Quantifying Thoughtfulness: Relationship between important Hubski user data reveals comment popularity rank misrepresentation.  

By Cadell Last (Hubski user: theadvancedapes)


Hubski is a quickly growing social network and link aggregating service. The site focuses on promoting thoughtful discussion regarding a diverse number of topics including (but not limited to) music, politics, science, technology, history, space, and economics. As of January 7th 2013, top trending tags within the Hubski community included #music, #politics, #news, #science, and #technology.

I have personally been a member of (under the name: theadvancedapes) since May of 2012. I discovered the site after the creator of Hubski (mk) wrote an article that was in some sense a response or comment on a blog post I had composed regarding the morality of Martian colonization. Since joining the site, it has steadily grown in total users, and monthly active users. However, that statement is based on qualitative experience; a quantitative analysis of this growth will not be included in this report.

On January 6th, 2013, a post by Hubski user JakobVirgil regarding Hubski follower popularity was released (link: Within this article Jakob demonstrated trends in Hubski follower statistics over time. Jakob found that there was extreme stratification in follower levels that have persisted over time. However, Jakob also concluded that there has been dramatic changes to this stratification, most notably, a increased disparity between those with the top followers and the rest of the Hubski community. No specific mention of the time elapsed was included in the document. Although this article gave useful insight into the changing dynamic of Hubski followers over time, I still think further analyses should be conducted with more rigorous data collection methods employed, to better understand follower disparity and trends.

Study and Methods

Since I do not currently have access to temporal data, this analysis will not seek to further elaborate on Jakob’s analysis. In this article I will seek to understand the relationships between important Hubski user variables, in order to understand whether the current rating system adequately represents (and advertises) the “most thoughtful” contributors on the website. This will be done by aggregating data on total number of badges, total number of followers, overall popularity rank, and overall comment popularity rank. Data were collected on January 7th, 2013 between 12:01 A.M. EST – 12:23 A.M. EST) from Data collected was publicly available, and permission to run this analysis was given by both mk and thenewgreen. Significance of the relationship between badge total, follower total, popularity rank, and comment rank were calculated using Pearson rank correlation and run on SPSS (Statistical Package for the Social Sciences). Data were also graphed using a simple scatter plot and a scatter plot matrix. These graphs were also constructed using SPSS.


Pearson rank correlation test between badge total, follower total, popularity rank, and comment rank were conducted:

Badge total was significantly related to follower total (Pearson: n = 59, P = .000) and overall popularity rank (Pearson: n = 59, P = .000). This was true cross-directionallyand between follower and popularity rank (Pearson: n = 59, P = .000) (Fig. 1, 2).

However, comment rank was not significantly related to total badges (Pearson: n = 59,P = .206), total followers (Pearson: n = 59, P = .235), or popularity rank (Pearson: n = 59, P = .352) (Fig. 1, 2).

Figure 1: Pearson rank correlation between important Hubski user variables


Figure 2: Relationship between important Hubski user variables



Below: two specific examples of Hubski data: Figure 3 represents not significant relationship between badge total and comment popularity; Figure 4 represents significant relationship between badge total and number of followers:






These data reveal an important misrepresentation of comment popularity rank on Hubski as of January 7th, 2013. Although it seems likely that the most popular Hubski users are adequately ranked, the most popular commenters, and consequently, likely the most thoughtful contributors on Hubski are not properly ranked. In the long-termthis may create a few issues on Hubski for its growing userbase:

1)New Hubski users do not have a useful list of the most thoughtful commenters on the site. As a consequence, the most thoughtful commenters will have a reduced opportunity to acquire new followers. Also, new users may not invest as much time and energy into thoughtfully contributing in the comments section, since it will not significantly improve their overall rank on

2)Individuals that consistently receive Hubski’s most prestigious award (being badged for a thoughtful comment or contribution) do not see any growth in their overall comment popularity rank. (side note: this is what I expect; although to be sure data would have to be collected for the number of badges received specifically for a comment (as opposed to the data currently collected which is a badge total aggregate for any badge received) and correlated with comment popularity rank).

3)Individuals that do not comment often (and/or individuals that are new to the site), will likely have a higher overall comment popularity rank, than those who comment frequently and have been members for a longer period of time. It may be important to create a new section/list for most popular *new* users that have commented under a certain number of times.

In the future, I think it would be useful if Hubski modified its current comment rank system to better list the commenters that the Hubski community finds the most thoughtful. Since Hubski is designed to generate thoughtful comments and discussion, this could be re-worked as a “thoughtfulness index” of some kind. This would help new users discover and follow the people that are most frequently rewarded for their contribution to the sites discussion-based formula. I believe that this approach would also encourage users to comment and discuss more, as opposed to simply posting articles without sharing their thoughts or further elaborating on an idea. It may also encourage people to consider badging individuals for comments, as opposed to just posts.

Important Links and Comments:

Popularity of Hubski users shows stratification. January 6th, 2013. (Hubski user: JakobVirgil) (Website:

If you would like to contact Cadell Last you can find him on Hubski (username: theadvancedapes), or contact him via email ( or on his website (

Data (in excel spreadsheet format) is available upon request.  PDF of this document is also available upon request.


Last, C. 2013. Quantifying Thoughtfulness: Relationship between important Hubski user data reveals comment popularity rank misrepresentation. Hubski Report (, 1: 1-6.



Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s