diff --git a/thesis/2-Background.tex b/thesis/2-Background.tex
index 9bc79ef32a96d1a7071a00a9456a8bfceb12d4f7..234972d600dd0d5ae8204ccc8cfe722e6239b4b0 100644
--- a/thesis/2-Background.tex
+++ b/thesis/2-Background.tex
@@ -43,26 +43,26 @@ In the following sections, we discuss the state of scientific knowledge (syn) on
 \section{Bots}
 \label{section:bots}
 
-According to literature, bots constitute the first line of defence against malicious edits~\cite{GeiHal2013}. %TODO but that's actually not true! edit filters are triggered first. Comment on this! -- tried to close the circle in conclusion of chapter 4
-They are also undoubtedly the quality control mechanism studied most in depth by the scientific community.
+According to literature, bots constitute the first ``line of defence'' against malicious edits~\cite{GeiHal2013}. %TODO but that's actually not true! edit filters are triggered first. Comment on this! -- tried to close the circle in conclusion of chapter 4
+They are also undoubtedly the quality control mechanism studied most in-depth by the scientific community.
 
 Geiger and Ribes~\cite{GeiRib2010} define bots as
 ``fully-automated software
 agents that perform algorithmically-defined tasks involved
-with editing, maintenance, and administration in Wikipedia.''
+with editing, maintenance, and administration in Wikipedia''.
 
 %TODO revise
 Different aspects of bots and their involvement in quality control(syn!) have been investigated:
-In the paper referenced above, Geiger and Ribes employ their method of trace ethnography (more on it in chapter~\ref{chap:methods}) to follow a disrupting editor around Wikipedia and comprehend the measures taken/applied in collaboration by bots (ClueBot and HBC AIV helperbot7) as well as humans using semi-automated tools (Huggle and Twinkle) up until they achieved that the malicious editor in question was banned~\cite{GeiRib2010}.
+In the paper referenced above, the researchers employ their method of trace ethnography (more on it in chapter~\ref{chap:methods}) to follow a disrupting editor around Wikipedia and comprehend the measures taken/applied in collaboration by bots (ClueBot and HBC AIV helperbot7) as well as humans using semi-automated tools (Huggle and Twinkle) up until they achieved that the malicious editor in question was banned~\cite{GeiRib2010}.
 Halfaker and Riedl offer a historical review of bots and semi-automated tools and their involvement in vandal fighting~\cite{HalRied2012} assembling a comprehensive list of tools and commenting/touching on/discussing/studying their work principle (syn!) (rule vs machine learning based).
 They also develop a bot taxonomy we will come back to in chapter~\ref{chap:overview-en-wiki}. %TODO quote bot taxonomy here?
-In~\cite{GeiHal2013}, Geiger and Halfaker conduct an indepth analysis of ClueBot NG, ClueBot's machine learning based successor, and its place within Wikipedia's vandal fighting infrastructure~\cite{GeiHal2013} concluding that quality control on Wikipedia is a robust process and most malicious edits eventually get reverted even if some of the actors (syn!) are inactive, although at a different speed.
-They discuss the mean times to revert of different mechanisms, their observations co-inciding (check spelling) with diagram~\ref{fig:funnel-no-filters},
+In~\cite{GeiHal2013}, Geiger and Halfaker conduct an in-depth analysis of ClueBot NG, ClueBot's machine learning based successor, and its place within Wikipedia's vandal fighting infrastructure~\cite{GeiHal2013} concluding that quality control on Wikipedia is a robust process and most malicious edits eventually get reverted even if some of the actors (syn!) are inactive, although at a different speed.
+They discuss the mean times to revert of different mechanisms, their observations coinciding with diagram~\ref{fig:funnel-no-filters},
 and also comment on the (un)realiability of external infrastructure bots rely upon (run on private computers, which causes downtimes).
 
-Further bots involved in vandal fighting discussed by the literature include (besides ClueBot~\cite{GeiRib2010} and ClueBot NG~\cite{GeiHal2013}, \cite{HalRied2012},):
+Further bots involved in vandal fighting (besides ClueBot~\cite{GeiRib2010} and ClueBot NG~\cite{GeiHal2013}, \cite{HalRied2012},) discussed by the literature include :
 XLinkBot (which reverts edits containing links to domains blacklisted as spam)~\cite{HalRied2012},
-HBC AIV Helperbots (responsible for various maintenance tasks which help to keep entries on the Administrator Intervention against Vandalism (AIV) dashboard up-to-date)~\cite{HalRied2012}, \cite{GeiRib2010},
+HBC AIV Helperbots (responsible for various maintenance tasks which help to keep entries on the Administrator intervention against vandalism (AIV) dashboard up-to-date)~\cite{HalRied2012}, \cite{GeiRib2010},
 MartinBot and AntiVandalBot (one of the first rule-based bots which detected obvious cases of vandalism)~\cite{HalRied2012},
 DumbBOT and EmausBot (which do batch cleanup tasks)~\cite{GeiHal2013}.