@@ -95,6 +95,16 @@ but, according to research, fundamentally changes the nature of the encyclopedia
...
@@ -95,6 +95,16 @@ but, according to research, fundamentally changes the nature of the encyclopedia
% How: within the subsections? as a separate section?
% How: within the subsections? as a separate section?
Distinction filters/Bots: what tasks are handled by bots and what by filters (and why)? What difference does it make for admins? For users whose edits are being targeted?
Distinction filters/Bots: what tasks are handled by bots and what by filters (and why)? What difference does it make for admins? For users whose edits are being targeted?
Why is it important we study these mechanisms?
- their relative usage increases/has increased since they were first introduced
- not transparent, especially for new users (see~\cite{ForGei2012}: "As it is, Kipsizoo is not even
sure whether a real person who deleted the articles or a bot." )
- "inofficial", run and maintained by the community
- higher entry barriers: new users have to orientate themselves in the picture and learn to use the software
- gamification concerns (is fighting vandalism becoming a game where certain users aim to revert as many edits as possible in order to get a higher score; and as a consequence these same users often times enforce reverts more rigorously than recommended and also pick cases that are easy and fast to arbitrate and do not require much additional research)
- they change the system not only in matter of scale (using bots/tools is faster, hence more reverts are possible) but in matter of substance: how everything interacts with each other
- they enable efficient patrolling of articles by users with little to no knowledge about the particular contents (thanks to their representation of the edits/information: e.g. diffs)
\cite{GeiRib2010}
\cite{GeiRib2010}
Partial explanation why literature paid little attention to (semi-)automated tools up to this date:
Partial explanation why literature paid little attention to (semi-)automated tools up to this date:
- old data according to which bots accounted for a very little amount of edits (2-4%)
- old data according to which bots accounted for a very little amount of edits (2-4%)
...
@@ -154,6 +164,38 @@ According to research focusing on vandalism fighting, the amount/share/proportio
...
@@ -154,6 +164,38 @@ According to research focusing on vandalism fighting, the amount/share/proportio
\cite{GeiRib2010}
\cite{GeiRib2010}
Check Figure 1: Edits to AIV by tool (in the meantime 10 years old. is there newer data on the topic??)
Check Figure 1: Edits to AIV by tool (in the meantime 10 years old. is there newer data on the topic??)
not really, see:
\cite{Geiger2017}
"In the English-lan-
guage Wikipedia, 22 of the 25 most active editors (by
number of edits) are bot accounts, and July 2017, they
made about 20\% of all edits to encyclopedia articles."
Geiger's evidence:
https://quarry.wmflabs.org/query/20703
Percent of bot edits in previous month (enwiki, all pages)
is_bot edits Percentage of all edits
0 7619466 79.4974
1 1965083 20.5026
https://quarry.wmflabs.org/query/20704
Percent of bot edits in previous month (enwiki, articles only)
is_bot edits Percentage of all edits
0 4273810 80.2025
1 1054966 19.7975
However, a month is a relatively small period and you can't make an argument about general trends based on it.
For instance, these same quarries ran on April 12, 2019 render following results:
https://quarry.wmflabs.org/query/35104
Percent of bot edits in previous month (enwiki, all pages)
is_bot edits Percentage of all edits
0 6710916 89.7318
1 767948 10.2682
https://quarry.wmflabs.org/query/35105
Percent of bot edits in previous month (enwiki, articles only)
