Walter Jessen Digital + Strategy http://www.walterjessen.com Mon, 19 May 2014 14:41:03 +0000 en-US hourly 1 http://wordpress.org/?v=3.9.1 Trying Postach.io http://www.walterjessen.com/personal-brand/trying-postach-io/ http://www.walterjessen.com/personal-brand/trying-postach-io/#comments Sun, 11 May 2014 14:29:10 +0000 http://www.walterjessen.com/?p=7488 I stumbled across Postach.io recently and have been experimenting with it. Postach.io is a service that turns an Evernote notebook into a content management system.

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I stumbled across Postach.io recently and have been experimenting with it. Postach.io is a service that turns an Evernote notebook into a content management system.

Postach.io

It’s truly effortless publishing. When you establish your Postach.io account, you link your blog to a folder in your Evernote account. Then, you simply create a note in that folder. When you tag it “published,” it appears on your blog. Check out the video below.

I’m particularly interested in Postach.io with respect to blogging while mobile. In today’s 140-character world, readers increasingly prefer shorter, more easily consumable content, which makes quick-hit and multimedia posts from a mobile device ideal.

Postach.io is a work in progress. I’ve already helped them with a theme issue (there’s a number of great themes to choose from, and they’re all customizable), and I’ve read that they’re working on a way to import posts from WordPress.

So, for the moment, come and follow me at WalterJessen.me.

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Open Source Big Data Technology Landscape http://www.walterjessen.com/business/open-source-big-data-technology-landscape/ http://www.walterjessen.com/business/open-source-big-data-technology-landscape/#comments Thu, 05 Sep 2013 02:38:14 +0000 http://www.walterjessen.com/?p=7470 A visual overview of the open source technology landscape for big data.

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The big data landscape is a rapidly growing ecosystem. It’s tough to keep track of all the companies entering the space, let alone the technologies driving it.

According to a report released last summer by the open-source business intelligence provider Jaspersoft, open-source projects/products dominated the emerging big data landscape. And it looks as though that hasn’t changed.

Mark van Rijmenam (Twitter: @VanRijmenam) is the founder of BigData-Startups.com and frequently blogs about big data, both at BigData-Startups and SmartDataCollective. One of his recent posts led me to the figure below. I thought it was a nice visual overview of the open source technology landscape for big data.

I’m familiar with some of the tools, such as KNIME for business intelligenceOrange for data mining and Gephi for open graph visualization. Others are missing. In particular, R for data analysis and statistical computingAlpine for predictive analytics and Cytoscape for complex network visualization.

The absence of R really surprises me as it was recently voted the top language for analytics, data mining and data science for the third year in a row at KDNuggets.

I wonder how many of these tools existed a year ago and how many I’ll get the chance to work with in the next year. Which would you choose?

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Point-and-click Extended Edition: August 22nd, 2013 http://www.walterjessen.com/point-and-click/point-and-click-extended-edition-august-22nd-2013/ http://www.walterjessen.com/point-and-click/point-and-click-extended-edition-august-22nd-2013/#comments Fri, 23 Aug 2013 01:31:29 +0000 http://www.walterjessen.com/?p=7457 An extended version of news and information focused primarily on media, communications, data and the Web.

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The system I’ve used in past years to capture and post Tweets broke down with version 1.1 of the Twitter API last year. I’m working on resurrecting it now. During one the tests tonight, I imported a number of recent Tweets. Typically, my ‘Point-and-click‘ posts aren’t so long, but I’ve shared some really great articles recently and wanted to archive them here for future reference. Enjoy!

Neuroscientists need to embrace open access publishing too [figshare blog]

Don’t Be Blinded By Big Data [InformationWeek]
Making decisions based on macro trends while ignoring #LittleData fundamentals is a recipe for failure.

Ogilvy Chief Data Officer Role May Be Sign of Things to Come [Advertising Age]

The Day the Knowledge Graph Exploded (+50.4%) [Moz]
As Google builds a ‘web of things,’ entities are going to gain ground and pages are going to lose ground.

Global Genomics Priorities and Public Health [GenomeWeb]

CVS’s Insanely Long Sales Receipts Are A Twitter Meme–And A Golden Branding Opportunity [Fast Company]

5 Steps To Data-Driven Marketing [SmartData Collective]

Why ‘Big’ is Blinding Us to the Real Value of Big Data [Wired]
It’s not volume, it’s the ability to integrate. Why ‘Big’ is Blinding Us to the Real Value of #BigData

Searching Big Data for ‘Digital Smoke Signals [New York Times]
Now this is cool. Using #bigdata to track unemployment & disease as if they were brands.

A Beginner’s Guide to Big Data Visualization [SmartData Collective]

Borrow These 5 Smart Startup Habits To Maximize Your Productivity [Fast Company]

Big Data Hiring: Unorthodox Advice [Information Week]
Great ideas to build a data science team. Big Data Hiring: Unorthodox Advice

Search Is Dead… Long Live Search! [Forbes]
Why #content matters so much today: semantic search results are based on context, not probability.

Want to start a big data company? Here are 5 things you need to know [Gigaom]

The 7 Steps Of Big Data: How To Make It Work [Forbes]

Bezos Could Deliver a Bold New Way of Packaging Content at the Post [Mediashift]

Healthcare Analytics Sought For Population Health Management [InformationWeek]
Healthcare #analytics sought for population health management (PHM), tech still in early stages.

10 Things You Need To Know About The Moto X Smartphone [ReadWrite]

What Makes The Moto X The Smartest Smartphone Around [ReadWrite]

The Moto X Represents the Future of Everything [CIO.com]

The ‘Internet of things’ will mean really, really big data [InfoWorld]
Internet-enabled sensors and objects promises big business benefits #bigdata

Health in the U.S. and other rich countries: We pay more in health care but are sicker. [Slate Magazine]
What factors contribute to the U.S’s “#health disadvantage”? Apparently everything

Why you should never trust a data visualisation [The Guardian]

Why PR Is Your Best Marketing Tool—And How To Do It [LinkedIn]

Mainstream Media <3 Sponsored Tweets [Digiday]

All links shared via Twitterfollow along!

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Mixwest 2013 Twitter Analytics Day Two http://www.walterjessen.com/social-media/mixwest-2013-twitter-analytics-day-two/ http://www.walterjessen.com/social-media/mixwest-2013-twitter-analytics-day-two/#comments Sun, 11 Aug 2013 00:02:43 +0000 http://www.walterjessen.com/?p=7418 An analytical overview of Mixwest 2013 #mixwest13 Day 2.

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Mixwest

Yesterday, I attended the second day of a two-day conference here in Indianapolis called Mixwest, which focuses on four core areas: marketing, social media, design and tech. It reminds me of the ScienceOnline conference but smaller, local (Midwest), and much more focused on business.

As I did yesterday for Mixwest 2013 Day 1, I mined all of the tweets about the conference from the Twitter stream.  I’m interested in organizing unstructured data and textual analytics. Below are Twitter analytics for Mixwest 2013 Day 2.

First, some statistics

Tweets were collected and analyzed from 7 am Friday morning August 9th, 2013 (2 hours before the conference started) to 6 pm the same day (1 hour after the conference ended). In total, there were 1,670 tweets — just 43 less than day 1 — consisting of 26,835 words from 226 people. There were 241 people tweeting on day 1, so that’s 15 less tweeters. The top tweeter was Kayla Hulen (Twitter: @kayla_hulen) with 74 tweets!

Here are the top 10 Twitter users and their number of tweets:

Rank Twitter user Number of tweets
1 @kayla_hulen 74
2 @bnpositive 61
3 @mixwest 60
4 @theramennoodle
58
5 @heatherchastain 54
6 @robbyslaughter 50
7 @juettj 48
8 @melissabalkon 46
9 @socialreactor 46
10 @edeckers 43

 

Like yesterday, again I didn’t make the top 10 and came in at number 21! For day 2, I attribute the lack of Tweets to Nathan Hand (Twitter: @nathan_hand) and his session “Using social media to find Bigfoot”. In case you were wondering, we did find him and no, we’re not telling you. Thanks for an interesting, engaging session Nathan!

Similar to day 1, most conference attendees using Twitter actually tweeted very little: 155 people tweeted just 1-4 times. Twenty people tweeted 5-9 times and 25 people tweeted 10-19 times. Each of the other groups with 20 or more tweets consisted of less than 15 people.

Tweets per tweeters

Let’s take a moment and focus on retweets. In my opinion, retweets are a way of saying “yeah, I like that” or “I agree!” and for the purposes of this analysis come in two forms: they can be a simple rebroadcast (e.g. RT: “original tweet”), which we’ll call a RT without conversation, or a retweet with comments back to the original tweeter (e.g. Agreed! RT: “original tweet”), which we’ll call a RT with conversation.

Without getting into details, a modified tweet (MT) is also classified as a RT with conversation, since usually the modification is made to provide extra space to comment.

Just under one-third (31%) of the #mixwest13 tweets today were retweets. Of those, 9% (12 of 522) were a RT with conversation. The top retweeter was Mixwest (Twitter: @Mixwest) with 41 retweets.

Mixwest13 day2 retweets

#Mixwest13 Day 2 word cloud of tweets

I love word clouds. I think they’re a useful visual representation of data and effectively convey “themes” of a data set.

From 26,835 words, 3,249 were unique. I calculated the frequency of all 3,249 words and then “cleaned” the data, removing all words less than 3 characters and common words such as “your”, “from”, “that” or “about”.

Word frequencies were adjusted — that is, blunted — at the high end such that any frequencies greater than 100 were set to 100, and any frequencies greater than 150 were set to 150. This was done to prevent distortion of the word cloud.

The top 100 terms were then imported into Wordle and a weighted tag cloud was generated. Feel free to download any of the files below and reshare.

Mixwest13 Day 2 Word Cloud

Here are the top 10 terms (prior to adjustment) from the word cloud above:

Rank Term Frequency
1 mixwest13 1679
2 @hunckler 145
3 @mixwest 144
4 @douglaskarr 121
5 about 117
6 @colefarrell 117
7 @edeckers 99
8 people 90
9 @robbyslaughter 84
10 social 79

All data and images are available for download: low-resolution image, high-resolution image or raw data set of 100 terms with frequencies.

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Mixwest 2013 Twitter Analytics Day One http://www.walterjessen.com/social-media/mixwest-2013-twitter-analytics-day-one/ http://www.walterjessen.com/social-media/mixwest-2013-twitter-analytics-day-one/#comments Thu, 08 Aug 2013 15:30:49 +0000 http://www.walterjessen.com/?p=7391 An analytical overview of Mixwest 2013 #mixwest13 Day 1.

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Mixwest

Today, I attended the first day of a two day conference here in Indianapolis called Mixwest, which focuses on four core areas: marketing, social media, design and tech. It reminds me of the ScienceOnline conference but smaller, local (Midwest), and much more focused on business.

As I did last year for BlogIndiana 2012 (see posts from day 1 and day 2), I mined all of the tweets about the conference from the Twitter stream. I’m interested in organizing unstructured data and textual analytics. Below are Twitter analytics for Mixwest 2013 Day 2. If you’re attending the Mixwest conference and like the analysis below, please let me know! It would be great to meet you and learn more about what you do.

First, some statistics

Tweets were collected and analyzed from 7 am Thursday morning August 8th, 2013 (2 hours before the conference started) to 6 pm the same day (1 hour after the conference ended). In total, there were 1,713 tweets consisting of 27,462 words from 241 people. The top tweeter was Leah Beatty (Twitter: @lediamedia) with 126 tweets!

Here are the top 10 Twitter users and their number of tweets:

Rank Twitter user Number of tweets
1 @lediamedia 126
2 @heatherchastain 83
3 @mixwest 65
4 @justheather 64
5 @sheriaricherson 60
6 @kayla_hulen 55
7 @theramennoodle 53
8 @juettj 49
9 @benrisinger 47
10 @bnpositive 46

 

Sadly, I didn’t make the top 10 and came in at number 13. I blame it entirely on Ryan Brock (Twitter: @ryanbrock) and his session “Just Write It: Blogging Against the Rules,” which was so good, I couldn’t listen, process, and tweet at the same time. Thanks Ryan.

Most conference attendees using Twitter actually tweeted very little: 172 people tweeted just 1-4 times. Twenty-seven people tweeted 5-9 times and 19 people tweeted 10-19 times. Each of the other groups with 20 or more tweets consisted of less than 10 people.

Tweets per tweeters

Let’s take a moment and focus on retweets. In my opinion, retweets are a way of saying “yeah, I like that” or “I agree!” and for the purposes of this analysis come in two forms: they can be a simple rebroadcast (e.g. RT: “original tweet”), which we’ll call a RT without conversation, or a retweet with comments back to the original tweeter (e.g. Agreed! RT: “original tweet”), which we’ll call a RT with conversation.

Without getting into details, a modified tweet (MT) is also classified as a RT with conversation, since usually the modification is made to provide extra space to comment.

Over one-third (37%) of the #mixwest13 tweets today were retweets. Of those, 14% (88 of 630) were a RT with conversation. The top retweeter was Sheri A. Richerson (Twitter: @SheriARicherson) with 53 retweets.

Mixwest13 Day 1 Retweets

#Mixwest13 Day 1 word cloud of tweets

I love word clouds. I think they’re a useful visual representation of data and effectively convey “themes” of a data set.

From 37,462 words, 3,030 were unique. I calculated the frequency of all 3,030 words and then “cleaned” the data, removing all words less than 3 characters and common words such as “your”, “from”, “that” or “about”.

Word frequencies were adjusted — that is, blunted — at the high end such that any frequencies greater than 100 were set to 100, any frequencies greater than 300 were set to 150, (note the Oxford comma!) and any frequencies greater than 400 were set to 200. This was done to prevent distortion of the word cloud.

The top 100 terms were then imported into Wordle and a weighted tag cloud was generated. Feel free to download any of the files below and reshare.

Mixwest13 Day 1 Word Cloud

Here are the top 10 terms (prior to adjustment) from the word cloud above:

Rank Term Frequency
1 mixwest13 1717
2 @jaybaer 334
3 @mixwest 181
4 @ryanbrock 160
5 great 159
6 content 138
7 @stevenshattuck 136
8 google 131
9 @lediamedia 96
10 time 95

All data and images are available for download: low-resolution image, high-resolution image or raw data set of 100 terms with frequencies.

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Point-and-click: February 28th, 2013 http://www.walterjessen.com/point-and-click/point-and-click-february-22nd-2013/ http://www.walterjessen.com/point-and-click/point-and-click-february-22nd-2013/#comments Thu, 28 Feb 2013 06:00:50 +0000 http://www.walterjessen.com/?p=7159 A digest of news and information focused primarily on media, communications, data and the Web.

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‘Big data’ is dead. What’s next? http://t.co/8w52efHLeZ [Venture Beat] #PredictiveAnalytics #SmartData

Only 11% rely on data to make decisions! #BigData Confuses Most Marketers http://t.co/CxUnlMwq [American News Report]

The real challenge is figuring out your question! #BigData is a solution looking for a problem: Gartner http://t.co/W0pBlron [ITworld]

Great tips! The Best Content Marketing Is Free http://t.co/rOPv4IBM [Inc] #contentmarketing

We’re more fooled by noise than ever before. Beware the Big Errors of ‘Big Data’ http://t.co/cDbHLRCb [Wired]

Big Data Right Now: Five Trendy Open Source Technologies http://t.co/maGBIKwW [TechCrunch] #bigdata

What’s Your Time To Insight from Data? http://t.co/6TfxN9QO [Forbes] #data #analytics

Meet Your Company’s New Chief Customer Officer http://t.co/cDhVvcuaoG [Harvard Business Review Blog Network]

All links shared via Twitterfollow along!

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Does the Twitter Timeline Widget Improve SEO? http://www.walterjessen.com/marketing/does-twitters-timeline-widget-increase-seo/ http://www.walterjessen.com/marketing/does-twitters-timeline-widget-increase-seo/#comments Mon, 25 Feb 2013 14:00:56 +0000 http://www.walterjessen.com/?p=7184 The social networking service Twitter offers a widget, which enables you to put updates anywhere on the web. But does it improve SEO?

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Twitter is enormously popular today. Indeed, there are roughly 72 million active accounts tweeting some 400 million times a day. Because of this popularity, more and more businesses are including Twitter in their social media strategy.

The social networking service offers a widget, which enables you to put updates anywhere on the web or create a live stream for an event. You can embed on a web page all the tweets from a particular account, list or search result.

A consulting client of mine recently asked about the search engine optimization (SEO) benefits the Twitter widget, which is a great question when you consider all the key words from the widget that show up on a page.

The Twitter widget is created from javascript code, which is interpreted in the browser. If you right click on a web page that displays a Twitter widget and view the page source, it looks like html code, but that code isn’t there when a search engine looks at the page (that’s not exactly true, but hang with me a minute).

For a number of years now, Twitter has had individual widgets to show tweets from a specific Twitter account, favorites from a single account, tweets by those on a Twitter list, or a Twitter search result. In the fall of last year, Twitter replaced individual widgets with a new, unified interactive timeline widget. The new widget allows people to not only see tweets, but respond to them right from the widget.

We use the Twitter widget on the Biomarker Commons home page to show links and discussion around biomarkers and personalized medicine. Check out how the new widget looks (left) compared to the old widget (right).

Twitter widget on Biomarker Commons

Back to the question about the SEO benefits of the Twitter widget and page html code. You can use Google Webmaster Tools to see what the Googlebot sees: sign in, select your website (you have to have previously added your site), Health >> Fetch as Google, fetch the homepage (or other page if the widget isn’t on your homepage), and then click on the “Fetch Status” link “Success”.

Here’s what the Googlebot sees when it looks at the widget on Biomarker Commons. Notice the <script> code at the end:

<h2>On Twitter</h2><div class="content"><p><a class="twitter-timeline" href="https://twitter.com/search?q=biomarker+OR++OR+%22personalized+%22" data-widget-id="305364624532963328">Tweets about "biomarker OR biomarkers OR "personalized medicine""</a></p><script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js?132930";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs");</script></div>

The Twitter widget is generated by javascript in the browser, so it isn’t content that is indexed by a search engine and thus has no SEO benefit.

By the way, if you’re using the old Twitter widget, it’s time to upgrade. The old widget uses the application programming interface (API) v1.0. Twitter will start running “blackout tests” for API v1 on March 5th, 2013, and over the next few weeks, will be announcing additional blackout tests and a more detailed schedule regarding the retirement of API v1.

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Visualizing ScienceOnline 2013 Tweets Day Three http://www.walterjessen.com/science/visualizing-scienceonline-2013-tweets-day-three/ http://www.walterjessen.com/science/visualizing-scienceonline-2013-tweets-day-three/#comments Tue, 05 Feb 2013 04:54:03 +0000 http://www.walterjessen.com/?p=7086 Twitter analytics from Day 3 of ScienceOnline2013.

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Saturday was the third and last day of ScienceOnline 2013. ScienceOnline is a non-profit organization that facilitates conversations, community and collaborations at the intersection of Science and the Web. They do this through online networks, projects and face-to-face events (both global and grassroots).

ScienceOnline‘s preeminent annual meeting takes place in North Carolina every January. As I did for the first two days of the conference, below is an analysis of Twitter data for day three.

 

First, some statistics

The tweets were collected between 12:00am and 11:59pm on Saturday, February 2nd. In total, there were 6,719 tweets (14% more than day one and 5% more than day two) consisting of 120,655 words from 1,261 people. Just like #scio13 day one and #scio13 day two, the top tweeter was Janet Stemwedel (Twitter: @docfreeride) with 214 tweets! Here are the top 10 Twitter users and their number of tweets:

Rank Twitter user Number of tweets
1 @docfreeride 214
2 @erinpodolak 143
3 @fiainros 131
4 @drmrfrancis 110
5 @WhySharksMatter 108
6 @TalkScienceToMe 100
7 @rocza 88
8 @jacquelyngill 85
9 @jenedavison 78
10 @boraz 75

Most conference attendees using Twitter actually tweeted very little: 1,076 people (85%) tweeted less than 10 times, one hundred fifteen people tweeted 10-20 times, 54 people tweeted 21-49 times, and 16 people tweeted over 50 times.

Let’s focus on retweets. Retweets are a way of saying “yeah, I like that” or “I agree!” and for the purposes of this analysis come in two forms: they can be a simple rebroadcast (e.g. RT: “original tweet”), which we’ll call a RT without conversation, or a retweet with comments back to the original tweeter (e.g. Agreed! RT: “original tweet”), which we’ll call a RT with conversation. Thirty nine percent (39%) of #SCIO13 day 3 tweets were retweets. Of those, 11% (300 of 2652) were a RTs with conversation. The top five retweeters were Bora Zivkovic (Twitter: @boraz) with 63 retweets, Janet Stemwedel (Twitter: @docfreeride) with 50 retweets, Chris Pires (Twitter: @jchrispires) with 42 retweets, Matthew Francis (Twitter: @drmrfrancis) with 38 retweets, and Adrienne Roehrich (Twitter: @fiainros) with 37 retweets.

#SCIO13 Day 3 tag cloud of tweets

As I’ve written before, I’ve always had a thing for tag clouds. I find tag clouds a useful visual representation of data; if they’re designed right, tag clouds can also look really good.

From 120,655 words extracted from the #scio13 day 3 tweets, 9,193 were unique. I calculated the frequency of all 9,193 words and then “cleaned” the data, removing all words less than 4 characters, numbers, and words that were either common words (such as “that”, “from”, “with” or “have”) or gibberish (consisting principally of url strings from shared links).

The top 100 terms were then imported into Wordle and a weighted tag cloud was generated. Feel free to download any of the files below and reshare.

 Day 2

Here are the top 10 terms from the tag cloud above:

Rank Term Frequency
1 science 768
2 chemophobia 530
3 session 459
4 people 428
5 onoffline 302
6 online 269
7 great 268
8 venn13 260
9 scigames 258
10 like 258

All data and images are avaliable for download: low-resolution image, high-resolution image or raw data set of 100 terms with frequencies.

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Visualizing ScienceOnline 2013 Tweets Day Two http://www.walterjessen.com/science/visualizing-scienceonline-2013-tweets-day-two/ http://www.walterjessen.com/science/visualizing-scienceonline-2013-tweets-day-two/#comments Sat, 02 Feb 2013 07:36:19 +0000 http://www.walterjessen.com/?p=7066 Twitter analytics from Day 2 of ScienceOnline2013.

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Yesterday was the second day of ScienceOnline 2013. ScienceOnline is a non-profit organization that facilitates conversations, community and collaborations at the intersection of Science and the Web. They do this through online networks, projects and face-to-face events (both global and grassroots).

ScienceOnline’s preeminent annual meeting takes place in North Carolina every January. I did an analysis of Twitter data for day one of ScienceOnline 2013 and wanted to do the same for day two.

First, some statistics

The tweets were collected between 12:00am and 11:59pm on Friday, February 1st. In total, there were 6,363 tweets (10% more than day one) consisting of 112,693 words from 1,302 people. Just like #scio13 day one, the top tweeter was Janet Stemwedel (Twitter: @docfreeride) with 138 tweets! Here are the top 10 Twitter users and their number of tweets:

Rank Twitter user Number of tweets
1 @docfreeride 138
2 @erinpodolak 105
3 @drmrfrancis 103
4 @fiainros 99
5 @boraz 98
6 @jenedavison 84
7 @haleybridger 76
8 @jacquelyngill 70
9 @davidmanly 68
10 @nerdychristie 64

Most conference attendees using Twitter actually tweeted very little: 1,135 people (87%) tweeted less than 10 times, ninety-six people tweeted 10-20 times, 57 people tweeted 21-49 times, and 14 people tweeted over 50 times.

Let’s focus on retweets. Retweets are a way of saying “yeah, I like that” or “I agree!” and for the purposes of this analysis come in two forms: they can be a simple rebroadcast (e.g. RT: “original tweet”), which we’ll call a RT without conversation, or a retweet with comments back to the original tweeter (e.g. Agreed! RT: “original tweet”), which we’ll call a RT with conversation. Forty percent (40%) of #SCIO13 day 2 tweets were retweets. Of those, 12% (301 of 2559) were a RTs with conversation. The top five retweeters were Bora Zivkovic (Twitter: @boraz) with 76 retweets, Janet Stemwedel (Twitter: @docfreeride) with 46 retweets, Matthew Francis (Twitter: @drmrfrancis) with 43 retweets, Chris Pires (Twitter: @jchrispires) with 29 retweets, and Genegeek (Twitter: @genegeek) with 28 retweets.

#SCIO13 Day 2 tag cloud of tweets

As I’ve written before, I’ve always had a thing for tag clouds. I find tag clouds a useful visual representation of data; if they’re designed right, tag clouds can also look really good.

From 112,693 words extracted from the #scio13 day 2 tweets, 9,444 were unique. I calculated the frequency of all 9,444 words and then “cleaned” the data, removing all words less than 4 characters, numbers, and words that were either common words (such as “that”, “from”, “with” or “have”) or gibberish (consisting principally of url strings from shared links).

The top 100 terms were then imported into Wordle and a weighted tag cloud was generated. Feel free to download any of the files below and reshare.

ScienceOnline2013 Day 2

Here are the top 10 terms from the tag cloud above:

Rank Term Frequency
1 science 928
2 explain 495
3 people 398
4 session 360
5 bababrinkman 286
6 scienceonline 248
7 like 247
8 good 237
9 great 220
10 data 217

All data and images are avaliable for download: low-resolution image, high-resolution image or raw data set of 100 terms with frequencies.

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Visualizing ScienceOnline 2013 Tweets Day One http://www.walterjessen.com/science/visualizing-scienceonline-2013-tweets-day-one/ http://www.walterjessen.com/science/visualizing-scienceonline-2013-tweets-day-one/#comments Fri, 01 Feb 2013 15:04:01 +0000 http://www.walterjessen.com/?p=7042 Twitter analytics from Day 1 of ScienceOnline2013.

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Yesterday was the first day of ScienceOnline 2013. ScienceOnline is a non-profit organization that facilitates conversations, community and collaborations at the intersection of Science and the Web. They do this through online networks, projects and face-to-face events (both global and grassroots).

ScienceOnline’s preeminent annual meeting takes place in North Carolina every January. I’ve been archiving all the tweets from this year’s conference. Yesterday, I tweeted out simple stats on Twitter usage over the course of the day. Last year I created a tag cloud of ScienceOnline2012 tweets and decided this year I would do the same for each day of the conference.

First, some statistics

The tweets were collected between 12:00am and 11:59pm on Thursday, January 31st. In total, there were 5,752 tweets consisting of 102,843 words from 1,333 people. The top tweeter was Janet Stemwedel (Twitter: @docfreeride) with 124 tweets! Here are the top 10 Twitter users and their number of tweets:

Rank Twitter user Number of tweets
1 @docfreeride 124
2 @jacquelyngill 117
3 @jenedavison 117
4 @erinpodolak 89
5 @louwoodley 76
6 @fiainros 70
7 @laurawheelers 70
8 @whysharksmatter 58
9 @talksciencetome 56
10 @drugmonkeyblog 55

Most conference attendees using Twitter actually tweeted very little: 1,187 people (89%) tweeted less than 10 times, ninety-five people tweeted 10-20 times, 41 people tweeted 21-49 times, and 10 people (listed above) tweeted over 50 times.

Let’s focus on retweets. Retweets are a way of saying “yeah, I like that” or “I agree!” and for the purposes of this analysis come in two forms: they can be a simple rebroadcast (e.g. RT: “original tweet”), which we’ll call a RT without conversation, or a retweet with comments back to the original tweeter (e.g. Agreed! RT: “original tweet”), which we’ll call a RT with conversation. Over one-third (36%) of #SCIO13 tweets were retweets. Of those, 12% (219 of 1869) were a RTs with conversation. The top five retweeters were Chris Pires (Twitter: @jchrispires) with 42 retweets, Drug Monkey (Twitter: @drugmonkeyblog) with 41 retweets, Bora Zivkovic (Twitter: @boraz) with 34 retweets, Christie Wilcox (Twitter: @nerdychristie) with 31 retweets, and S. Bodman (Twitter: @sciwhat) with 27 retweets.

#SCIO13 Day 1 tag cloud of tweets

As I’ve written before, I’ve always had a thing for tag clouds. I find tag clouds a useful visual representation of data; if they’re designed right, tag clouds can also look really good.

From 102,843 words extracted from the #scio13 day 1 tweets, 8,445 were unique. I calculated the frequency of all 8,445 words and then “cleaned” the data, removing all words less than 4 characters, numbers, and words that were either common words (such as “that”, “from”, “with” or “have”) or gibberish (consisting principally of url strings from shared links).

The top 100 terms were then imported into Wordle and a weighted tag cloud was generated. Feel free to download any of the files below and reshare.

ScienceOnline2013 Day 1

Here are the top 10 terms from the tag cloud above:

Rank Term Frequency
1 science 1133
2 session 395
3 people 377
4 scibs 358
5 out 353
6 amp 320
7 scienceonline 297
8 sciosdm 278
9 uncertainty 276
10 myscistory 266

All data and images are avaliable for download: low-resolution image, high-resolution image or raw data set of 100 terms with frequencies.

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