Visualizing ScienceOnline 2013 Tweets Day Three

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.

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.

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