diff --git a/thesis/2-Background.tex b/thesis/2-Background.tex
index a17f5e3252ebbcbb8a932dd4dbcb99df3bc4565f..0f89dded3dcd96f7f9f7a8a2115db54caee5e450 100644
--- a/thesis/2-Background.tex
+++ b/thesis/2-Background.tex
@@ -95,6 +95,16 @@ but, according to research, fundamentally changes the nature of the encyclopedia
 % 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?
 
+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}
 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%)
@@ -154,6 +164,38 @@ According to research focusing on vandalism fighting, the amount/share/proportio
 
 \cite{GeiRib2010}
 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)
+is_bot	edits	Percentage of all edits
+0	    3426624	92.1408
+1	    292274	7.8592
 
 \subsection{Semi-automated tools}
 
diff --git a/thesis/references.bib b/thesis/references.bib
index 788ec38b1b060331193208b5bfb74bbeda345c68..3fff9cb2c809604a88deb9689b318b59a22e47e4 100644
--- a/thesis/references.bib
+++ b/thesis/references.bib
@@ -13,6 +13,15 @@
   volume = {4}
 }
 
+@inproceedings{ForGei2012,
+  title = {Writing up rather than writing down: Becoming Wikipedia literate},
+  author = {Ford, Heather and Geiger, R Stuart},
+  booktitle = {Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration},
+  pages = {16},
+  year = {2012},
+  organization = {ACM}
+}
+
 @article{Geiger2017,
   author = {Geiger, R Stuart},
   title = {Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture},