Strange duplicate behavior from GROUP_CONCAT of two LEFT JOINs of GROUP_BYs

Your second query is of the form:

q1 -- PK user_id
LEFT JOIN (...
    GROUP BY user_id, t.tag
) AS q2
ON q2.user_id = q1.user_id 
LEFT JOIN (...
    GROUP BY user_id, c.category
) AS q3
ON q3.user_id = q1.user_id
GROUP BY -- group_concats

The inner GROUP BYs result in (user_id, t.tag) & (user_id, c.category) being keys/UNIQUEs. Other than that I won't address those GROUP BYs.

TL;DR When you join (q1 JOIN q2) to q3 it is not on a key/UNIQUE of one of them so for each user_id you get a row for every possible combination of tag & category. So the final GROUP BY inputs duplicates per (user_id, tag) & per (user_id, category) and inappropriately GROUP_CONCATs duplicate tags & categories per user_id. Correct would be (q1 JOIN q2 GROUP BY) JOIN (q1 JOIN q3 GROUP BY) in which all joins are on common key/UNIQUE (user_id) & there is no spurious aggregation. Although sometimes you can undo such spurious aggregation.

A correct symmetrical INNER JOIN approach: LEFT JOIN q1 & q2--1:many--then GROUP BY & GROUP_CONCAT (which is what your first query did); then separately similarly LEFT JOIN q1 & q3--1:many--then GROUP BY & GROUP_CONCAT; then INNER JOIN the two results ON user_id--1:1.

A correct symmetrical scalar subquery approach: SELECT the GROUP_CONCATs from q1 as scalar subqueries each with a GROUP BY.

A correct cumulative LEFT JOIN approach: LEFT JOIN q1 & q2--1:many--then GROUP BY & GROUP_CONCAT; then LEFT JOIN that & q3--1:many--then GROUP BY & GROUP_CONCAT.

A correct approach like your 2nd query: You first LEFT JOIN q1 & q2--1:many. Then you LEFT JOIN that & q3--many:1:many. It gives a row for every possible combination of a tag & a category that appear with a user_id. Then after you GROUP BY you GROUP_CONCAT--over duplicate (user_id, tag) pairs and duplicate (user_id, category) pairs. That is why you have duplicate list elements. But adding DISTINCT to GROUP_CONCAT gives a correct result. (Per wchiquito's comment.)

Which you prefer is as usual an engineering tradeoff to be informed by query plans & timings, per actual data/usage/statistics. input & stats for expected amount of duplication), timing of actual queries, etc. One issue is whether the extra rows of the many:1:many JOIN approach offset its saving of a GROUP BY.

-- cumulative LEFT JOIN approach
SELECT
   q1.user_id, q1.user_name, q1.score, q1.reputation,
    top_two_tags,
    substring_index(group_concat(q3.category  ORDER BY q3.category_reputation DESC SEPARATOR ','), ',', 2) AS category
FROM
    -- your 1st query (less ORDER BY) AS q1
    (SELECT
        q1.user_id, q1.user_name, q1.score, q1.reputation, 
        substring_index(group_concat(q2.tag  ORDER BY q2.tag_reputation DESC SEPARATOR ','), ',', 2) AS top_two_tags
    FROM
        (SELECT 
            u.id AS user_Id, 
            u.user_name,
            coalesce(sum(r.score), 0) as score,
            coalesce(sum(r.reputation), 0) as reputation
        FROM 
            users u
            LEFT JOIN reputations r 
                ON    r.user_id = u.id 
                  AND r.date_time > 1500584821 /* unix_timestamp(DATE_SUB(now(), INTERVAL 1 WEEK)) */
        GROUP BY 
            u.id, u.user_name
        ) AS q1
        LEFT JOIN
        (
        SELECT
            r.user_id AS user_id, t.tag, sum(r.reputation) AS tag_reputation
        FROM
            reputations r 
            JOIN post_tag pt ON pt.post_id = r.post_id
            JOIN tags t ON t.id = pt.tag_id
        WHERE
            r.date_time > 1500584821 /* unix_timestamp(DATE_SUB(now(), INTERVAL 1 WEEK)) */
        GROUP BY
            user_id, t.tag
        ) AS q2
        ON q2.user_id = q1.user_id 
        GROUP BY
            q1.user_id, q1.user_name, q1.score, q1.reputation
    ) AS q1
    -- finish like your 2nd query
    LEFT JOIN
    (
    SELECT
        r.user_id AS user_id, c.category, sum(r.reputation) AS category_reputation
    FROM
        reputations r 
        JOIN post_category ct ON ct.post_id = r.post_id
        JOIN categories c ON c.id = ct.category_id
    WHERE
        r.date_time > 1500584821 /* unix_timestamp(DATE_SUB(now(), INTERVAL 1 WEEK)) */
    GROUP BY
        user_id, c.category
    ) AS q3
    ON q3.user_id = q1.user_id 
GROUP BY
    q1.user_id, q1.user_name, q1.score, q1.reputation
ORDER BY
    q1.reputation DESC, q1.score DESC ;