wip

text/3_method.tex

@@ -15,10 +15,18 @@ StackExchange introduced a \emph{new contributor} indicator to all communities o
 
 %TODO state plots of sec 5 here and why these were chosen
 % -> also limitierungen, andere faktoren
+This thesis investigates the following criteria to determine whether the change affected a community positively or negatively, or whether the community is largly unaffected:
+\begin{itemize}
+ \item \textbf{Sentiment of answers to a question}. This symbolizes the quality of communication between different individuals. Better values indicate better communication. Through the display of the \emph{new contributor} indicator, the answerer should react less negatively towards the new user if they behave outside the community standards.
+ \item \textbf{Vote score of questions}. This is similar to the sentiment criterion. Voters will likely vote more postively (not voting instead of down voting, or upvoting instead of of not voting) due to the \emph{new contributor}. Thereby the vote score should increase after the change.
+ \item \textbf{The amount of first and follow-up question}. This symbolizes the willingness of users to participate in the community. Higher amounts of first questions indicate higher number of new participating users. Higher follow-up questions indicate that users are more willing to stay within the community.
+\end{itemize}
+If these criteria improve after the change is introducted, the community is affected positively. If they worsen, the community is affected negatively. If the criteria stay largely the same, then the community is unaffected. A question may receive answers and votes after the \emph{new contributor} indicator is no longer shown and therefore they are not considered as part of the data set to analyze.
+%only when new contributor insicator is shown
 
 
 %TODO more vader explanation
-To measure the effectiveness of the change this thesis utilizes Vader, a sentiment analysis tool with exceptional performance in analysing and categorizing microblog-like texts as well as good generalization in other domains \cite{hutto2014vader}. The choice is based on the speed and simplicity of Vader. Vader uses a lexicon of words with attached sentiment values and rules related to grammar and syntax to determine a sentiment value between -1 and 1 to a given piece of text. The sentiment range is divided into 3 classes: negative (-1 to -0.05), neutral (-0.05 to 0.05), and positive (0.05 to 1). The outer edges of the value space are rarely reached as the text would have to be extremely negative or positive which is very unlikely. This design allows fast and verifiable analysis.
+To measure the effect on sentiment of the change this thesis utilizes Vader, a sentiment analysis tool with exceptional performance in analysing and categorizing microblog-like texts as well as good generalization in other domains \cite{hutto2014vader}. The choice is based on the speed and simplicity of Vader. Vader uses a lexicon of words with attached sentiment values and rules related to grammar and syntax to determine a sentiment value between -1 and 1 to a given piece of text. The sentiment range is divided into 3 classes: negative (-1 to -0.05), neutral (-0.05 to 0.05), and positive (0.05 to 1). The outer edges of the value space are rarely reached as the text would have to be extremely negative or positive which is very unlikely. This design allows fast and verifiable analysis.
 
 % sentiment calculation via vaderlib, write whole paragraph and explain, also add ref to paper \cite{hutto2014vader}
 

todo2

@@ -10,7 +10,7 @@
 - DONE alle sentiment methoden + vader
 
 3.
-- DONEXT argumente warum ich genau diese variablen (sentiment, votes, #questions)
+- DONE argumente warum ich genau diese variablen (sentiment, votes, #questions)
 - DONEXT limitierungen, andere faktoren
 - DONEXT vader genau beschreiben