Quantifying Information Scarcity

content distribution
I'll polish this up in July

Now to get to what this Tongclaw board is really about. I’ve been thinking for a long time, about how smart someone is (how much they know), not correlating to how much upvotes they get. I wouldn’t go as far to call Quora a popularity contest, as ones amount of followers isn’t exactly proportionately affecting their upvotes. There had to be another factor at play, to why some people with less answers, manage to get more upvotes than people with more answers and more followers.

One thing I have noticed, is that people in general are more likely to get upvotes in different categories. In fact, it is the topics with less knowledgeable people in them, of which people have more upvotes than them. People are much less likely to get upvotes in the Startups category, as they are in the Flowers category, so if they are going to, they better have some exclusive knowledge to add.

It is evident that the people on Quora who receive more upvotes on their answers, have more scarcity to their knowledge. The scarcity comes from less people on Quora knowing what they know, so if they were to leave, not much else of the remaining people would be able to take their place. Regarding the Quora community, their knowledge and viewpoints are more exclusive to them, than anyone else’s around them could ever be. Their information scarcity makes them a valuable contributive member to Quora, gain much more upvotes per answer, and leave a bigger gap to fill if they were to leave.

To work out how much information scarcity people have, the distribution-of-upvotes-among-answers would have to be worked out. Using Tongclaw, I have been able to work this metric out. To make the metric fair or more accurately, weighted, I have grouped the members into a cumulative frequency. Members are listed in the ascending order of answers they have.

The lower your number, the better you are!

Less than 100 answers
Renata Honorato – 0
Shad Pilgrim – 0
Claudia Sly – 1.6
Huo Ju – 0.48
Sheila Cruze – 4.12
Michaela Jayne – 0.74 (got more upvotes than answers)
Jennifer Li– 1.76
Matt Livesey – 0.51 (got more upvotes than answers)
Soussan Sabra – 0.81 (got more upvotes than answers)
Claire Elizabeth Carter – 0.42 (got more upvotes than answers)
Anthony Smith – 0.59 (got more upvotes than answers)
User – 0.30 (got more upvotes than answers)
Alexandra Pell 0.04 – (got more upvotes than answers)

(got more upvote than answer details, will be updated below, soon this week)

100 to 499 answers
Alex Regueiro – 1.27
Alton Sun – 1.96
Maria Suyay Videla – 1.40
Marianne Baker – 1.36
Hamza Alsbaihi – 1.95
Liz Mullen – 1.32
Tony Miller – 1.44
Jan Leadbetter – 1.45
Antonia Anni – 1.79
Ariel Williams – 1.22

500+ answers
User – 1.08
Charles Lyell – 1.23
Marie Stein – 1.15
Elizabeth L. Mead – 1.18
David Urquhart – 1.28
Mark Simchock – 2.00
Viola Yee – 1.71
Bevan Audstone  – 1.54
Lisa Galarneau – 1.77
Alex K. Chen – 1.38
Stephanie Vardavas – 1.36
Adisa Nicholson – 2.20

So what can we take from this data? That’s for you to figure out!

I believe that the people who got the lowest scores, are the most wisest. Knowledge is free, but wisdom is sacred. I also believe that those people are more popular within the Quora community, as well. Well you know what I say. No intelligent person ever got a career without talking their way into it.

For anyone about to ask how I used Tongclaw to gather the statistics, I can’t show you it because I’ve got stuck coding it. If you would like your own metric, reply to this post.