Who Are The Top Biotech Influencers on Twitter?

About six months ago, Andrew Maynard (@2020Science) and Mike Chelen (@MikeChelen) analyzed the influence of the then 400 ‘Scientwists’ on Twitter compiled by David Bradley (@sciencebase). Since then, I’ve wanted to do something similar for Biotech (Biotwech?) Twitterers, but the application they used no longer works in batch.

Mike Chelen came to my rescue and created an API which will work in batch for Klout, a similar application which gives Twitter influence along with other metrics and classifications for user types. Using the list of users from the Biotech Twibe, including the top 25 influencers from the Wefollow Biotech tag, I created the bubble chart visualization using the ‘Many Eyes’ application. The size of each users’ bubble, which is directly proportional to their influence, is based on the Klout score, which ranges from 0 to 100 and is based on 25 metrics including the size of your ‘engaged’ network, retweets, and number of Twitter lists.

You can view the full Twitter Biotech Influence data set to see more details, and even interact more with the visualization as well. Those of us who are in the Twitter space will not be surprised at most who are in the list of the top twenty, but there are also some interesting observations to be made. Because Twibes are not based on the content a user tweets, there can be some spurious results. For example, while Wise_Diva is certainly influential, she rarely tweets about Biotech, but since it is one of her interests, she joined the Twibe.

Someday soon, hopefully we’ll have applications which will take tweet content into the equation, and we’ll be able to find the key influencers based on keywords (currently, on Klout, if you log in and go to the ‘dashboard’ you can search for influencers on some topics–biology and pharmacy seem to be the closest matches). Klout does list the content of a users’ tweets, but using this information directly may not accurately portend their tweet content. For example, Sally Church aka @MaverickNY is the top ‘true’ biotech influencer, but her content is listed as cancer, FDA, Google, iPhone, Boston, and social media. We do know, however, that she tweets often about Biotech, but just does not use the term. (And, would you move to Boston already Sally since it’s clear you love the place?) So, likely the content algorithms will need to be very smart, or we’ll need to do some curation. Also, you should consider that if one of your goals is to be an influencer for a certain keyword, that you should use the keyword in your tweets, as you will not always be able to count on human curation.

Another interesting trend is the fact that the top influencers are categorized by Klout as personas, activists, and connectors. While the Klout website doesn’t describe how these categorizations are made, the descriptions they give are listed in the table below. The Network Score, Amplification Score, and True Reach values were capricious, with several being zero for many accounts, so we did not analyze them (but you can visualize them on the Many Eyes site using the menu on the lower left). Klout appeared to be better than tools like Wefollow at discerning between active and inactive accounts, as the latter has at least two inactive accounts ranked highly, including @IamBiotech which is #1. However, some of the top ranking accounts, notably @sdbn at #14 which I manage, are partial bots (mine fed from RSS), and Klout seems to not discern that these accounts are not 100% ‘human.’

Another interesting observation is that 12 of the 16 top influencers’ ‘known’ genders are female, or 75%. Are women better at fostering microblogging networks? This finding is particularly interesting in light of the recent reports that science blogging networks have significantly more male than female bloggers. While it is tempting to speculate why, it should be noted that Twitter users are 60% female. However, it’s been shown that men are likely to get more followers.

Surely I’ve missed some biotech Tweeters, I thought about adding them separately, but my vestigial scientific tendencies told me that it would make the ‘experiment’ less reproducible (also why I left Wise_Diva in). The Twibes application is the only one I’ve found that will output a list of users–Wefollow and Twitter Lists do not. Please add yourself to the Biotech Twibe and I will redo this visualization in a month or so. I’d like also to make some tag clouds of all of the tweets, especially the influencers, but alas I can’t find a tool for that.

I’d like to give special recognition to Mike Chelen for creating the Klout API. Often I get frustrated with analyzing social media data, and the scientists who ‘get’ social media are really fantastic resources. Mike did this for us for free, but you should check out his Linkedin profile and consider hiring him for your project. The intersection of social media and science is one of the most fascinating aspects of my job, and I think exciting things are on the horizon. For a teaser, see how some scientists are using a Bioinformatics/statistics program to create word clouds of Pubmed abstracts. Of course, the Batch Klout API he developed can be used to analyze any list…have at it!

Finally, why is this study important for life science and biotech companies? Let’s say we’re able to find the top influencer for PCR, lung cancer, kinase research etc. These accounts, whether they’re individuals or brands, will be able to spread a message quickly and effectively. Of course, the issue of the ‘separation of church and state’ in media and advertising will still have a play, but we’ve talked about how this can be done carefully by companies. Indeed, Klout is already thinking about this for the larger brands, shouldn’t you be thinking about it for your life science brand?

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