Who are the Most Influential Data Scientists on Twitter?

In this post, we will attempt to analyse whether the number of followers on Twitter is representative of how much influence a data scientist has on the subject. We will use the list provided by BigData-MadeSimple to conduct a study to itemize the rankings and see if there is any, if at all, relationship between the number of followers and “influence.”  A copy of the most influential data scientists ranked by the number of followers can be downloaded here. Although there are many lists of top data scientists on Twitter, many of them have overlaps. For instance, the list here has a very concise summary of the most influential data scientists on Twitter and Quora, where they perform a Twitter network cluster analysis to determine  the most influential data scientists on Twitter. Although the list includes significant data scientists and figures in the industrial world, as well as prominent scientists in academia, we will restrict ourselves in this post to the items in the industry list as of July 5, 2014.

An interesting part of the list is the inclusion of the entity “Kaggle.” While it does not represent a personality, we thought it would be an interesting academic study to include Kaggle in the list as a “data science influencer.” On its website, Kaggle defines itself as a “platform for predictive modeling competitions and consulting. We’re making data science into a sport.”

In this article, we will try to investigate how using Majestic’s metrics compares to employing ranking of data scientists by the number of followers on Twitter.

 Rankings based on MajesticSEO’s Metrics

Below, we provide a comparison of the original list sorted by number of followers and compare it with the rankings using Majestic’s Trust Flow ranked by order of decreasing Trustflow. An Excel file containing the final classification can be downloaded here.

Original Order sorted by Number of Followers Original Twitter Handle Majestic Ranking
1  @hmason  @bigdata
2  @SebastianThrun  @kdnuggets
3  @analyticbridge  @analyticbridge
4  @kdnuggets  @datatelling
5  @bigdata  @hmason
6  @AndrewYNg  @mich8elwu
7  @dpatil  @AndrewYNg
8  @peteskomoroch  @peteskomoroch
9  @hackingdata  @KirkDBorne
10  @kaggle  @cutting
11  @data_nerd  @octonion
12  @KirkDBorne  @John4man
13  @octonion  @data_nerd
14  @revodavid  @revodavid
15  @cutting  @dpatil
16  @mathena  @drewconway
17  @drewconway  @josh_wills
18  @mrogati  @johnmyleswhite
19  @John4man  @jakeporway
20  @DataJunkie  @SebastianThrun
21  @johnmyleswhite  @ogrisel
22  @mich8elwu  @furukama
23  @ogrisel  @posco
24  @jakeporway  @hackingdata
25  @mza  @gutelius
26  @furukama  @mrogati
27  @josh_wills  @DataJunkie
28  @siah  @kaggle
29  @posco  @siah
30  @datatelling  @fhuszar
31  @gutelius  @mza
32  @aweigend  @aweigend
33  @fhuszar  @aheineike
34  @aheineike  @mathena
35  @wahalulu  @wahalulu

A snapshot of the rankings based on Majestic’s Trust Flow metric is displayed in the figure below:


1: bigdata; 2: kdnuggets; 3: analyticbridge

It may be noted that “Business” and “Computers” dominate Majestic’s Topical Trust Topics for the top three ranked items.


This study provides a different and more versatile methodology for measuring the influence of a Twitter profile based on the number and the quality of websites that link to a particular profile. A comprehensive account of the methodology employed in this posting can be found in this  Forbes article.

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