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Tuesday, 24 July 2012

My Adopted Chinese Name

In China there is a very famous Canadian by the name of Mark Rowswell dashan.com. One of the reasons he is so famous in China is that his Chinese is very very good. His adopted Chinese name is 大山 (dàshān) which means great or large mountain.

Several years ago I decided to also adopt a Chinese name. One day a name popped into my mind. Mark's Chinese is very good but my Chinese is only basic. Consequently, I chose the name 小山 (xiǎoshān) which means little mountain 😀

An advantage of having an adopted name is that one can change it and I can change it to reflect my progress in mastering the Chinese language. So as my Chinese improves I can change it to 中山 (zhōngshān) which means middle mountain. Then 大山 (dàshān) and finally, if I ever reach this level of proficiency, 巨山 (jùshān) which means gigantic mountain.

There are though, some days when I think my Chinese is so poor that maybe my adopted name should be 微山 (Wēishān) as this means micro mountain.

Thursday, 14 June 2012

Rethinking data

"Data! Data! Data!" he cried impatiently. "I can't make bricks without clay." — Sherlock Holmes in The Adventure of the Copper Beeches.

Data may be the preeminent obsession of our age [1]. We marvel at the ever-growing quantity of data on the Internet, and fortunes are made when Google sells shares for the first time on the stock market. We worry about how corporations and governments collect, protect, and share our personal information. A beloved character on a television science fiction show is named Data. We spend billions of dollars to convert the entire human genome into digital data, and having completed that, barely pause for breath before launching similar and even larger bioinformatic endeavours. All this attention being paid to data reflects a real societal transformation as ubiquitous computing and the Internet refashion our economy and, in some respects, our lives. However, as with other important transformations—think of Darwin's theory of natural selection, and the revolutionary advances in genetics and neuroscience—misinterpretation, misapplication, hype, and fads can develop. In this post, I'd like to examine the current excitement about data and where we may be going astray.

Big Data

Writing in the New York Times, Steve Lohr points out that larger and larger quantities of data are being collected—a phenomenon that has been called "Big Data":

In field after field, computing and the Web are creating new realms of data to explore — sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, a research firm.

Widespread excitement is being generated by the prospect of corporations, governments, and scientists mining these immense data sets for insights. In 2008, a special issue of the journal Nature was devoted to Big Data. Microsoft Research's 2009 book, The Fourth Paradigm: Data-Intensive Scientific Discovery, includes these reflections by Craig Mundie (p.223):

Computing technology, with its pervasive connectivity via the Internet, already underpins almost all scientific study. We are amassing previously unimaginable amounts of data in digital form—data that will help bring about a profound transformation of scientific research and insight.

The enthusiasm in the lay press is even less restrained. Last November, Popular Science had a special issue all about data. It has a slightly breathless feel—one of the articles is titled "The Glory of Big Data"—which is perhaps not so surprising in a magazine whose slogan is "The Future Now".

Data Science

Along with the growth in data, there has been a tremendous growth in analytical and computational tools for drawing inferences from large data sets. Most prominently, techniques from computer science—in particular data mining and machine learning—have frequently been applied to big data. These approaches can often be applied automatically—which is to say, without the need to make much in the way of assumptions, and without explicitly specifying models. What is more, they tend to be scalable—it is feasible (in terms of computing resources and time) to apply them to enormous data sets. Such approaches are sometimes seen as black boxes in that the link between the inputs and the outputs is not entirely clear. To some extent these characteristics stand in contrast with statistical techniques, which have been less optimized for use with very large data sets and which make more explicit assumptions based on the nature of the data and the way they were collected. Fitted statistical models are interpretable, if sometimes rather technical.

In an article on big data, Sameer Chopra suggests that organizations should "embrace traditional statistical modeling and machine learning approaches". Some have argued that a new discipline is forming— dubbed data science [2]—which combines these and other techniques for working with data. In 2010, Mike Loukides at O'Reilly Media wrote a good summary of data science, except for this odd claim:

Using data effectively requires something different from traditional statistics, where actuaries in business suits perform arcane but fairly well-defined kinds of analysis.

Leaving aside the confusion between statistics and actuarial science (not to mention stereotyped notions of typical attire), what is curious is the suggestion that "traditional statistics" has little role to play in the effective use of data. Chopra is more diplomatic: machine learning "lends itself better to the road ahead". Now, in many cases, a fast and automatic method may indeed be just what's needed. Consider the recommendations we have come to expect from online stores. They may not be perfect, but they can be quite convenient. Unfortunately, the successes of computing-intensive approaches for some applications has encouraged some grandiose visions. In an emphatic piece titled "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete", Chris Anderson, the editor in chief of Wired magazine, writes:

This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity.

Furthermore:

We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

Anderson proposes that instead of taking a scientific approach, we can just "throw the numbers" into a machine and through computational alchemy transform data into knowledge. (Similar thinking shows up in commonplace references to "crunching" numbers, a metaphor I have previously criticized.) The suggestion that we should "forget" the theory developed by experts in the relevant field seems particularly unwise. Theory and expert opinion are always imperfect, but that doesn't mean they should be casually discarded.

Anderson's faith in big data and blind computing power can be challenged on several grounds. Take selection bias, which can play havoc with predictions. As an example, consider the political poll conducted by The Literary Digest magazine, just before the 1936 presidential election. The magazine sent out 10 million postcard questionnaires to its subscribers, and received about 2.3 million back. In 1936, that was big data. The results clearly pointed to a victory by the republican challenger, Alf Landon. In fact, Franklin Delano Roosevelt won by a landslide. The likely explanation for this colossal failure: for one thing, subscribers to The Literary Digest were not representative of the voting population of the United States; for another, the 23% who responded to the questionnaire were likely quite different from those who did not. This double dose of selection bias resulted in a very unreliable prediction. Today, national opinion polls typically survey between 500 and 3000 people, but those people are selected randomly and great efforts are expended to avoid bias. The moral of this story is that, contrary to the hype, bigger data is not necessarily better data. Carefully designed data collection can trump sheer volume of data. Of course it all depends on the situation.

Selection biases can also be induced during data analysis when cases with missing data are excluded, since the pattern of missingness often carries information. More generally, bias can creep into results in any number of ways, and extensive lists of biases have been compiled. One important source of bias is the well-known principle of Garbage In Garbage Out. Anderson refers to measurements taken with "unprecedented fidelity". It is true that in some areas, impressive technical improvements in certain measurement have been made, but data quality issues are much broader and are usually problematic. Data quality issues can never be ignored, and can sometimes completely derail an analysis.

Another limitation of Anderson's vision concerns the goals of data analysis. When the goal is prediction, it may be quite sufficient to algorithmically sift through correlations between variables. Notwithstanding the previously noted hazards of prediction, such an approach can be very effective. But data analysis is not always about prediction. Sometimes we wish to draw conclusions about the causes of phenomena. Such causal inference is best achieved through experimentation, but here a problem arises: big data is mostly observational. Anderson tries to sidestep this by claiming that with enough data "Correlation is enough":

Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

But on the contrary, investigations of cause and effect (mechanistic explanations) are central to both natural and social science. And in applied fields such as government policy, it is often of fundamental importance to understand the likely effect of interventions. Correlations alone don't answer such questions. Suppose, for example, there is a correlation between A and B. Does A affect B? Does B affect A? Does some third factor C affect both A and B? This last situation is known as confounding (for a good introduction, see this article [pdf]). A classic example concerns a positive correlation between the number of drownings each month and ice cream sales. Of course this is not a causal relationship. The confounding factor here is the season: during the warmer periods of the year when people consume more ice cream, there are far more water activities and hence drownings. When a confounding factor is not taken into account, estimates of the effect of one factor on another may be biased. Worse, this bias does not go away as the quantity of data increases—big data can't help us here. Finally, confounding cannot be handled automatically; expert input is indispensable in any kind of causal analysis. We can't do without theory.

Big data affords many new possibilities. But just being big does not eliminate the problems that have plagued the analysis of much smaller data sets. Appropriate use of data still requires careful thought—about both the content area of interest and the best tool for the job.

Thinking about Data

It is also useful to think more broadly about the concept of data. Let's start with an examination of the word data itself, to see what baggage it carries.

We are inconsistent in how we talk about data. The words data and information are often used synonymously (think of "data processing" and "information processing"). Notions of an information hierarchy have been around for a long time. One model goes by the acronym DIKW, representing an ordered progression from Data to Information to Knowledge and eventually Wisdom. Ultimately, these are epistemological questions, and easy answers are illusory.

Nevertheless, if what we mean by data is the kind of thing stored on a memory stick, then data can be meaningless noise, the draft of a novel, a pop song, the genome of a virus, a blurry photo taken by a cellphone, or a business's sales records. Each of these types of information and an endless variety of others can be stored in digital memory: on one level all data are equivalent. Indeed the mathematical field of information theory sets aside the meaning or content of data, and focuses entirely on questions about encoding and communicating information. In the same spirit, Chris Anderson argues that we need "to view data mathematically first and establish a context for it later."

But when we consider the use of data, it makes no sense to think of all data as equivalent. The complete lyrics of all of the songs by the Beatles is not the same as a CT scan. Data are of use to us when they are "about" something. In philosophy this is the concept of intentionality, which is an aspect of consciousness. By themselves, the data on my memory stick have no meaning. A human consciousness must engage with the data for them to be meaningful. When this takes place, a complex web of contextual elements come into play. Depending on who is reading them, the Beatles' lyrics may call to mind the music, the cultural references, the history of rock and roll, and diverse personal associations. A radiologist who examines a CT scan will recognize various anatomical features and perhaps concerning signs of pathology. Judgements of quality may also arise, whether in mistranscribed lyrics or a poorly performed CT scan.

The word data is the plural of the Latin word datum, meaning "something given". So the data are the "givens" in a problem. But in many cases, it might be helpful to think of data as taken rather than given. For example, when you take a photograph, you have a purpose in mind, you actively choose a scene, include some features and exclude others, adjust the settings of the camera. The quality of the resulting image depends on how steady your hand is, how knowledgeable you are of the principles of photography. Even when a photograph is literally given to you by someone else, it was still taken by somebody. The camera never lies, but the photograph may be misunderstood or misrepresented.

When a gift is given to you, it is easy to default to the passive role of recipient. The details of how the gift was selected and acquired may be entirely unknown to you. A dealer in fine art would carefully investigate a newly acquired work to determine its provenance and authenticity. Similarly, when you receive data from an outside source, it is important to take an active role. At the very least, you should ask questions. Chris Anderson claims that "With enough data, the numbers speak for themselves." But on their own, the numbers never speak for themselves, any more than a painting stolen during WWII will whisper the secret of its rightful ownership. One common source of received data today is administrative data, that is, data collected as part of an organization's routine operations. Rather than taking such data at face value, it is important to investigate the underlying processes and context.

It is also possible to make use of received data to design a study. For example, to investigate the effect of a certain exposure, cases of a rare outcome may be selected from a data set and matched with controls, that is individuals who are similar except that they did not experience that outcome. (This is a matched case-control study.) Appropriate care must be taken in how the cases and controls are selected, and in ensuring that any selection effects in the original database do not translate into bias in the analysis. Tools for the valid and efficient analysis of such observational studies have been investigated by epidemiologists and statisticians for over 50 years.

When we collect the data ourselves, we have an opportunity to take an active role from the start. In an experiment, we manipulate independent variables and measure the resulting values of dependent variables. Careful experimental design lets us accurately and efficiently obtain results. In many cases, however, true experiments are not possible. Instead, observational studies, where there is no manipulation of independent variables, are used. Numerous designs for observational studies exist, including case-control (as mentioned above), cohort, and cross-sectional. Again, careful design is vital to avoid bias, and to efficiently obtain results.

Conclusion

Excitement over a new development—be it a discovery, a trend, or a way of thinking—can sometimes spill over, like popcorn jumping from a popper. This may give rise to related, but nevertheless distinct ideas. In the heat of the excitement (and not infrequently a good deal of hype), it's important to evaluate the quality of the ideas. Exaggerated claims may not be hard to identify, but they are also frequently pardoned as merely an excess of enthusiasm.

Still, the underlying bad idea may, in subtler form, gradually gain acceptance. The costs may only be appreciated much later. Today it is easy to see how damaging ideas like social Darwinism—the malignant offspring of a very good idea—proved to be. But at the time, it may have seemed like a plausible extrapolation from a brilliant new theory.

The role of data in our societies and our own lives is becoming increasingly central. We live in a world where the quantity of data is exploding and truly gargantuan data sets are being generated and analyzed. But it is important that we not become hypnotized by their immensity. It is all too easy to see data as somehow magical, and to imagine that big data combined with computational brute force will overcome all obstacles.

Let's enjoy the popcorn—but turn down the heat a little.

_____________________________
1. ^In this post, I won't worry too much about whether to treat data as singular or plural. It strikes me as a little bit like the question of whether to talk about bacteria or a bacterium. While the distinction is sometimes important, people can get awfully hung up on it, with little benefit.
2. ^ See this interesting history of data science.

Monday, 27 February 2012

Western Brands on Weibo

The purpose of this article is to list some of the Western Companies/Brands that are using China's Sina Wēibó 新浪微博. The text in the square brackets is the Sina Wēibó 新浪微博 name. This article is a continuation of schappo.blogspot.co.uk/2011/08/companies-on-sina.html

7 For All Mankind [@7ForAllMankind] weibo.com/7forallmankind ☆
Abercrombie & Fitch [@Abercrombie] weibo.com/abercrombieny ☆
Accenture [@埃森哲中国] weibo.com/accenture ☆
Accor Hotels [@雅高酒店AccorHotels] weibo.com/accorchina ☆
Air Liquide [@液空中国] weibo.com/airliquidechina ☆
AKG [@雅登-AKG中国] weibo.com/akgchina ☆
AkzoNobel [@阿克苏诺贝尔中国] weibo.com/akzonobelinchina ☆
Alberta Ferretti [@AlbertaFerretti] weibo.com/albertaferretti ☆
ALDO [@ALDO1972] weibo.com/n/ALDO1972 ☆
Alexander McQueen [@Alexander-McQueen] weibo.com/alexandermcqueen ☆
Allen Edmonds [@AllenEdmonds中国] weibo.com/allenedmondschina ☆
Allianz Insurance [@安联保险-Allianz] weibo.com/allianzone ☆
Alpenliebe [@微有爱] weibo.com/alpenliebekindness ☆
American Express [@美国运通中国官方微博] weibo.com/amexchina ☆
Anya Hindmarch [@Anya_Hindmarch_Official] weibo.com/anyahindmarchlondon
Argos [@Argos爱顾商城] weibo.com/2720491021 ☆
ASOS [@ASOS] weibo.com/asosofficial
Aspinal of London [@Aspinal-of-London] weibo.com/aspinaloflondonltd
Associated Press [@美联社] weibo.com/apimages ☆
Aston Martin [@阿斯顿马丁拉共达] weibo.com/astonmartinlagondacn ☆
Aston Villa FC [@阿斯顿维拉足球俱乐部] weibo.com/AVFCOfficial ☆
AVIS [@AVIS安飞士租车] weibo.com/avischina ☆
Balenciaga [@Balenciaga] weibo.com/officialbalenciaga ☆
Balmain [@瑞士宝曼手表] weibo.com/balmainwatches ☆
Barbie [@Barbie芭比官方微博] weibo.com/barbieofficial ☆
BASF [@巴斯夫大中华] weibo.com/basfinchina ☆
Bayer [@拜耳中国官方微博] weibo.com/bayerchina ☆
Bentley Motors [@宾利BentleyMotors] weibo.com/bentleymotorsuk ☆
Bergdorf Goodman [@Bergdorfs] weibo.com/bergdorfs ☆
Best Buy [@BestBuy百思买] weibo.com/bestbuycn ☆
Bloomingdaleʼs [Bloomingdales_USA] weibo.com/bloomingdalesusa
Blue Nile Inc [@BlueNileInc] weibo.com/bluenileinc ☆
Bobbi Brown [@BobbiBrownChina] weibo.com/bobbibrownchina ☆
Bonpoint [@Bonpoint-中国] weibo.com/bonpoint ☆
Bosch [@博世中国] weibo.com/boschauto ☆
Boucheron [@Boucheron宝诗龙微博] weibo.com/boucheronparis ☆
Breitling [@百年灵BREITLING] weibo.com/breitlingchina ☆
Bremont [@Bremont宝名表] weibo.com/bremont ☆
British Airways [@英国航空] weibo.com/britishairways ☆
Brompton Bicycle [@Brompton_bicycle_伯龙腾] weibo.com/bromptonbicycle ☆
BVLGARI [@BVLGARI宝格丽] weibo.com/bulgari ☆
BVLGARI Perfume [@宝格丽香水] weibo.com/bulgariperfume ☆
Cambridge Satchel Co. [@The_Cambridge_Satchel_Company] weibo.com/jianqiaobao ☆
Campo Marzio Design [@CampoMarzio中国区] weibo.com/campomarzio ☆
Camus [@卡慕CAMUS] weibo.com/camuschina ☆
CARAT London [@CARAT官方微博] weibo.com/caratlondon
Caterpillar [@Caterpillar官方微博] weibo.com/caterpillarinchina ☆
Cath Kidston [@CathKidstonChina] weibo.com/cathkidstonchina ☆
Champagne Taittinger [@泰亭哲香槟] weibo.com/champagnetaittinger
Cheerios [@雀巢脆谷乐] weibo.com/nestlecheerios ☆
Chopard [@萧邦Chopard] weibo.com/chopardchina ☆
Christian Louboutin [@ChristianLouboutin官方微博] weibo.com/LouboutinWorld ☆
Christie's [@佳士得国际] weibo.com/christies ☆
Clarisonic [@Clarisonic科莱丽-欧莱雅] weibo.com/clarisonicchina .
Club Monaco [@Club_Monaco] weibo.com/clubmonaco ☆
CME Group [@CMEGroup] weibo.com/cmegroup ☆
Cows Creamery [@COWS冰激凌] weibo.com/cowscreamery
Decanter [@Decanter醇鉴] weibo.com/decantercn ☆
Ducati [@杜卡迪中国] weibo.com/ducatichina ☆
Dulux [@多乐士Lets_Colour] weibo.com/letscolor ☆
DuPont [@杜邦公司] weibo.com/dupont ☆
eBay [@eBay] weibo.com/ebay ☆
Elizabeth Arden [@伊丽莎白雅顿美丽沙龙] weibo.com/elizabetharden ☆
EMC Corporation [@EMC中国-云计算] weibo.com/emcgreatchina ☆
Eppendorf [@eppendorf官方微博] weibo.com/eppendorfchina ☆
Ernst & Young [@安永EY] weibo.com/eyernstyoung ☆
Etro [@ETRO艾绰] weibo.com/etrochina ☆
Eurostar [@欧洲之星_Eurostar] weibo.com/eurostarchina
Fairmont Hotels & Resorts [@费尔蒙酒店] weibo.com/fairmonthotels ☆
Fendi [@FENDI] weibo.com/fendi ☆
Financial Times [@FT中文网] weibo.com/ftchinese ☆
Finnair [@芬兰航空Finnair] weibo.com/finnaircom
Firefox [@火狐] weibo.com/firefox ☆
Fisher-Price [@费雪中国官方微博] weibo.com/fisherprice ☆
Fisherman's Friend [@渔夫之宝官方微博] weibo.com/ffgfwb ☆
Fissler [@德国菲仕乐] weibo.com/fisslerchina2013 ☆
Flipboard [@Flipboard] weibo.com/flipboard ☆
Freescale Semiconductor [@飞思卡尔] weibo.com/freescale ☆
Furla [@Furla_孚勒] weibo.com/furlaofficial ☆
G-Star RAW [@G-STARCHINA] weibo.com/gstarchina ☆
Geox [@健乐士GEOX] weibo.com/jianleshigeox ☆
Girard-Perregaux [@GP芝柏表] weibo.com/gpchina ☆
Glenmorangie [@格兰杰单一麦芽威士忌] weibo.com/glenmorangiechina ☆
GNC [@GNCLiveWell] weibo.com/gnclivewell ☆
GRAFF [@格拉夫GRAFF] weibo.com/graff ☆
Gregory Mountain Products [@Gregory官方微博] weibo.com/gregory1977 ☆
Grey Goose [@法国灰雁GreyGoose] weibo.com/greygoosechina ☆
Guinevere Launcelot [@Guinevere_Launcelot] weibo.com/gltlondon
Gymboree [@金宝贝国际早教微课堂] weibo.com/gymboree ☆
H2O+ [@H2O水芝澳官方微博] weibo.com/h2ochina ☆
Hackett London [@Hackett-London] weibo.com/hackettlondon ☆
Halma [@HALMA中国] weibo.com/halma ☆
Hardy Amies [@HardyAmies赫迪雅曼] weibo.com/HardyAmies
Harry Winston [@海瑞温斯顿HarryWinston] weibo.com/harrywinston ☆
Hasbro [@孩之宝中国] weibo.com/hasbrochina ☆
Holland & Barrett [@HollandAndBarrett] weibo.com/hollandandbarrett ☆
Hollister [@Hollister] weibo.com/hollister ☆
Hooters [@美国猫头鹰餐厅-中国] weibo.com/hooterschina ☆
Hublot [@宇舶表] weibo.com/hublothanhan ☆
Hyatt [@凯悦酒店集团HYATT] weibo.com/hyatthotelscorp ☆
IBM [@IBM中国] weibo.com/ibm100 ☆
IMAX [@IMAX] weibo.com/imax ☆
Irregular Choice [@IrregularChoice香港] weibo.com/irregularchoicehk ☆
IWC [@IWC万国表] weibo.com/iwcchina ☆
J.Lindeberg [@JLINDEBERG林德伯格] weibo.com/jlindeberg ☆
Jack Wolfskin [@JackWolfskin官方微博] weibo.com/jackwolfskingermany ☆
Jaeger-LeCoultre [@积家官方微博] weibo.com/jaegerlecoultrechina ☆
Jo Malone [@JoMaloneLondon祖玛珑] weibo.com/jomalonelondon
Juniper Networks [@瞻博网络] weibo.com/junipernetworks ☆
Kate Spade New York [@katespade官方微博] weibo.com/katespadeny ☆
Kipsta [@KIPSTA中国] http://weibo.com/kipstachina ☆
Kleenex [@舒洁kleenex] http://weibo.com/n/舒洁kleenex ☆
Lagostina [@拉歌蒂尼] weibo.com/lagostina ☆
Lana Marks [@LANA-MARKS-CHINA] weibo.com/lanamarks ☆
Lancaster [@兰嘉丝汀] weibo.com/lancasterchina ☆
Le Coq Sportif [@lecoqsportif中国] weibo.com/lecoqsportif ☆
Lindt [@Lindt瑞士莲巧克力] weibo.com/lindtchina ☆
Lonely Planet [@LonelyPlanet] weibo.com/lonelyplanet ☆
Luis Via Roma [@LUISAVIAROMA官方微博] weibo.com/luisaviaroma
MAC Cosmetics [@MAC魅可] weibo.com/maccosmetics ☆
Macy's [@美国梅西百货] weibo.com/MacysChina
Manchester City FC [@曼城足球俱乐部MCFC] weibo.com/mcfcofficial
Manchester United FC [@曼联足球俱乐部] weibo.com/manchesterunited ☆
Mango [@MANGO中国官网] weibo.com/mangofashion ☆
Marc Jacobs [@MarcJacobsIntl莫杰] weibo.com/marcjacobsintl ☆
Maria Luisa [@MARIA_LUISA] weibo.com/marialuisa ☆
Marimekko [@MARIMEKKO_玛莉美歌] weibo.com/marimekkoofficial ☆
Marmot [@Marmot中国] weibo.com/marmot001 ☆
Marni [@MARNI] weibo.com/officialmarni ☆
Marvin Watches [@Marvin-瑞士摩纹表] weibo.com/marvinwatch ☆
MasterCard [@万事达人] weibo.com/mastercardchina ☆
Maxi-Cosi [@Maxi-Cosi] weibo.com/maxicosi ☆
McLaren [@迈凯伦汽车] weibo.com/mclarenchina ☆
Media Markt [@万得城电器] weibo.com/mediamarktchina ☆
Medtronic [@美敦力中国] weibo.com/medtronicchina ☆
Meltwater Group [@Meltwater] weibo.com/meltwater ☆
Mettler Toledo [@梅特勒-托利多中国] weibo.com/mettlertoledo ☆
Michael Kors [@Michael-Kors] weibo.com/michaelkors ☆
Monster Cable [@Monster-魔声中国] weibo.com/monsterchina ☆
Mothercare [@mothercare官方微博] weibo.com/mothercarechina ☆
Movado [@摩凡陀Movado] weibo.com/movado ☆
MTV [@MTV中文频道] weibo.com/mtvchina
Mulberry [@Mulberry_Official] weibo.com/mulberryofficial ☆
NASDAQ OMX [@纳斯达克交易所] weibo.com/nasdaqomx
Neiman Marcus [@NeimanMarcus尼曼] weibo.com/neimanmarcuschina ☆
NERF [@孩之宝NERF-热火] weibo.com/ilovenerf ☆
New Balance [@新百伦newbalance] weibo.com/newbalanceofficial ☆
New York Times [@纽约时报中文网] weibo.com/nytchinese ☆
Nuxe Paris [@Nuxe欧树] weibo.com/nuxe ☆
Old Navy [@OldNavyChina] weibo.com/oldnavychina
Ovaltine [@阿华田Ovaltine] weibo.com/ovaltine001 ☆
Oxford University Press [@牛津大学出版社全球学术出版] weibo.com/oupacademic ☆
Pandora [@PANDORA珠宝] weibo.com/pandorajewellery ☆
Papa John's Pizza [@棒约翰PapaJohns] weibo.com/papachina
Paul Smith [@PaulSmith保罗史密斯] weibo.com/paulsmithofficial ☆
Paula's Choice [@PaulasChoice宝拉珍选] weibo.com/paulaschoice01 ☆
PayPal [@PayPal_China] weibo.com/paypalmarketing ☆
Penguin Books [@企鹅出版社] weibo.com/penguinbooks ☆
Perficient [@博克软件] weibo.com/perficientchina ☆
Peugeot Scooters [@标致摩托] weibo.com/peugeotscooters ☆
Pfizer [@辉瑞中国] weibo.com/pfizerchina
Piaget [@PIAGET] weibo.com/piaget ☆
Piaggio [@比亚乔机车] weibo.com/piaggio1884 ☆
Pineider [@彼耐德Pineider] weibo.com/pineider ☆
Pizza Hut [@必胜客欢乐餐厅] weibo.com/pizzahut ☆
Pomellato [@Pomellato宝曼兰朵] weibo.com/pomellatoinchina ☆
Pony [@ponychina] weibo.com/ponychina ☆
Printemps [@春天百货Printemps] weibo.com/printempsparis ☆
Pull-in [@PULLIN内衣] weibo.com/pullinasia ☆
Razorfish [@RazorfishChina] weibo.com/razorfish
Ritz-Carlton [@丽思卡尔顿酒店] weibo.com/ritzcarlton ☆
Rockport [@ROCKPORT美国乐步] weibo.com/rockportchina ☆
Roger Dubuis [@罗杰杜彼RogerDubuis] weibo.com/rogerdubuis ☆
Roger Vivier [@RogerVivier_罗杰维维亚] weibo.com/rogervivier ☆
Rovio Entertainment [@Rovio娱乐] weibo.com/rovioentertainment
Rupert Sanderson [@RupertSanderson] weibo.com/rupertsanderson ☆
Schneider Electric [@施耐德电气中国] weibo.com/schneidercn ☆
SELECTED [@SELECTED中国官方微博] weibo.com/selectedchina
Selfridges [@Selfridges] weibo.com/selfridgesuk .
Sergio Rossi [@sergio_rossi] weibo.com/sergiorossi ☆
Shell [@壳牌中国集团] weibo.com/shellinchina
Sheraton Hotels & Resorts [@喜来登酒店及度假村Sheraton] weibo.com/sheratonhotels ☆
Shopbop [@shopbop] weibo.com/shopbopchina ☆
Sigma-Aldrich [@SigmaAldrich] weibo.com/sigmaaldrich ☆
Skechers [@SKECHERS斯凯奇] weibo.com/skechers ☆
Skyscanner [@Skyscanner天巡] weibo.com/skyscannertx ☆
South Coast Plaza [@SouthCoastPlaza] weibo.com/southcoastplaza
Standard Chartered Bank [@渣打银行中国] weibo.com/scbmainlandchina ☆
Stickhouse [@Stickhouse] weibo.com/stickhouse
Stiebel Eltron [@斯宝亚创StiebelEltron] weibo.com/stiebeleltron ☆
Stroili Oro [@StroiliOro] weibo.com/stroilioro ☆
TAG Heuer [@豪雅TAGHeuer] weibo.com/tagheuerchina ☆
Ted Baker [@TedBakerLondon] weibo.com/tedbakerlondon ☆
Tesco [@乐购中国官方微博] weibo.com/TESCOofficial ☆
The Glenlivet [@格兰威特威士忌] weibo.com/theglenlivet ☆
Thermo Fisher Scientific [@赛默飞] weibo.com/thermofishercn ☆
Times Higher Education [@泰晤士报高等教育期刊] weibo.com/timeshighereducation ☆
TLD Registry [@域通联达] weibo.com/tldregistry ☆
Toblerone [@瑞士三角巧克力] weibo.com/toblerone ☆
Tom & Jerry [@华纳兄弟-猫和老鼠] weibo.com/tomandjerryoffical ☆
Topshop Shēnzhèn [@TOPSHOP深圳] weibo.com/topshopsz ☆
Tottenham Hotspur [@热刺TottenhamHotspur] weibo.com/tottenhamhotspur ☆
Truefitt & Hill [@TRUEFITT-HILL-CHINA] weibo.com/truefittandhill ☆
Unisys [@优利中国] weibo.com/unisyschina
Valentino [@Valentino官方微博] weibo.com/valentinoofficial ☆
Van Cleef & Arples [@VanCleefArpels梵克雅宝] weibo.com/vancleefarpelschina ☆
Vichy Laboratoires [@薇姿医生] weibo.com/vichybrand ☆
Visa [@Visa中国] weibo.com/visachina ☆
VMware [@VMware中国] weibo.com/vmware ☆
Volvo [@沃尔沃集团中国] weibo.com/volvogroupchina ☆
Wall Street Journal [@华尔街日报中文网] weibo.com/chinesewsj
Wallpaper* Magazine [@WallpaperMagazine] weibo.com/wallpapermag ☆
Walmart [@沃尔玛中国官方微博] weibo.com/wmcsr
West Bromwich Albion [@西布朗足球俱乐部官微] weibo.com/westbrom
Westin Hotels & Resorts [@Westin] weibo.com/westinhotels ☆
Wiggle [@Wiggle中国] weibo.com/wigglechina .
William & Son [@WilliamandSon] weibo.com/williamandson
Wolfram [WolframChina] weibo.com/wolframchina
YOOX [@YOOX网络概念店] weibo.com/yooxcn ☆
Yves Rocher [@Yves-Rocher伊夫黎雪] weibo.com/yvesrocher1959 ☆
Zatchels [@Zatchels] weibo.com/zatchelsuk ☆
Zenith [@ZENITH真力时] weibo.com/zenithchina ☆

Monday, 20 February 2012

Language Characteristics

In this article I list some of the characteristics of natural languages and scripts as they are manifested and used in modern day IT. With languages there are always exceptions and so there will be some exceptions to these characteristics. I will not be delving into linguistic technicalities such as the distinction between mora and syllable or the distinction between logogram and ideogram. I will take a more broad brush approach.

Arabic

Arabic is written in the Arabic script
Written from right to left
The space character (U+0020 SPACE) is used as a separator between words and sentences
The sentence terminator full stop is the Unicode character U+002E FULL STOP
Unicase ie no uppercase and lowercase letter forms
A Keyboard Mapping is sufficient in order to write Arabic
The Arabic script is inherently cursive and hence is presented/displayed in it's cursive form.
Letters change shape according to their position within a word. These different shapes are named Initial, Medial, Final and Isolated forms. en.wikipedia.org/wiki/Arabic_alphabet#Letter_forms

Chinese

Chinese is written in the Chinese script which consists of hànzì (汉字) characters, of which, there are tens of thousands
Written from left to right. Once browsers implement CSS3 Writing Modes we may well see some return to the traditional vertical text in webpages dev.w3.org/csswg/css-writing-modes/#vertical-intro
There is no space character separator between words and sentences
The sentence terminator full stop is the Unicode character U+3002 IDEOGRAPHIC FULL STOP
Unicase ie no uppercase and lowercase letter forms
An Input Method is required in order to write Chinese
All characters, including punctuation, are monospaced. Thus, for example, the list items separator in the text string "北京，南京，东京" is the single character U+FF0C FULLWIDTH COMMA. The text string "北京、南京、东京" uses the single character U+3001 IDEOGRAPHIC COMMA as the list items separator.
With respect to number of characters required to communicate, Chinese is much more compact than English. Given a sentence written in English, the same sentence written in Chinese would require far fewer characters. This compactness gives Chinese a significant advantage over English for IDNs and when microblogging.

English

English is written in the Latin script
Written from left to right
The space character (U+0020 SPACE) is used as a separator between words and sentences
The sentence terminator full stop is the Unicode character U+002E FULL STOP
Has uppercase and lowercase letter forms
A Keyboard Mapping is sufficient in order to write English

Japanese

Japanese is written in the Japanese scripts Kanji (漢字), Hiragana (ひらがな) and Katakana (カタカナ)
Written from left to right. Once browsers implement CSS3 Writing Modes we may well see some return to the traditional vertical text in webpages dev.w3.org/csswg/css-writing-modes/#vertical-intro
There is no space character separator between words and sentences
The sentence terminator full stop is the Unicode character U+3002 IDEOGRAPHIC FULL STOP
Unicase ie no uppercase and lowercase letter forms. Uppercase is sometimes used for emphasis in English. Similarly, Katakana is sometimes used for emphasis.
An Input Method is required in order to write Japanese
In general, Japanese, like Chinese is monospaced. The exception is that there are half-width forms of Katakana and some punctuation characters. The half-width forms are in Unicode block Half-width and Full-width Forms U+FF00 ➤ U+FFEF.
With respect to number of characters required to communicate, Japanese is much more compact than English. Given a sentence written in English, the same sentence written in Japanese would require far fewer characters. This compactness gives Japanese a significant advantage over English for IDNs and when microblogging.

Korean

Korean is written in the Hangeul (한글) script
Written from left to right
The space character (U+0020 SPACE) is used as a separator between words and sentences
The sentence terminator full stop is the Unicode character U+002E FULL STOP
Unicase ie no uppercase and lowercase letter forms
An Input Method is required in order to write Korean
The individual Korean letters (jamo/자모) are grouped into and displayed as Syllabic blocks. e.g. the individual jamo ㅎ ㅏ ㄴ ㄱ ㅜ ㄱ are combined to form the two Korean characters 한국

Russian

Russian is written in the Cyrillic (Кириллица) script
Written from left to right
The space character (U+0020 SPACE) is used as a separator between words and sentences
The sentence terminator full stop is the Unicode character U+002E FULL STOP
Has uppercase and lowercase letter forms
A Keyboard Mapping is sufficient in order to write Russian

Saturday, 31 December 2011

Browser Language

By Browser Language I do not mean the Browser User Interface Language. I am referring to the Browser Preferred Language for displaying Pages. I will use the acronym BL to mean Browser preferred Language for displaying pages.

An internationalised website will have pages in multiple languages. These pages can be displayed according to BL eg If BL is Korean then the website will send it's Korean pages to the browser.

With most browsers, the BL can be set in the preferences and can be set independent of the language settings of the OS. Some browsers do inherit their BL from the OS language setting.

This ability to change the BL has so much potential that few are aware of. Google are switched on to this potential. Google maps, if embedded correctly, will automatically adapt to BL. You can try it out for yourself. Visit lboro.ac.uk/about/findus.html and you will see a Google map of Loughborough. Now change the BL in your browser preferences and refresh. You will see menus displayed in the BL you chose. If you had chosen Japanese as your BL you would also see some place names transliterated into Japanese.

This ability for the user to change BL is a good thing, a very good thing. But... This function is buried down in the preferences. It is my experience that few people are aware that the BL can be changed and even less are aware of the possibilities this opens up.

My recommendation to all the browser manufacturers is that the BL preferences should be made manifest by bringing them up front. Put a BL graphic in a prominent position on the browser window so that it is always visible. This BL graphic will serve to inform the user of the current BL and allow the user to change the BL (eg a popup BL selection menu).

Such a BL graphic will:

Raise user awareness of BL and the ability to change BL
Encourage users to explore sites that adapt according to BL
Encourage web developers to incorporate content, widgets and features that are BL adaptive

Here is an illustrative story. About a year ago, a Chinese person told me he had a problem when viewing some Google maps. His problem was that the map info was displayed in Chinese but he wanted to see the info displayed in English. This problem occurred when he viewed these maps from his own computer and he could not work out how to view the info in English.

I explained to him that what he was experiencing was not a problem but rather a symptom of a very powerful feature. The feature being Google maps auto adapting to BL. His Computer had a Chinese OS and the browser he used had it's BL set to Chinese. I told him how to change his browser's BL and then, of course, he could view the Google map info in any of the many supported languages.

A manifest BL graphic would have made it obvious what was happening and would have enabled him to explore and appreciate Google maps BL adaption and BL adaptive websites in general.

Friday, 30 December 2011

Microblog Space (character) Saving

Take the following example two lines of text:

Fruit: apples, pears! Veg: cabbage, onions. Cars? Ford; Mini
Fruit：apples，pears！Veg：cabbage，onions．Cars？Ford；Mini

Line 1 has 60 characters and line 2 has 52 characters. I reduced the number of characters by using full-width punctuation characters. These full-width forms start at Unicode codepoint U+FF01. Using full-width forms I can dispense with the space character. So, for example, instead of colon + space I use full-width colon only. For microblog posts small space savings like this can be very useful.

I could, and sometimes do, use OSX Character Viewer to obtain these full-width punctuation characters. When I am microblogging in English I toggle to the Simplified Pinyin Input Method to write the full-width punctuation. This may sound complicated and time consuming but with a little practice it is easy and quick and does not disrupt the flow of writing. There is one exception character. When I type full stop whilst in the OSX Simplified Pinyin Input Method the ideographic full stop is produced rather than the full-width full stop. That is what I would expect and still saves me a space character. So, when I am microblogging I would write the example text, above, as:

Fruit：apples，pears！Veg：cabbage，onions。Cars？Ford；Mini

Sometimes I use my OSX Japanese Input Method for microblog punctuation. The one difference is that when typing comma the ideographic comma is produced. i.e.

Fruit：apples、pears！Veg：cabbage、onions。Cars？Ford；Mini

English is my primary language and, as such, my examples are in English. The same space saving technique can be used for any language/script that uses the standard ASCII punctuation e.g. Korean & Russian

My setup is OSX Lion with the standard Input Methods. I have setup a keyboard shortcut (Control + Space) to quickly toggle between a pair of languages. When microblogging my toggle pairs are English & Chinese or English & Japanese. Your setup may well be different to mine and you may well get different results to me. Whatever your setup, you will be able to use the Chinese or Japanese space saving punctuation characters.

Tuesday, 6 December 2011

ZARA Website

Interesting! And the sort of thing I would do. ZARA's website adapts the Social Media links displayed according to the Region. ZARA caters for multiple regions and for the majority displays a link to their Facebook facebook.com/Zara

There are some regions for which it displays a link to that region's Social Media:

China 中国: Zara's China landing page is zara.com/webapp/wcs/stores/servlet/home/cn/zh/zara-I2011 and the displayed Social Media link is Sina Wēibó 新浪微博 weibo.com/zarachina

Russia Россия: Zara's Russia landing page is zara.com/webapp/wcs/stores/servlet/home/ru/ru/zara-I2011 and the displayed Social Media link is Vkontakte ВКонтакте vk.com/zara

With their Japan landing page it appears that the intention is to link to a Zara twitter as indicated by the displayed text on zara.com/webapp/wcs/stores/servlet/home/jp/ja/zara-jp-W2011 The link does not though resolve to a Zara Twitter or indeed any Twitter.