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|
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.
Here are the top 10 terms from the tag cloud above:
Walter Jessen is a digital strategist, writer, web developer and data scientist. You can typically find him behind the screen something with an internet connection.