind. The dif­fer­ence between the two pur­poses for these items undoubt­edly influ­ences the way that peo­ple cre­ate them, and what they cre­ate: it’s a ques­tion that more detailed inves­ti­ga­tions of tag­ging prac­tices will surely exam­ine.
The third ver­sion of the same URL his­tory page, shown in Fig­ure 4, was released very shortly after the sec­ond, prov­ing tag cloud evo­lu­tion is hap­pen­ing so quickly as to be dif­fi­cult to track delib­er­ately on a broad scale.
Fig­ure 4: Del.icio.us URL Page — March 2006 #2

This ver­sion changes the con­tent and lay­out of the Post­ing His­tory block, restor­ing the com­bined dis­play of indi­vid­ual tag­gers who tagged the URL, with the tags they applied to it, in the order in which they tagged the URL for the first time.
The third ver­sion makes two marked improve­ments over the first and sec­ond versions:

1. Pre­sen­ta­tion of the indi­vid­ual chains of under­stand­ing that inter­sect with this focus / cloud in nav­i­ga­ble form, to increase aware­ness of the con­text for this item and allow users to retrace these paths to their origins
2. Pre­sen­ta­tion of indi­vid­ual tag­gers’ flat­tened clouds that inter­sect this focus as nav­i­ga­tion mech­a­nisms for mov­ing from the cur­rent focus to else­where within the larger landscape

These three dif­fer­ent ver­sions of the del.icio.us URL details page show that the amount and type of con­tex­tual infor­ma­tion accom­pa­ny­ing a sin­gle focus is increas­ing, and that the num­ber of con­crete nav­i­ga­ble con­nec­tions to the larger seman­tic land­scape of which the focus is one ele­ment also increas­ing
Over­all, it’s clear that clouds are quickly emerg­ing as nav­i­ga­tion tools for com­plex land­scapes of mean­ing, and that cloud con­text has and will con­tinue to become more impor­tant for cloud cre­ation and use.
And so before dis­cussing the con­text nece­sary for clouds and the role of clouds as nav­i­ga­tion aids in more detail, it will be help­ful to get an overview of land­scapes of mean­ing, and how they arise.
Land­scapes of Mean­ing
A land­scape of mean­ing is a densely inter­con­nected, highly valu­able, exten­sive infor­ma­tion envi­ron­ment rich in seman­tic con­tent that is cre­ated by com­mu­ni­ties of tag­gers who build con­nected tag clouds. In the early land­scapes of mean­ing emerg­ing now, a con­nec­tion between clouds can be a com­mon tag, tag­ger, or focus: any one of the three legs of the Tag­ging Tri­an­gle required for a tag cloud (more on this below). Because tag clouds visu­al­ize seman­tic fields, con­nected tag clouds visu­al­ize and offer access to con­nected seman­tic fields, serv­ing as bridges between the indi­vid­ual accu­mu­la­tions of mean­ing each cloud con­tains.
Con­nect­ing hun­dreds of thou­sands of indi­vid­u­ally cre­ated clouds and fields, as del.icio.us has enabled social book­mark­ers to do by pro­vid­ing nec­es­sary tools and infra­struc­ture, cre­ates a very large infor­ma­tion envi­ron­ment whose ter­rain or geog­ra­phy is com­posed of seman­tic infor­ma­tion. Such a seman­tic land­scape is a land­scape con­structed or made up of mean­ing. It is an infor­ma­tion envi­ron­ment that allows peo­ple to share con­cepts or for social pur­poses of all kinds, while sup­ported with visu­al­iza­tion, con­tex­tual infor­ma­tion, func­tion­al­ity, and far-ranging nav­i­ga­tion capa­bil­i­ties.
The flickr Land­scape
flickr is a good exam­ple of a land­scape of mean­ing that we can under­stand as a seman­tic land­scape. In a pre­vi­ous post on tag clouds, I con­sid­ered the flickr all time most pop­u­lar tags cloud (shown in Fig­ure 5) in light of the basic struc­ture of clouds:
“The flickr style tag cloud is …a visu­al­iza­tion of many tag sep­a­rate clouds aggre­gated together. …the flickr tag cloud is the visu­al­iza­tion of the cumu­la­tive seman­tic field accreted around many dif­fer­ent focuses, by many peo­ple. …the flickr tag cloud func­tions as a visu­al­iza­tion of a seman­tic land­scape built up from all asso­ci­ated con­cepts cho­sen from the com­bined per­spec­tives of many sep­a­rate tag­gers.”
Fig­ure 5: The flickr All Time Most Pop­u­lar Tags Cloud

From our ear­lier look at the struc­ture of first gen­er­a­tion tag clouds we know a tag cloud visu­al­izes a seman­tic field made up of con­cepts referred to by labels which are applied as tags to a focus of some sort by tag­gers.
Based on our under­stand­ing of the struc­ture of a tag cloud as hav­ing a sin­gle focus, the flickr cloud shows some­thing dif­fer­ent because it includes many focuses. The flickr all time most pop­u­lar tags cloud com­bines all the indi­vid­ual tag clouds around all the indi­vid­ual pho­tos in flickr into a sin­gle visu­al­iza­tion, as Fig­ure 6 shows.
Fig­ure 6: The flickr Land­scape of Mean­ing

This means the flickr all time most pop­u­lar tags cloud is in fact a visu­al­iza­tion of the com­bined seman­tic fields behind each of those indi­vid­ual clouds. It’s quite a bit big­ger in scope than a tra­di­tional sin­gle focus cloud. Because the scope is so large, the amount of mean­ing it sum­ma­rizes and con­veys is tremen­dous. The all time most pop­u­lar tags cloud is in fact a his­toric win­dow on the cur­rent and his­tor­i­cal state of the seman­tic land­scape of flickr as a whole.
This is where con­text becomes crit­i­cal to the proper under­stand­ing of a tag cloud. The cloud title “All time most pop­u­lar tags” sets the con­text for this tag cloud, within the bound­aries of the larger land­scape envi­ron­ment defined and com­mu­ni­cated by flickr’s user epx­e­ri­ence. With­out this title, the cloud is mean­ing­less despite the large and com­plex seman­tic land­scape — all of the infor­ma­tion envi­ron­ment of flickr — it visu­al­izes so effec­tively, because cloud con­sumers can­not retrace a com­plete chain of under­stand­ing to cor­rectly iden­tify the cloud’s ori­gin.
flickr — 1^st Gen­er­a­tion Land­scape Nav­i­ga­tion
The flickr cloud is a pow­er­ful nav­i­ga­tion mech­a­nism for quickly and eas­ily mov­ing about within the land­scape of mean­ing built up by all those thou­sands and thou­sands of indi­vid­ual clouds. Still, because it is a first gen­er­a­tion cloud, we can­not directly fol­low any of the many indi­vid­ual chains of under­stand­ing con­nect­ing this cloud’s tags back to spe­cific tag­gers, or the con­cepts they asso­ciate with spe­cific pho­tos or focuses. In this visu­al­iza­tion, the group’s under­stand­ing of mean­ing is more impor­tant than any individual’s under­stand­ing. And so the flickr cloud does not yet allow us com­pre­hen­sive nav­i­ga­tion of the under­ly­ing seman­tic land­scape illus­trated in Fig­ure 6 (chains of under­stand­ing sug­gested in light green). The flickr cloud also remains a first gen­er­a­tion tag cloud because users can­not con­trol its con­text.
Fig­ure 7: A Seman­tic Land­scape

Even so, these nav­i­ga­tional and con­tex­tual needs will help iden­tify the way that users rely on clouds to work in land­scapes of mean­ing.
Growth of Land­scapes
Land­scapes of mean­ing like flickr, del.icio.us, or the bur­geon­ing num­ber of social seman­tic busi­ness ven­tures debut­ing as I write — typ­i­cally grow from the bot­tom up, emerg­ing as dozens or thou­sands of indi­vid­ual tag clouds cre­ated for dif­fer­ent rea­sons by dif­fer­ent tag­gers coin­ci­den­tally or delib­er­ately inter­con­nect and over­lap, all of this hap­pen­ing through a vari­ety of social mech­a­nisms. Tag­gers typ­i­cally cre­ate con­nected or over­lap­ping tag clouds one at a time, adding tags, focuses, and tag­gers (by cre­at­ing new accounts) in the ad hoc fash­ion of open net­works and archi­tec­tures. But first we should look at the Tag­ging Tri­an­gle to under­stand the most basic ele­ments of a tag cloud.
The Tag­ging Tri­an­gle
To make a tag cloud, you have to have three ele­ments: a focus, a tag­ger, and a(t least one) tag. I call this the Tag­ging Tri­an­gle, illus­trated in Fig­ure 8. In the most com­mon ren­der­ings of famil­iar tag clouds, one or two of these ele­ments are often implied but not shown: yet all three are always present.
This illus­tra­tion shows a cloud of labels, not tags, because a ren­dered cloud is really a list of labels. The labels shown in most first gen­er­a­tion clouds are often tags, but struc­turally they could also be a set of names for tag­gers, as in the del.icio.us post­ing his­tory block proto-cloud we saw above, or a set of focuses as in the ‘Inverted Cloud’ I sug­gested.
Fig­ure 8: The Tag­ging Tri­an­gle

An Exam­ple Land­scape
A sim­ple exam­ple of the growth of seman­tic land­scapes leads nat­u­rally to the dis­cus­sion of spe­cific ways that tag clouds will enable nav­i­ga­tion within large land­scapes of mean­ing.
Fig­ure 9 shows the tag cloud accreted around a sin­gle focus. This cloud includes some of the tags that Tag­ger 1 has used in total across all the tag clouds she’s cre­ated (those other clouds aren’t shown). We’ll assume that she’s cre­ated other clouds for other focuses.
Fig­ure 9: A Sin­gle Tag Cloud

When a sec­ond per­son, Tag­ger 2, tags that same focus (again with a sub­set of the total set of all his tags), and some of those tags are the same as those used for this focus by Tag­ger 1, their indi­vid­ual tag clouds for this focus (shown by the dashed line in the cumu­la­tive tag cloud) con­nect via the com­mon tags, and the cumu­la­tive cloud grows. If any of the tags from their total sets are the same, but are not used for this focus, they form another con­nec­tion between the two tag­gers. Fig­ure 10 shows two indi­vid­ual clouds con­nected in both these ways.
Fig­ure 10: Two Con­nected Clouds

When a third tag­ger adds a third cloud with com­mon tags and unique tags around the same focus, the cumu­la­tive cloud grows, and the num­ber of both kinds of con­nec­tions between tags and tag­gers grows. Fig­ure 11 shows three con­nected clouds.
Fig­ure 11: Con­nected Clouds

Every tag cloud visu­al­izes a seman­tic field, and so the result of this bot­tom up growth is a series of inter­linked seman­tic fields cen­tered around a com­mon focus, as Fig­ure 12 shows. Since seman­tic fields are made of con­cepts, linked fields result in linked con­cepts.
Fig­ure 12: Con­nected Seman­tic Fields

The total num­ber and the vari­ety of kinds of inter­con­nec­tions amongst these three tag­gers, their tags, and a sin­gle focus is remark­able. As this sim­ple exam­ple shows, the total num­ber and den­sity of con­nec­tions link­ing even a mod­er­ate size pop­u­la­tion of tag­gers, tags, and focuses could quickly become very large. This increased scale dri­ves qual­i­ta­tive and quan­ti­ta­tive topol­ogy changes in the net­work that per­mit a land­scape of mean­ing to emerge from con­nected seman­tic fields.
Land­scapes And Depth
The accu­mu­la­tion of con­nec­tions and con­cepts cre­ates a land­scape of mean­ing with real depth; but it’s the depth of a land­scape that dri­ves its value. For this dis­cus­sion, I’m defin­ing depth loosely as the amount of seman­tic infor­ma­tion or the den­sity of the seman­tic field either across the whole land­scape, or at a cho­sen point.
Value of course is a very sub­jec­tive judge­ment. In par­tic­i­pa­tory economies like that of del.icio.us, the value to indi­vid­ual users is pre­dom­i­nantly one of loosely struc­tured seman­tic exchange based on accu­mu­la­tion of col­lec­tive value through shared indi­vid­ual efforts. From a busi­ness view­point, a group of investors and yahoo as a buyer saw con­sid­er­able value in the emer­gent land­scape and / or other kinds of assets
To make the idea of depth a bit clearer, Fig­ure 13 illus­trates two views of a seman­tic land­scape built up by the over­lap of tag clouds. The aer­ial view shows the con­tents, dis­tri­b­u­tion, and over­lap of a num­ber of tag clouds around a set of focuses. The hori­zon view shows the depth of the seman­tic field for each focus, based on the amount of over­lap or con­nec­tion between the cloud around that focus and all the other clouds.
Fig­ure 13: Seman­tic Land­scape Depth Views

Of course this is only a con­cep­tual way of show­ing the cumu­la­tive seman­tic infor­ma­tion that makes up a land­scape of mean­ing, so it does not address the rel­a­tive value of this infor­ma­tion. Plainly some indi­ca­tion of the qual­ity of the seman­tic infor­ma­tion in a land­scape is crit­i­cal impor­tant to mea­sure­ments of both depth and value. Met­rics for qual­ity could come from a com­bi­na­tion of assess­ment of the diver­sity and gran­u­lar­ity of the tag pop­u­la­tion for the focus, bench­marks for the domain of the focus and tag­gers (health­care indus­try), and an esti­mate on the matu­rity of the domain, the focus, and the tag clouds in the seman­tic land­scape.
Look­ing ahead, it’s likely that accepted met­rics for defin­ing and describ­ing the depth, value, and char­ac­ter­is­tics of seman­tic fields and land­scapes will emerge as new com­bi­na­tions of some of the mea­sure­ments used now in the realms of cog­ni­tive lin­guis­tics, set the­ory, sys­tem the­ory, topol­ogy, infor­ma­tion the­ory, and quite a few other dis­ci­plines besides.
In Part Two
The sec­ond post in this series of two will fol­low sev­eral of the top­ics intro­duced here to con­clu­sion, as well as cover some new top­ics, including:

• How chains of under­stand­ing shape needs for cloud con­text and nav­i­ga­tion paths
• How the tag­ging tri­an­gle will define nav­i­ga­tion within land­scapes of meaning
• The emer­gence of strat­i­fi­ca­tion in land­scapes of meaning
• The idea that clouds and land­scapes have a shape which con­veys mean­ing and value
• The kinds of con­tex­tual infor­ma­tion and con­trols nec­es­sary for nav­i­ga­tion and social exchanges

Watch­ing Nav­i­ga­tion Fol­low Chains of Under­stand­ing
I’ll close with a screen­cast put together by Jon Udell that cap­tures a wide rang­ing nav­i­ga­tion path through the del.icio.us landscape.

The Feb­ru­ary edi­tion of Usabil­ity News reports on a usabil­ity study (Where’s the Search? Re-examining User Expec­ta­tions of Web Objects) of user expec­ta­tions for Web page lay­outs that con­tains a sur­pris­ing but inter­est­ing visu­al­iza­tion of page shapes, based on quan­ti­ta­tive user research. (Note: I found the study via the UI Design Newslet­ter, from HFI.)
The study looks at users” expec­ta­tions for the loca­tion of com­mon web page com­po­nents, such as site search and adver­tis­ing. The authors find that expec­ta­tions for page lay­outs are largely the same now, as com­pared to those found in an ear­lier study, Devel­op­ing Schemas for the Loca­tion of Com­mon Web Objects, con­ducted in 2001.
More inter­est­ing is the way the researchers report their results; visu­al­iz­ing them as heat map style grid plots for the expected loca­tion of each ele­ment vs. a blank grid. Here’s two exam­ples, the first show­ing expected loca­tions for ‘back to home’ links, the sec­ond for the ‘site search engine’.
Fig­ure 1: Back to Home Link Loca­tion

Fig­ure 2: Site Search Engine Loca­tion

These heat maps look a lot like page shapes, expressed as scat­ter­plots.
I like the com­bi­na­tion of quan­ti­ta­tive and qual­i­ta­tive per­spec­tives at work in these page shapes ren­dered as scat­ter­plots. I think it could allow for grounded dis­cus­sion and inter­pre­ta­tion of user feed­back on design options, within a clear and sim­ple struc­ture that doesn’t require an HCI degree to appre­ci­ate. If I try it out, I’ll share the out­comes.
In a more tra­di­tional style of visu­al­iza­tion, Eric Scheid found another another good exam­ple of page shapes a while back in Jonathon Boutelle’s post­ing on blog lay­outs called “Mullet”-style blog lay­out. Jonathon was advo­cat­ing for a new default blog page shape that increases infor­ma­tion den­sity and scent, but hews closely to pre-existing expec­ta­tions.
Fig­ure 3: Typ­i­cal Blog Page Shape

Fig­ure 4: Sug­gested Blog Page Shape

And that’s the last time I’m men­tion­ing m.u.l.l.e.t.s this year, lest Google get the wrong idea about the sub­ject mat­ter of this blog