Who Do The Science Literati Listen to on Twitter?
I really shouldn’t have got distracted by this today, but I did; via Owen Stephens: seen altmetric – tracking social media & other mentions of academic papers (by @stew)?
Monthly Altmetric data downloads of tweets containing mentions of published articles are available for download from Buzzdata, so I grabbed the September dataset, pulled out the names of folk sending the tweets, and how many paper mentioning tweets they had sent from the Unix command line:
cut -d ',' -f 3 twitterDataset0911_v1.csv | sort |uniq -c | sort -k 1 -r > tmp.txt
Read this list into a script, pulled out the folk who had sent 10 or more paper mentioning updates, grabbed their Twitter friends lists and plotted a graph using Gephi to see how they connected (nodes are coloured according to a loose grouping and sized according to eigenvector centrality):
My handle for this view is that is shows who’s influential in the social media (Twitter) domain of discourse relating to the scientific topic areas covered by the Altmetric tweet collection I downloaded. To be included in the graph, you need have posted 10 or more tweets referring to one or more scientific papers in the collection period.
We can get a different sort of view over trusted accounts in the scientific domain by graphing the network of all the friends of (that is, people followed by) the people who sent 10 or more paper referencing tweets in September, as collected by altmetric, edges going from altmetric tweeps to all their friends. This is a big graph, so if we limit it to show folk followed by 100 or more of the folk who sent paper mentioning tweets and display those accounts, this is what we get:
My reading of this one is that it show folk who are widely trusted by folk who post regular updates about scientific papers in particular subject areas.
Hmmm… now, I wonder: what else might I be able to do with the Altmetric data???
PS Okay – after some blank “looks”, here’s the method for the first graph:
1) get the September list of tweets from Buzzdata that contain a link to a scientific paper (as determined by Altmetric filters);
2) extract the Twitter screen names of the people who sent those tweets.
3) count how many different tweets were sent by each screen name.
4) extract the list of screen-names that sent 10 or more of the tweets that Altmetric collected. This list is a list of people who sent 10 or more tweets containing links to academic papers. Let’s call it ‘the September10+ list’.
5) for each person on the September10+ list, grab the list of people they follow on Twitter.
6) plot the graph of social connections between people on the Septemeber10+ list.
Okay? Got that?
Here’s how the second graphic was generated.
a) take the September10+ list and for each person on it, get the list of all their friends on Twitter. (This is the same as step 5 above).
b) Build a graph as follows: for each person on the September10+ list, add a link from them to each person they follow on Twitter. This is a big graph. (The graph in 6 above only shows links between people on the September10+ list.)
c) I was a little disingenuous in the description in the body of this post… I now filter the graph to only show nodes with degree of 100 or more. For folk who are on the September10+ list, this means that the sum of the people on the September10+ list, and the total number of people they follow is equal to or greater than 100. For folk not on the September10+ list, this means that they are being followed by people with a degree of 100 or more who are on the September10+ list (which is to say they are being followed by at least 100 or so people on the September10+ list; I guess there could be folk followed by more than 100 people on the September10+ list who don’t appear in the graph if, for example, they were followed by folk in the original graph who predmoninantly had a degree of less than 100?).
d) to plot word cloud graphic above, I visualise the filtered graph and then hide the nodes whose in-degree is 0 (that is, they aren’t followed by anyone else in the graph).
Got that? Simples… ;-)