Tag Archives: Twitter

Fun With Sparktweets

Posted on May 3, 2013 by Ernesto Ramirez

It’s no secret we love data here at Quantified Self, but we also love seeing how people interact with data. We’ve explored many of those interactions here and we’re always on the lookout for new and different ways people communicate their data and the insights therein.  A few weeks ago we wrote up a short “how to” post describing a recent phenomenon on Twitter – sparktweets. It didn’t take too long before we started seeing the Quantified Self community using these new “data words.”

We couldn’t stop thinking about sparktweets. What kind of data could you communicate in 140 characters? What would people do if it was easier to make a sparktweet? So we asked out friend Stan James to help us out and our Sparktweet Tool was born. Since then we’ve seen some great tweets roll though our feed, and we would love to see more. Need some inspiration? Here’s a few we really enjoyed:

What kind of conversation can you start with you data? Head on over to our Sparktweet Tool then make sure to add a link to your tweet in the comments or add to our conversation on the forum.

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How To Make A Sparktweet

Posted on April 13, 2013 by Ernesto Ramirez

Update: Want to make your own Sparktweet? We made a simple tool that you can use. Check it out here!

I was stumbling around Twitter the other day when I was confronted with something new and different:

Apparently that little data representation is not all that new and different. Way back in 2010 Alex Kerin figured out that Twitter was accepting unicode and decide to play around and see if it could represent data. Lo and behold it could and a SparkTweet was born:

Before we get into how you too can start populating your Twitter feed and Facebook (I checked and it worked there as well) with representations of your own Quantified Self data let’s dive into some history.

The data visualization theorist and pioneer, Edward Tufte, is primarily responsible for the widespread use of sparklines. In his wonderful his book, Beautiful Evidence, Tufte describes sparklines as

a small intense, simple, word-sized graphic with typographic resolution. Sparklines mean that graphics are no longer cartoonish special occasions with captions and boxes, but rather sparkline graphic can be everywhere a word or number can be: embedded in a sentence, table, headline, map, spreadsheet, graphic.

In another wonderful book, The Visual Display of Quantitative Information, Tufte describes sparklines as “datawords: data-intense, design-simple, word-sized graphics.“ Of course, those of us in the QS community are deeply interested not only in data, but also in how data operates in society, what is means as a cultural artifact that is discussed and exchanged in language both written and verbal. This interest iswhat initially  piqued my curiosity.  The movement of data and a dataword distributed among text and publicly expressed in a tweet. I can’t help but wonder, what does this mean for how we think about and express data about our world?*

How To

Update:Thanks to our QS friend, Stan James, you can now make Sparktweets right here on Quantifiedself.com. Just head over to our Sparktweet Tool page and start making your own “data words.”

If you want display quantitative data in your Twitter stream it shouldn’t take you all that long to get started. Lucky for us Alex Kerin has provided a nifty little Excel workbook that will generate the unicode that can be pasted into your tweet. Just download this workbook and follow the simple instructions! Soon you’ll be able to send out tweets just like this:

For those of you with a bit more technical skill Zack Holman has made a very neat command line tool that will quickly generate the unicode for sparklines.

Now you’re ready and able to go forth and tweet your data! If you use a sparktweet to express your Quantified Self data be sure to let us know in the comments or tweet at us with #sparktweet and/or #quantifiedself.

*Of course the use of sparktweets is not without controversy in the world of data visualization. For more discussion on sparktweets and their utility I suggest you start here.

 

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#qschat Number 1

Posted on March 22, 2012 by Ernesto Ramirez

On Tuesday March 12, Nick Dawson asked if there was a Quantified Self Twitter Chat. Lots of interest followed so we decided to make it happen. We had the very first #qschat twitter chat last Thursday and it was a lot of fun. I learned a lot about what people are thinking and doing in the Quantified Self space and in their daily lives. I’ve included some highlights from the chat below, but you can always just search Twitter for #qschat for the full list of tweets.

We’re going to try our best to make this happen every Thursday night at 6PM PST. Just follow the Quantified Self Twitter account to join in!

Our three questions for tonight (3/22/12) will be:

  1. What is one thing you’ve learned from your self-tracking and Quantified Self practice?
  2. Have you ever shared your experiments and results with anyone? If so how did it go. If not, why not?
  3. Health is an obvious Quantified Self area of interest. What other areas have you applied, or want to apply, Quantified Self to?

Again, join us at 6 PM PST to talk about these three questions and more!

Read more to see some selected tweets from our first chat last Thursday!
Continue reading →

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How I wasted two years on Twitter, all because I wasn’t tracking

Posted on March 25, 2011 by Matthew Cornell

Between 2007 and 2009 I spent a ton of time in Twitter before it finally hit me that 1) the net improvement to my life was zilch, and 2) had I thought of it going in as an experiment, I would have quit a long time ago and freed up energy for more effective efforts. Of course social media tools can provide plenty of value, but, as Alex said, Social media is an addictive time suck.

How do we go about measuring the value of Twitter? Business calls it ROI, but I think of it as simply what you hope to get out of it. The key is deciding why you’re using it. In my case I was dabbling, which is a fine motivation, as long as it’s done experimentally. After all, how many discoveries came from just getting curious and trying out something new? But here I should have set a time limit, and I’d still want to have something quantified, even it it’s as soft as “perceived value.”

But for more specific uses, coming up with measures is important. Are you trying to get more customers? Do you want to hear from people who can give you ideas for your product or book? Or maybe it’s more of a social pulse use – keeping in touch. Some metrics are straightforward, such as # inquiries about your business, or number of tweets from others that made you smile. However, I think a major challenge is latency – the time delay between action on your part and resulting effects seen in your life. For example, it might be months before you hear from someone who’s been silently reading your tweets. Maybe in those cases we could make the measure more direct by asking them explicitly what the impact is. I’m not sure.

While I didn’t treat using Twitter as an experiment per se, I managed a few times to use Twitter itself as a platform for experimentation. Continue reading →

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Denis Harscoat on Quantter

Posted on March 21, 2011 by Alexandra Carmichael

Denis Harscoat is the founder of Quantter and co-organizer of the London Quantified Self group and the new Paris QS group. He and co-founder Francis Dierick noticed people quantifying their activites on Twitter, and created a simple way to gather this data together. In the video below, Denis takes us on a tour of Quantter – leveraging Twitter hashtags to help people track their daily activities, receive motivating cheers from their friends, and help them reach their goals. Quantter is also a proud sponsor of the Quantified Self Conference in May, so you can meet Denis and Francis there! (Filmed at the Amsterdam Quantified Self meetup held at Mediamatic).

Denis Harscoat – Quantter from Quantified Self Amsterdam on Vimeo.

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NodeXL maps of tweets about Quantified Self

Posted on January 26, 2011 by Alexandra Carmichael

This is a guest post from Marc Smith, an expert on analyzing social media networks. Thanks Marc!

–

I am very interested in the Quantified Self conference to be held in Mountain View, California, May 28 and 29.  While I have attended just a few of the in-person meet-ups, which were engaging and intriguing events, I have followed the blog and tweet stream closely.  These events feature short presentations about practices, prototypes, and products that record information about our own behavior and activity.  It is great to hear that, as the meet-ups grew to become very large, a conference has been scheduled to accommodate the demand and growing interest in the intersection of sensors and other devices with medical and personal self-monitoring.  I plan to attend.  Using a variety of devices, our lives can now create detailed inscriptions that illuminate our behavior and patterns with novel clarity and detail.  (See: http://quantifiedself.com/conference/)

Quantifying the “Quantified Self” discussion in Twitter: Here is a map of the connections among the people who recently tweeted the string “quantified self“.

20110120-NodeXL-Twitter-Quantified Self Graph Highlighted Most Between User with tooltip

This is the list of the most “between” users in this network:
20110120-NodeXL-Twitter-Quantified Self Graph Top Between List

The most between participants in this graph are: @quantifiedself, @egadenne, @harscoat, @genomera, @neufit, @jxa, @agaricus, @emergentorder, @bulletproofexec, @2healthguru.

The topics discussed in the quantifiedself tweet stream can be rendered as a network graph based on words that co-occur:
20110120-NodeXL-Twitter-Quantified Self High Between Keyword Co-occurance Network Graph

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Kiel Gilleade on The Body Blogger

Posted on November 10, 2010 by Alexandra Carmichael

From the London QS Show&Tell meetup group: Kiel Gilleade talks about his experiences with tracking his heart rate 24×7 and sharing it in real-time via the Internet. More information about the project can be found at Kiel’s Physiological Computing Site and at his BodyBlogger Twitter stream. Watch the video below to hear Kiel talk about the interesting social ramifications of continuous heart rate tracking, and what he has learned about alcohol, sleep, and stress.

The Body Blogger – Physiology in a Public Space from Kiel Gilleade on Vimeo.

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7 Secrets to Maximize Social Media with Minimal Time

Posted on September 17, 2010 by Alexandra Carmichael
Social media is an addictive time suck. We know that, but we still spend almost a quarter of our time on social networking sites. Can this be optimized? Can we get most of the benefits of Twitter, Facebook, and LinkedIn without such a time cost?
People have increasingly been asking me these questions, so I decided to put together a list of my top secrets to social media savvy.
If you have a message to spread about your project and you want to reach a big audience without twittering yourself into a stupor, this list is for you. #7 is the most important one, so make sure to read all the way to the end.

Continue reading →

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Who Publicly Tweets Body Weight Using Withings?

Posted on July 8, 2010 by Gary Wolf

On of the most well known QS devices is the Withings WiFi body scale. Automatically transmitting weight to a computer or mobile phone, the scale is a good example of a solid, mainstream approach to self-tracking. But I was curious recently to see how many people are taking advantage of the ability to publicly tweet their body weight. The number is not very large; normally just a few every hour, a few hundred in total, assuming most people measure their weight once per day. Certainly a small fraction of Withings users.  English tweets dominate, but German and Japanese tweets are also common.

withings.comTwitterSearch.jpeg
QS friends know that I’ve been ranting lately against the notion that the only reason people will do anything is because they want to improve their visibility in a social network. The viral spread of Facebook, Twitter, and Foursquare have convinced some otherwise smart people I know that nobody will do anything that is not “social.”

This means that one of the most important features of a wireless scale is easy, automatic public tweeting of your weight. It makes a good story, but as far as I can tell it is not very real,  even among early adopters. I bother to point this out because the focus on public exposure of personal data obscures some of the reasons people actually do want to track themselves using convenient tools that stream data to computers and phones. They want this information not to share publicly, but for themselves, and perhaps for a small number of others who comprise their private network of close support.

Personal data has tremendous personal value. In aggregate, and anonymized, it is important for science and public health. But the theory that personal data, outside of sports and gaming, is a type of social currency is still waiting for some evidence to back it up.

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Health Hashtags: A Microsyntax for People and Machines

Posted on November 8, 2009 by Mike Kirkwood
With the explosion of microblogging, tweeting, and status updates, it is clear that embedding personal metrics in social tools is on the tips of our fingers and is a natural extension to the personal toolbox. This post explores the opportunity of OHME (Open Mobile Health Exchange), a first-mover in the new world of Microsyntax, and a new entry into the microsyntax.org working group.

How it works
Taking Twitter as the backdrop, and the #hash being the first example, Microsyntax might be termed ‘in-line metadata’.  It is a self enclosed tag that associates this post with other like tagged posts.   It helps search, and it helps set context and find-ability.   
The first version of OHME adds more meaning to a set of personal metrics, including blood pressure, weight, steps per day, pain, and about 20 metrics a person can log using SMS, Twitter, devices, or nearly any tool that sends messages.  The project offers royalty free libraries for schemas and parsers.

Where it fits in today (person to machine)
When micro-blogging, or posting personal status, hashes can be used to help systems (machine readable) tools use these tags and syntax to facilitate actions.   For example, posting #spd=13045 suggests that a person has walked the equivalent of 13,045 steps in this day.   

With microsyntax there is a new dialog on how to aggregate device manufacturers, software vendors, and users to grow a vocabulary that thrives and rewards them with good tools and increased connection to their community.

ohme-bubble.jpg

Where it leads in future (machine to machine)
The advent of microsyntax, and OHME provides a new rhythm to the stream.   Mashups made from diverse streams of personal data allow new contexts to emerge, and new possibilities for action and specialization.  How will the health care system respond?  Will it become more patient centric, or merely use data generated automatically by various devices to make us more “hospital ready?”  Microsyntax such as the OHME project highlight the opportunity for every person to have quality streams of personal metrics.   Health loggers are already using microsyntax today.  Now is the time to build tools that aggregate and share these streams in meaningful ways.

Some considerations (machine to person is person to person)
In observing the landscape, it looks promising that natural alliances can form around syntax and vocabularies, giving rise to tools that support each other’s streams and have graceful hand-off from system to system.   In this new world model of data stewardship, a future can be seen where the microsyntax stream becomes more a critical resource.   It is in this context that enterprise class systems may emerge to help guide microsyntax systems towards reliable services.    

Today, our social web may be a bit fragile for such un-fettered live results about personal metrics.   A community designed sandbox for moving services gradually into the consciousness and letting first-adopters set the terms is a promise for microsyntax.   

Even though it is easy to type #911 #Robbery, our social and operational systems may not be as easy to accept the consequences of the message until we set rules and contexts of reliability – and the sender is authenticated in a way that grows
trust.   

In this arena, microsyntax has both the honor of being extremely easy for the user (can do it without a mouse or selection) and to locate (parsers and search ala Twitter).   It also on the cutting edge of personal utility and personal safety and asks the question of how do we communicate personal streams.   

Speaking for one logger, this is a great step forward, the start of an the ecosystem that supports people and patients everywhere.   #OM+1!

Disclosure:  Mike Kirkwood‘s first post on Quantified Self, he is CEO of Polka a personal health platform. twitterhashtagblack.jpg
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