As I’ve done for another conference, I collected tweets for this year’s Experimental Biology conference with a Python script that I wrote and played around with them in R. I could not attend but it was great to follow along through twitter. Check out David Despain’s roundup post for nutrition coverage.
Here is the spreadsheet of 5,455 tweets starting on 4/18 at 4:21PM EST through 4/28 at 6:34PM EST: link. This is obviously more days than the conference, but if anyone wants to do any analysis you can cut out what you wish. There was one unfortunate period of about an hour on 4/23 between ~10:30AM and 11:30AM EST when my computer decided to BSOD and therefore tweets weren’t collected.
I was able to figure out how to find and download images in the tweets and automatically upload them to a tumblr here. There were 271 photos so it is a cool way to view the conference through others’ eyes. I will post my code eventually when I clean it up.
This is what the tweet frequency looked like over time:
Some people include their location in their profiles, so I ran those through google maps and retrieved lat/long coordinates. Here is a USA map of the 628 that google could interpret (darker = multiple people from that location):
The mean number of tweets for each person tweeting to the hashtag was 6.3 (SD = 21). A few people tweeting a lot skewed the average (median = 1):
Here are the top 19 tweeters. Impressive numbers:
|Handle||# of tweets (includes retweeting others)|
And the most popular tweets:
|Tweet||# Times Retweeted|
|RT @molecular: Muscle repair after injury helped by fat-forming cells t.co/Skf3Q8RpFp [article] #molecular #EB2013||20|
|RT @biochembelle: #EB2013 #APSadvocacy Schatteman”If we based policy on science, the world would be a different place. We base policy on em||13|
|RT @clin_sci: A new look for our website! RT & amp; follow @clin_sci for a chance to win a Kindle Fire. (Visit us at #EB2013 Booth 511 @expb||13|
|RT @daviddespain: Caffeine assoc with slowed cognitive decline, alcohol with faster decline in Baltimore long. study of aging – M. Bedouin||13|
|RT @SciTriGrrl: Love the “this is what a scientist looks like” t-shirt, But ironic there are mens & amp; kids but NO WOMEN’S sizes. It matte||13|
|A new look for our website! RT & amp; follow @clin_sci for a chance to win a Kindle Fire. (Visit us at #EB2013 Booth 511 @expbio)||12|
|RT @daviddespain: Vijay Ganji: “D didn’t used to be a vitamin; was a hormone until we started driving cars, spending more time indoors watc||11|
|RT @ASBMB: to be clear… #EB2013 is still “ON” Please RT||10|
|RT @expbio: #EB2013 Business as usual. Registration opens at 7:00AM on Saturday, April 20. Visit t.co/21NjuF8D6z for updates!||10|
|Going to #EB2013? Stop by LI-COR Booth 261 and register to win a C-DiGit Blot Scanner for Chemiluminescent Western Blots!||9|
|RT @ASBMB: #EB2013 – Executive Committee of EB meeting now and an official statement is forthcoming||9|
|RT @daviddespain: Is long-term calorie restriction in humans worth it? t.co/3sumErQDUK My post from #EB2013 with prelim findings fro||9|
|RT @Laelaps: Any dinosaur fans at #EB2013? On Tuesday, at 7PM, I’ll be at @HarvardBooks to talk about My Beloved Brontosaurus t.co/U||9|
|RT @ScritchfieldRD: Q2 #EB2013 Research shows eating high protein bfast increases satiety and may help2avoid unhealthy snacking t.co||9|
|RT @biochembelle: #EB2013 MT @BostonLogan: The airport is open and operating. Cabs are coming to and from the airport.||8|
|RT @molecular: Seeking a career in Bioscience? Ask the experts your questions during #EB2013, just include #BioscienceQA at end of tweet.||8|
|RT @nutsci: YESx1000 MT @ILSI_NA: Should journals issue peer-reviewed press releases to improve media reporting of science? D Allison propo||8|
|RT @daviddespain: Just a spoonful of sugary drink confusion t.co/VO1IwFqIuV my new post from @nutritionorg’s #sugarshowdown at #EB20||7|
|RT @daviddespain: Willett: ratio of n-3/n-6 is completely unsupported by evidence and “doesn’t make any sense” #EB2013||7|
I am not sure how useful this is but here is a word cloud of words appearing in tweets at least 100 times:
This plots each person’s tweets over time and orders them by the first tweet so we can see the rate of people joining over time. It is fairly steady but clearly people who tweeted first to the hashtag tended to tweet the most.
I may do some deeper analyses of the tweets in the future, let me know if you have requests in the comments.