This notebook contains an analysis of the thanks given and received by different types of editors (those with high average edit counts vs those with low average edit counts)

SQL Queries

Thanks by year

use nowiki_p;

select A.rev_user as ID, A.num_edits as Edits, coalesce(B.num_thanks, 0) as Thanks

from (select user_id, user_name from user) as C

join (select rev_user, rev_user_text, count(rev_user) as num_edits from revision where rev_timestamp < timestamp('2018-06-01') and rev_timestamp >= timestamp('2017-06-01') and rev_user != 0 group by rev_user order by count(rev_user)) as A on A.rev_user = C.user_id

left join (select log_user_text, count(log_user_text) as num_thanks from logging_userindex where log_action = 'thank' and log_type='thanks' and log_timestamp < timestamp('2018-06-01') and log_timestamp >= timestamp('2017-06-01') group by log_user_text) as B on B.log_user_text = C.user_name or B.log_user_text = A.rev_user_text

order by Edits;

Note: log_user_text is a username (not an ID) which could produce some inaccuracy. I decided to use log_user_text because there's no ID representation of log_title, and I wanted to be consistent with my thanks given and thanks received data

Note: We use fourteen languages for this study (three more than the eleven language sample from studies 1-4).

Note: The data below will either be for thanks given or thanks received depending on the filenames definition near the top of the notebook


Although the most active editors both send and receive the most thanks, they have the lowest thanks to edits ratios (they send or receive fewer thanks with respect to their edit count).