Popular Posts: Type A vs. Type B

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A statistical approach to separating Type A and Type B popular posts.

Physics

Q&A for active researchers, academics and students of physics

-- Type A Popular Posts:    (Variance Index > 50)
--   Type A's are those that have received a lot of attention in a short
--   amount of time due to becoming "hot" at some point in its history.
--   Type A's are often linked on Reddit, Hacker News, SE Multicollider, etc...

--   Type A's are generally "interesting" or "fun" questions.
--   They are upvoted because they are "awesome", "cool", or "funny".


-- Type B Popular Posts:    (Variance Index < 20)
--   Type B's are those that slowly and steadily receive traffic over time.
--   Type B's get nearly all their trafffic from a steady source such as:
--   Search Engines, top questions list, or Jon Skeet's profile.

--   The vast majority of Type B's are generally less interesting, but are very
--   useful to developers who come across the same problem in the question.
--   They are usually upvoted because they are "useful".


-- Posts that are both Type A and Type B will likely to have a variance index
-- somewhere between 20 and 50.


-- The "Variance Index" is computed as:
--   index = 100 * (Stddev(upvotes received for each UTC day*) + 1) / sqrt(total upvotes)

-- *Days where no upvotes are received are not counted.
-- *Standard Deviations with fewer than 30 days of data are normalized.


declare @minDataSize numeric = 30

DECLARE @endDate date
SELECT  @endDate = max(CreationDate) from Posts;

WITH rawData(type,pid,uid,age,score,vMax,vSum,vNStd)
AS(
SELECT
    (CASE tmp.posttype
        WHEN 1 THEN 'Q'
        WHEN 2 THEN 'A'
     END),
    tmp.[Post Link],
    tmp.[User Link],
    tmp.[Age],
    tmp.score,
    MAX(tmp.dVotes),
    SUM(tmp.dVotes),
    CASE
    -- If more than 30 days of votes, use standard deviation.
    WHEN COUNT(tmp.dVotes) >= @minDataSize THEN STDEVP(tmp.dVotes) + 1
    -- If less than 30 days of data, use some sort of zero-padded standard deviation.
    ELSE
        SQRT(
            (
                VARP(tmp.dVotes) * COUNT(tmp.dVotes)
                - SQUARE(MAX(tmp.dVotes) - AVG(tmp.dVotes + 0.))
                + SQUARE(MAX(tmp.dVotes) - SUM(tmp.dVotes + 0.) / @minDataSize)
                + SQUARE(SUM(tmp.dVotes + 0.) / @minDataSize) * (@minDataSize - COUNT(tmp.dVotes))
            ) / @minDataSize
        ) + 1
    END
FROM

-- Subquery forked from:
-- http://data.stackexchange.com/stackoverflow/query/108188/upvotes-per-post-and-per-day
    (SELECT
      p.PostTypeId AS posttype,
      v.PostId AS [Post Link],
      p.OwnerUserId AS [User Link],
      v.CreationDate AS [Date],
      DATEDIFF(DAY,p.CreationDate,@endDate) AS [Age],
      Count(*) AS dVotes,
      p.score AS score
    FROM Votes v
    LEFT JOIN Posts p ON p.Id = v.PostId
    WHERE
        v.VoteTypeId = 2 and p.PostTypeId is not null
    GROUP BY
        v.PostId,
        v.CreationDate,
        p.CreationDate,
        p.OwnerUserId,
        p.PostTypeId,
        p.Score
    ) AS tmp
    
WHERE tmp.score > 0
GROUP BY
    tmp.[Post Link],
    tmp.posttype,
    tmp.[User Link],
    tmp.score,
    tmp.[Age]
)

SELECT TOP 1000
    type AS [ ],
    pid AS [Post Link],
    uid AS [User Link],
    age AS [Age],
    score AS [Score],
    vMax AS [Vote Spike],
    CAST(vNStd / SQRT(vSum) * 100 AS DECIMAL(20,5)) AS [Variance Index],
    CAST(vNStd AS DECIMAL(20, 3)) AS [Type A],
    CAST(score / vNStd AS DECIMAL(20, 3)) AS [Type B]
FROM rawData
ORDER BY [Score] DESC

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