Today, Google has added functionality for nine new languages to Analytics.

The new languages are: Arabic, Croatian, Hebrew, Hindi, Latvian, Romanian, Serbian, Slovenian, and Ukrainian.

We are confident this will improve the usage of Google Analytics across the world, and help website owners and AdWords advertisers get even more out of their internet marketing efforts. Happy data mining!

These nine new languages bring the total to 40 total languages available to Google Analytics users – “quite the polygot,” according to Google.

If you wish to change the language for Google Analytics, you must be using the new version. Make sure you’re logged in to your Google account, then click on the settings button at the top right.

Google Analytics has also updated the help center to support these nine new languages.

The new languages are Arabic, Farsi, Hebrew, and Urdu and users can head on over to the Translation Center and start working today.

The Translation Center, launched in 2011, is a giant crowdsourcing program that allows users from dozens of languages to help translate Twitter (the product, not the actual tweets). Twitter says that over 425,000 people have helped with translations. Because of their work, Twitter is available in 22 languages – a number which is about to jump to 26.

Twitter had this to say in a blog post:

As we prepare to add Arabic, Farsi, Hebrew and Urdu to Twitter, we’ve developed new ways to ensure that Tweets and hashtags will work properly in right-to-left languages. We’ve also made changes behind the scenes to give right-to-left language speakers a localized user experience. As soon as our volunteers have completed their translation work, we’ll make Arabic, Farsi, Hebrew and Urdu available for everyone on Twitter.com later this spring.

Translation Center is now open to #Arabic, #Farsi, #Hebrew, #Urdu. Please help us translate Twitter to these languages! twitter.com/translator/sta…

— Translator(@translator) January 25, 2012

The most recent language addition to Twitter was Swedish, which became available earlier this month.

[Image Courtesy Dave Parker, Flickr]

On Monday, Wikipedia chief Jimmy Wales tweeted:

And the people maintaining the German-language Wikipedia page made good on the promise:

The German Wikipedia wasn’t by any means alone, though. Other large language Wikipedias – those featured around the main logo on Wikipedia’s homepage – also demonstrated their support:

Wikipedia Spanish.

Wikipedia Polish.

Wikipedia Chinese (the second image is the translated banner since it was less obvious).

Wikipedia Portuguese.

Wikipedia Russian.

Wikipedia Japanese.

Wikipedia Italian anted up big time on their support and actually threw in a complete splash page denouncing SOPA (you can still access the site once you click through it).

Okay, so with the English page completely shut down, that’s all ten languages featured on Wikipedia’s homepage and ten pages standing in solidarity with… er. Wait. That’s only nine. I only counted nine. Who are we missing here? Let’s check the list.

…..France.

Sigh. Uh, hey France? Nous devons parler.

Twitter says over 400,000 volunteers have contributed to its translation center. This was launched last February as a means of crowdsourcing translations from Twitter users. Twitter has actually been crowdsourcing translations since 2009, but the center introduced a new system for doing so, relying on feedback from translators to make it better.

Other recently launched languages include: Danish, Finnish, Norwegian and Polish. You can see the entire list here:

“As the number of volunteer translators grows, we’ll explore new ways to empower users to translate and localize Twitter for even more communities,” Twitter says in a blog post. “Expect to see further improvements to the Translation Center in the coming year, including new translation tools and more opportunities to help make Twitter accessible to every person on the planet.”

Twitter has an official Translator account that you can follow to keep abreast of further updates regarding the center and Twitter translation in general.

If you follow the types of people that I follow, your response might be “Duh.” Cynicism, anger, and even depression are the lifeblood of Twitter, right? It’s what makes the Twitterverse turn. But there has to be enough expression of joy and jubilation to counteract all of those negative Nancies, right?

Apparently, no. Our Twitter activity shows that we are all unhappier than we were just a few years ago. But let’s back up for a second. How did anyone possibly research this?

Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years.

That’s pulled from the abstract of a University of Vermont study that was published this month in PloSONE. The team analyzed tweets for three years, combing through more than 46 billion individual words typed by over 63 million Twitter users around the world.

The 10,000 or so most common words spoken on Twitter were painstakingly rated by volunteers based on their level of “happiness” on a scale of one to nine. The researchers called these ratings the “emotional temperature,” and said that “like billions of moving atoms add up to the overall temperature of a room, billions of words used to express what people are feeling resolve into a view of the relative mood of large groups.”

For instance, the word “laughter” received a pretty high score of 8.5. A word like “truck” is neutral and got a score of 5.48. Negative words like “greed” received lower scores (3.06).

Because many tweets have location data and all of them have timestamps, the researchers were able to analyze patterns in “happiness” across the past three years.

According to the analyzed data, the happiest dates were Christmas Day 2008, 2009, and 2010. That was followed closely by Christmas Eve. Fourth of July (in America), Easter, and Thanksgiving were also relatively happy days. The only non-annual event that was a truly positive day outlier was the royal wedding in April of 2011.

Some of the most “unhappy” instances according to Twitter revolved around the U.S. bank bailouts, the Chilean miner incident, and the Japanese earthquake and subsequent tsunami.

But you can see the general decline. We are a sad, sad bunch.

Of course, this study is just taking into account Twitter users – so it could be that folks who use Twitter are getting down in the dumps. But Twitter is a fairly universal service nowadays. What do you think? Can the world’s happiness level be quantified using social media posts?

[I appreciate the level of detail given to the research. This is a gem:
One arguably false finding of a cultural event being negative was the finale of the last season of the highly rated television show ‘Lost’, marked by a drop in our time series on May 24, 2010, and in part due to the word ‘lost’ having a low happiness score of = 2.76, but also to an overall increase in negative words on that date.]

[Via PsychCentral]

Multiregional websites using substantially the same content. Example: English webpages for Australia, Canada and USA, differing only in price

Multiregional websites using fully translated content, or substantially different monolingual content targeting different regions. Example: a product webpage in German, English and French

The support is an expansion of the rel=”alternate” hreflang link element. Google says it has expanded it to handle content that is translated or provided for multiple geogrhapic regions.

“The hreflang attribute can specify the language, optionally the country, and URLs of equivalent content,” the company explains on the Webmaster Central blog. “By specifying these alternate URLs, our goal is to be able to consolidate signals for these pages, and to serve the appropriate URL to users in search. Alternative URLs can be on the same site or on another domain.”

You can always use re=”canonical” for pages that have the same content in the same language that are aimed at different countries.

“We’ll use that signal to focus on that version in search, while showing the local URLs to users where appropriate,” the company says. “For example, you could use this if you have the same product page in German, but want to target it separately to users searching on the Google properties for Germany, Austria, and Switzerland.”

Google looks at some examples of when to use the new markup here. They also have a help center article about the markup here.

Duolingo is the brainchild of Carnegie Mellon professor Luis von Ahn, and it first jumped on everyone’s radar earlier this year. Back then he told TechCrunch:

The solution was to transform language translation into something that millions of people WANT to do, and that helps with the problem of lack of bilinguals: language education,” von Ahn writes. It is estimated that there are over 1 billion people learning a foreign language. So, the site that we’ve been working on, Duolingo, will be a 100% free language learning site in which people learn by helping to translate the Web. That is, they learn by doing.

And today, it looks like the program is in its early stages of actualization. Duolingo has announced that today, November 30th, marks the start of the private beta period:

Having not yet been able to play with Duolingo, it’s easy to explain how it works because it’s such a beautiful concept. Once you choose the foreign language that you wish to learn (Spanish or German, with French, Italian and Chinese on the way), Duolingo gives you foreign language phrases from actual pages around the web. It only gives you phrases that match up with your level – like beginner, intermediate and advanced.

You then go to work translating those phrases. Duolingo helps you out with suggestions if you get stuck on certain words and everyone’s translations are submitted to be voted on so that the community can choose the best translations. The experience also includes some sort of language tutorials for words that you had trouble with.

In theory, Duolingo could help people learn another language while doing something more important – translating the far reaches of the internet. It’s basically crowdsourced translation as a game. Pretty neat, huh?

You can visit the Duolingo site to register for a private beta invite via email. I know I’m anxiously awaiting my chance to give it a try, although I know I must be patient -

Check out the video they just dropped –

If you’re unfamiliar with rage comics, think of them as cartoons using an ever-growing set of internet memes. Various faces and other crudely-drawn representations are used to express certain feelings – anger, shock, defeat, surprise, pleasure, success, horror. Initially, a rage comic was based around a certain rage character – the f7u12 guy (or fffffffuuuuuuuuuuuu guy). Something would happen, and rage guy would be very upset by it. Nowadays, “rage comic” encompasses any comic made with a series of these drawings, no matter if it includes rage guy or not.

Want a look into the world of rage comics? Check out the subreddit /r/fffffffuuuuuuuuuuuu, the biggest collection of rage comics on the internet. You might want to browse the face database, to figure out what they all mean.

The rage comic has a plethora of uses. Seriously. There is no emotion – no situation great or insignificant that cannot be expressed with a thoughtfully constructed rage comic.

And one teacher has decided to use them in his classroom.

Scott Stillar teaches English at the University of Tsukuba in Japan. He thinks that rage comics are a great way to teach the English language.

“Rage comics are special because at their core they consist of well known faces or expressions,” Stillar told the Daily Dot, “which are meant to show universal emotions of varying degrees under a wide variety of circumstances.”

He created his own subreddit for these educational rage comics, /r/EFLcomics, which has grown to over 6,600 subscribers.

The comics vary in their grasp of the English language, as well as their proper use of the specific rage faces.

The first is from reddit user 11ru109, a Japanese student:

And this from student 11rd113:

How about this meditation on failing to save your game/work:

“It allows the students to express themselves creatively in a meaningful and enjoyable way in their second language. The feedback in the form of ‘up votes’ or comments they receive from the online community where they are posted also adds an interactive element to the assignment,” Stillar told the Daily Dot.

What do you think? Can rage comics be a successful tool in teaching the English language? Let us know in the comments.

The device comes ready to work with 5 different types of language – American English, Australian English, UK English, French and German. Some English speakers with are finding that Siri is having a hard time understanding what they are saying – and it’s all because of their accent.

Personally, Siri has been working almost perfectly for me since I unwrapped my new 4S last weekend. But that’s most likely because I tend to have the vocal eccentricities of a newscaster from Topeka, Kansas. If Siri was going to work well for anyone, it would probably be this writer.

Throw in a little spice when it comes to your accent, and Siri seems to get a little wonky. Depending on the accent, the speech recognition can range from missing a couple words here and there to completely mangling what you want to say.

The Apple support discussion threads have various entries that address this issue. One user claims that Siri doesn’t understand his Indian accent, while another user claims that his wife’s Filipino English is easily understood by everyone – except for Siri.

Here’s a collection of videos that folks have posted to YouTube, showing how Siri responds to certain accents. First up, here’s one that shows a Japanese man attempt to get Siri to understand the word “work.”

This video courtesy of Gizmodo Australia shows that Siri works quite well with an Australian accent – as long as you keep the Siri set to Australian English. Change that and you have a world of problems –

Next, here’s Siri reaction to an English speaker with a thick Scottish accent. It’s struggling, to say the least –

More fun from Japanese speakers. I’m pretty sure he didn’t say “read dick.”

On the flip side, here is Siri working quite well with an English speaker with an Indian accent –

Finally, here’s Siri working great with both American and British English speakers, courtesy of Stuff.tv –

What do you guys think of Siri? Are you having trouble getting it to understand you? Have you tried to get it to say anything funny? Let us know in the comments.

The article says that clickliverpool.com, an independent news site, used the Google search engine tool that “divides language used on web addresses into three categories of basic, intermediate and advanced” to look at the Twitter pages of the “most followed Premier League players.”

What they found was that Rio Ferdinand’s tweets consisted of 88% basic language and only 11% intermediate language – the worst of the worst. By comparison, Wayne Rooney’s tweets were a whopping 56% intermediate.

It didn’t take me long to figure out how clickliverpool performed their unscientific study. One of the advanced search options in Google involves the ability to “annotate results with reading levels.” Then you can search within a site or domain to find the range of results in each category – basic, intermediate and advanced. Google calls it the reading level tool.

So all you have to do is search the Twitter page of whoever you are studying with annotated language results turned on and you’ll get a reading of how advanced their language is across their tweets. For example, the tweets of footballer Jack Wilshere, who was included in the study, look like this when you perform the search “site:http://twitter.com/#!/jack_wilshere.”

But here’s where we find some discrepancy. According to the study, Jack Wilshere had a 43% Basic level and a 54% Intermediate level. What gives?

If you search ‘http://twitter.com/#!/jack_wilshire” without the specific denotation of a “site:” search, you will get these results:

They key is the parameters. Without the “site:” search, Google is actually calculating the reading levels for millions of results – basically anything on the web that mentions Jack Wilshere, Twitter, or anything in between. When you add that “site:” search, it simply pulls results from Twitter statuses created by the Jack Wilshere account. 9 million plus results vs. 819. And 819 is what we want.

So this list looks to be inaccurate, except for the top (worst) performer, Rio Ferdinand. His results match the “site:” search. It is likely that when clickliverpool used the Google tool to find its other results, they may have forgotten to search the specific Twitter page and instead searched millions of internet mentions – basically calculating the reading level of articles about the footballers instead of tweets made by them.

But the whole point of pointing this out is to say that this is quite a fun thing to do. By using the Google reading level search tool, you can look at the complexity of language in your friends’ tweets or those of celebrities. It is important to note, however, that these results will include some of your retweets, which aren’t necessarily your own words. But if you use this tool to check out someone who posts original tweets on a regular basis, it should give you a hint at the complexity of their language use on the social media site.

So, I conducted my own little unscientific study of the top 10 users on Twitter, based on number of followers. I wanted to see who was the most eloquent of the bunch.  What I found surprised me, as I expect it will surprise you.  Here is the top 10 list, organized by number of followers:

1. Lady Gaga
2. Justin Bieber
3. Barack Obama
4. Britney Spears
5. Kim Kardashian
6. Katy Perry
7. Ashton Kutcher
8. Ellen DeGeneres
9. Taylor Swift
10. Shakira

And here is how they ranked when it comes to language complexity, based on the biggest percentage of Intermediate results:

1. Britney Spears (67%)
2. Katy Perry (66%)
3. Ashton Kutcher (49%)
4. Barack Obama (23%)
5. Lady Gaga (13%)
6. Kim Kardashian (6%)
7. Taylor Swift (5%)
8. Justin Bieber (4%)
9. Shakira (3%)
10. Ellen DeGeneres (<1%)

Surprised?

The next question of course is what classifies something as intermediate text as opposed to basic text? It’s hard to say. Telling the difference between the intermediate tweets and the basic tweets is incredibly difficult.

The only person in the ten to score a tweet on the “Advanced” spectrum of the reading scale was Katy Perry. She received that distinction for this tweet:

Is it the melatonin reference? Two of them? Albino? Something about this tweet has Google labeling it advanced.

Of course, this language search tool isn’t perfect. But it’s pretty fun.

[Photo Courtesy Twitter]