The most influential data scientists on Twitter

Twitter is emerging as an important medium for determining influence in many fields. Social ranking sites like Klout and Traackr include Twitter as a heavily-weighted component of their ranking algorithms, for example. Twitter isn't representative of the members of any field, but in areas where the members primarily engage online, it can be a useful proxy. SocialFlow's Gilad Lotan has used an analysis of social networks on Twitter to rank influences in such fields, including the communities of python users and data scientists. Here's the network chart for data scientists:

Each circle represents a data scientists on Twitter (anyone with Data Science, Data Scientist, Machine Learning, Data Strategy or the like in their bio). The circle size is proportional to their influence during a week this past October. If you're active in the R or Data Science communities on Twitter, you'll recognize many of the names: Hilary Mason (@hmason) from bitly, Pete Skomoroch (@peteskomoroch) from LinkedIn; DJ Patil (@dpatil), author of Building Data Science Teams, and #rstats regulars Ryan Rosario (@datajunkie), John Myles White (@johnmyleswhite) and Mark Alen (@siah). (I'm down there in the bottom right corner: @revodavid.)

The colors represent connected clusters within the social graph, detected autonomously. With Hilary Mason's help, Gilad assigns meaning to these clusters:

Purple seems to be a mix of east coast and academics, while the dark blue is the west coast data drinking crew. Yellow looks like west coast social network folks while green have been doing it for a while. Although @BigDataBorat is identified within that segment… hmmm… The orange cluster is harder to nail down. Perhaps more academic, applied math and less tech-scene?

The analysis of the social network code was performed with Python, and the visualization was created with Gephi. You can find the details of how it was done, including slides and Python code, at the link below.

Gilad Lotan:  Mapping Twitter’s Python and Data Science Communities (via Quora)

Comments

[Andy]

No mention of @ogrisel, ruler of all twitter?

[Stephan Jäckel, Business Consultant]

Influencers do influence people, means they do change mindsets and opinions. What may first look attractive at least from a marketing point of view means that prospects, leads and sales generated this way do belong to easy to influence, meaning not very loyal, customers. Their customer life-cycle is going to be probably short and the ROI of winning them relatively low compared to loyal customers.

On the other hand from a management-perspective it will be of growing interest to find hidden-champions out there, people with bright and creative ideas who just have not been identified yet, those few or nobody knows of. This need applies to HR Management as well as Product Development and Marketing. Competition for brain power will grow and the competition for free thinkers little influenced by others with own clear visions will become decicive in business success - at least in my opinion.

[Ben]

I find it incredibly interesting that datapoints gathered from Twitter can also be used to a certain extent for studies and revealing visualizations. For example, watching trends of specific keywords used in people's tweets leading up to the election can give candidates an idea of their popularity, where they are being talked about most, favorable or unfavorably, etc.

I can see this style of impromptu research evolving with the development of more mass social networks.

[Dallas]

I'm surprised Nate Silver @fivethirtyeight isn't on here - or is that a different type of data analysis? I'd say he is the first data professional to reach mainstream rockstar status and influence.