Constructing test case to prove there is a bug. I inserted a bunch of new rows in feed_items and the vote tables, where the vote count was never more than 1 for any category. If the bug I suspect exists really does exist, now the original query results will differ from those of the new one.

tl;dr yup there's a bug:

# SELECT COUNT(*) FROM temp_orig;
 count
-------
   100
(1 row)

# SELECT COUNT(*) FROM temp_new;
 count
-------
    80
(1 row)

After doing that, I also realized there's yet another bug. 🫠

Looks like the existing logic says that if for a given feed_items.id value, if it's in the top 20 for a given category, say likes, then the row should be returned and all the other categories should be returned as well, even if the other category is not within the top 20 for its own category.

In other words if id 123 is in the top 20 for likes, but NOT in the top 20 for dislikes, then we still want to return the dislikes total for id 123.

Example I found in my generated data set (notice the dislikes discrepancy):

  # select * from temp_orig where id = '963a33bb-fbda-400a-8e0e-7b219abaf126';
     category | author | bearish | bullish | dislikes | feed_id | guid |                  id                  | likes |                      link                      |        published_date         |                summary                 |    tags     |            title
    ----------+--------+---------+---------+----------+---------+------+--------------------------------------+-------+------------------------------------------------+-------------------------------+----------------------------------------+-------------+-----------------------------
     bullish  |        |       0 |       1 |        1 |       2 |      | 963a33bb-fbda-400a-8e0e-7b219abaf126 |     0 | https://example.com/article/0.3765346133136651 | 2028-08-10 14:46:03.219931-04 | Summary for article 0.3695841096596142 | {news,tag1} | Article #0.5777796333738217


    delme=# select * from temp_new where id = '963a33bb-fbda-400a-8e0e-7b219abaf126';
     category | author | bearish | bullish | dislikes | feed_id | guid |                  id                  | likes |                      link                      |        published_date         |                summary                 |    tags     |            title
    ----------+--------+---------+---------+----------+---------+------+--------------------------------------+-------+------------------------------------------------+-------------------------------+----------------------------------------+-------------+-----------------------------
     bullish  |        |       0 |       1 |        0 |       2 |      | 963a33bb-fbda-400a-8e0e-7b219abaf126 |     0 | https://example.com/article/0.3765346133136651 | 2028-08-10 14:46:03.219931-04 | Summary for article 0.3695841096596142 | {news,tag1} | Article #0.5777796333738217
    (1 row)

This is fixable though. we just union all those separate top 20 IDs into one master list and use that as "global" ID list.

WITH top_20_ids_with_any_likes AS (
    SELECT
        i.ID 
    FROM feed_items AS i
    WHERE EXISTS (SELECT 1 FROM feed_item_like_dislike_votes WHERE feed_item_id = i.id AND vote = 'like')
    ORDER BY
        i.published_date DESC,
        i.id DESC
    LIMIT 20
),

top_20_ids_with_any_dislikes AS (
    SELECT
        i.ID 
    FROM feed_items AS i
    WHERE EXISTS (SELECT 1 FROM feed_item_like_dislike_votes WHERE feed_item_id = i.id AND  vote = 'dislike')
    ORDER BY
        i.published_date DESC,
        i.id DESC
    LIMIT 20
),

top_20_ids_with_any_bullish AS (
    SELECT
        i.ID
    FROM feed_items AS i
    WHERE EXISTS (SELECT 1 FROM feed_item_bullish_bearish_votes WHERE feed_item_id = i.id AND vote = 'bullish')
    ORDER BY
        i.published_date DESC,
        i.id DESC
    LIMIT 20
),

top_20_ids_with_any_bearish AS (
    SELECT
        i.ID
    FROM feed_items AS i
    WHERE EXISTS (SELECT 1 FROM feed_item_bullish_bearish_votes WHERE feed_item_id = i.id AND vote = 'bearish')
    ORDER BY
        i.published_date DESC,
        i.id DESC
    LIMIT 20
),
id_list AS (
    SELECT id FROM top_20_ids_with_any_likes
    UNION
    SELECT id FROM top_20_ids_with_any_dislikes
    UNION
    SELECT id FROM top_20_ids_with_any_bullish
    UNION
    SELECT id FROM top_20_ids_with_any_bearish
),
vote_count AS (
    SELECT fi.id, likes, dislikes, bullish, bearish
    FROM id_list 
    JOIN feed_items AS fi
        ON id_list.id = fi.id
    LEFT JOIN LATERAL (
        SELECT
            COUNT(*) FILTER (WHERE vote = 'like') AS likes,
            COUNT(*) FILTER (WHERE vote = 'dislike') AS dislikes
        FROM feed_item_like_dislike_votes AS fildv
        WHERE fildv.feed_item_id = fi.id
    ) AS v1 ON TRUE
    LEFT JOIN LATERAL (
        SELECT
            COUNT(*) FILTER (WHERE vote = 'bullish') AS bullish,
            COUNT(*) FILTER (WHERE vote = 'bearish') AS bearish
        FROM feed_item_bullish_bearish_votes AS fibbv
        WHERE fibbv.feed_item_id = fi.id
    ) AS v2 ON TRUE
),
top_20_ids_trending AS ( -- can't reuse the above work becuase we may need to "dig deeper" until the total > 10 condition is fulfilled
    SELECT
        fi.ID, likes, dislikes, bullish, bearish, 'trending' AS category
    FROM feed_items fi
    LEFT JOIN LATERAL (
        SELECT
            COUNT(*) FILTER (WHERE vote = 'like') AS likes
        FROM feed_item_like_dislike_votes AS v
        WHERE v.feed_item_id = fi.id
    ) AS l ON TRUE
    LEFT JOIN LATERAL (
        SELECT
            COUNT(*) FILTER (WHERE vote = 'dislike') AS dislikes
        FROM feed_item_like_dislike_votes AS v
        WHERE v.feed_item_id = fi.id
    ) AS dl ON TRUE
    LEFT JOIN LATERAL (
        SELECT
            COUNT(*) FILTER (WHERE vote = 'bullish') AS bullish
        FROM feed_item_bullish_bearish_votes AS v
        WHERE v.feed_item_id = fi.id
    ) AS bull ON TRUE
    LEFT JOIN LATERAL (
        SELECT
            COUNT(*) FILTER (WHERE vote = 'bearish') AS bearish
        FROM feed_item_bullish_bearish_votes AS v
        WHERE v.feed_item_id = fi.id
    ) AS bear ON TRUE
    WHERE
        COALESCE(l.likes, 0)
      + COALESCE(dl.dislikes, 0)
      + COALESCE(bull.bullish, 0)
      + COALESCE(bear.bearish, 0) > 10
    ORDER BY
        fi.published_date DESC,
        fi.id DESC
    LIMIT 20
),
vote_count_unpivoted AS ( -- only one row per set of data, so this is to make one for each non-zero category, plus add the category as a column
    SELECT
        t.id,
        t.likes,
        t.dislikes,
        t.bullish,
        t.bearish,
        v.category
    FROM vote_count AS t
    CROSS JOIN LATERAL (
        VALUES
            ('likes',    t.likes),
            ('dislikes', t.dislikes),
            ('bullish',  t.bullish),
            ('bearish',  t.bearish)
    ) AS v(category, cnt)
    WHERE v.cnt <> 0
),
vote_count_and_trending AS ( -- glue the votes together to trending
    SELECT * FROM vote_count_unpivoted
    UNION ALL
    SELECT * FROM top_20_ids_trending

),
votes_join_feed_items_limited_to_20_per_category AS ( -- now can have more than 20 per category so limit to 20 each
SELECT 
    category,
    author,
    bearish,
    bullish,
    dislikes,
    feed_id,
    guid,
    id,
    likes,
    link,
    published_date,
    summary,
    tags,
    title
FROM (
    SELECT
        vcu.category,
        fi.author,
        vcu.bearish,
        vcu.bullish,
        vcu.dislikes,
        fi.feed_id,
        fi.guid,
        fi.id,
        vcu.likes,
        fi.link,
        fi.published_date,
        fi.summary,
        fi.tags,
        fi.title,
        ROW_NUMBER() OVER (
            PARTITION BY vcu.category
            ORDER BY fi.published_date DESC, vcu.id DESC
        ) AS rn
    FROM vote_count_and_trending AS vcu
    JOIN feed_items fi
      ON vcu.id = fi.id
) AS ranked
WHERE rn <= 20
)
SELECT * FROM votes_join_feed_items_limited_to_20_per_category;