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I read this morning some very interesting news about Twister, a new decentralized and anonymous Twitter-like social network. It was created by Miguel Freitas, an engineer based in Rio De Janeiro, this new micro-blogging application can’t be shut down by, for example, some nation state or large corporation. Also, Twister is designed in such a way that other users cannot know whether you are online, what your IP address is, or who you follow.

As a bitcoin “aficionado”, I find particularly interesting that the magic behind Twister is this popular alternative currency. It is another great example of the great range of potential applications that bitcoins unfold.

You can read more about this story at Wired Magazine:

Twister is surprisingly easy to use for an application that’s so new, that isn’t controlled by a central authority, and that places so much emphasis on security. Other decentralized alternatives to Twitter and Facebook — such as, Identica and Diaspora — require that you either operate your own dedicated server or trust someone else to run a server for you. Twister works more like peer-to-peer file sharing software: Launch the app, and it connects with other users. There’s no need for a central server.

It manages this trick through the bitcoin protocol, though not the network that actually drives the digital currency. Basically, the protocol handles user registration and logins. Just as machines — called miners — verify transactions over the bitcoin network to ensure no one double-spends bitcoins and everyone spends only their own coins, a network of Twister computers verifies that user names aren’t registered twice, and that posts attached to a particular user name are really coming from that user.

Posts are handled through the BitTorrent protocol. This lets the system distribute a large number of posts through the network quickly and efficiently, and it lets users receive near-instant notifications about new posts and messages — all without the need for central servers.

I was reading this morning about a cool visualization of tweet languages on the NYC area. It allows to see in what areas there is a majority of tweets in each language. You can find it here.

From Fast Company:


Visualizing the location of the 8.5 million geo-located tweets sent from devices in the five boroughs between January 2010 and February 2013, the interactive Twitter NYC map provides a colorful snap shot of digital life in New York. Tweets were sorted by language using Google’s translation tools, color-coded, and then plotted on a map. Spanish tweets are noted by a blue dot, Portuguese by a red dot, and Japanese by a green dot.

As project co-creator Ed Manley wrote in a blog post, “it is immediately clear how Manhattan dominates as the centre for Twitter activity in New York.” It also is the most multilingual area on Twitter, particularly in tourist-dense destinations like Times Square.

Mostly, though, people in New York are tweeting in English: 95% of tweets analyzed by the project were in that language. The next top language, Spanish, accounted for 2.7% of tweets. New York’s top Twitter languages (after English) were Spanish, Portuguese, Japanese, Russian, Korean, and French. If you’re looking for the neighborhoods with the most linguistic diversity combined with tech savvy, Manley also notes that the most popular zones for non-English tweets are South Brooklyn, Coney Island, and Jackson Heights.

Today I had the pleasure to meet some of the members of the Information Visualization team at AT&T Research. I was already aware of some of their work, such as the really cool plots of the “music universe“. Exploring this map one can learn what bands are similar to the bands she or he likes and, observing the map from a vantage point, one can see how music bands are aggregated in a big “continent”, except for classical music and reggae, that seem to be far from all other music styles.

Today I saw some new demos from that team that are so cool. One of my favorites is a real time visualization of Twitter. It feeds from real time twits on an initial category one chooses and, from that moment on, it clusters and visualizes them in real time in such a cool way. You can select a key word to be highlighted during the process.

You will need a fast PC to see the whole beauty of it. My laptop is having trouble on displaying it as fluid and cool as I saw earlier today.

If you want to see other cool visualization projects, you should check the Information Visualization team and Yifan Hu’s home page.

Everyone already knows what happened yesterday on the East Coast so I will not give many details. An earthquake, centered somewhere in Virginia, was felt all over the north-east coast of the US. The Capitol Building and the White House were evacuated in DC, people felt the tremor as far up as in Toronto and many people got quite scared in New York, especially the ones that work in a very tall building.

Related to that, this morning I was watching the “news” (if you can call the Good Morning NY shows a news show…) and one of the reporters explained something that was very interesting. His family lives in Virginia and his mom called him about the earthquake before it even hit New York. In a similar way, the tsunami of tweets and Facebook posts scattered across the East Coast way faster than the actual tremor.

A friend of mine posted an old XKCD comic that describes this interesting effect:

I have been thinking about two things:

  • Could we somehow use Twitter and other social media to alert citizens of upcoming disasters? Even better, could we use the combination (social network data + location data) to predict the trajectory of a disaster, its intensity gradient and other characteristics of the event to improve alert systems?
  • If there is ever a major disaster, what will people do? Run and then tweet? Tweet and then run? Tweet while running?

As a final comment I’d like to add that feeling an earthquake on a 25th floor of a tall New York building was very scary. It might have not been felt that much on the street level, but up there…

About me:

Born in Barcelona, moved to Los Angeles at age 24, ended in NYC, where I enjoy life, tweet about music and work as a geek in security for wireless networks.
All the opinions expressed in this blog are my own and are not related to my employer.
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