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\chapter*{Abstract} \chapter*{Abstract}
\label{cha:abstract} \label{cha:abstract}
StackExchange is a question and answer platform and like other social platforms, StackExchange is eager provide a good first impression to users. StackExchange made many decitions to attract new users. One of these decitions was to introduce the \emph{new contributor} indicator which is shown users that may answer a question from a new user. This thesis investigates whether this change improved the impression new users experience. To measure whether the change achieved its intended target, this thesis uses VADER to quantify the sentiment of the answers to questions of new contributors which are then used in an interupted time series. The results indicate that in some of the communities the change did indeed achieve its intented purpose. StackExchange is a question and answer platform and like other social platforms, StackExchange is eager to provide a good first impression to users. StackExchange made many decisions to attract new users. One of these decisions was to introduce the \emph{new contributor} indicator which is shown to users that may answer a question from a new user. This thesis investigates whether this change improved the impression, new users experience. To measure whether the change achieved its intended target, this thesis uses VADER to quantify the sentiment of the answers to questions of new contributors which are then used in an interrupted time series. The results indicate that in some of the communities the change did indeed achieve its intended purpose.
%This is a place-holder for the abstract. %This is a place-holder for the abstract.

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% write new numbers about so %TODO % write new numbers about so %TODO
In August of 2018, the StackExchange team introduced a small change which may have had a huge impact on the platform. They added a new feature to visibly mark questions from new contributors, as part of their effort to make the site more welcoming for new users \cite{post2018come}. Specifically, members who want to answer a question created by a new contributor are shown a notification in the answer box that this question is from a new contributor. The StackExchange team hopes that this little change encourages members to be more friendly and forgiving toward new users. In August of 2018, the StackExchange team introduced a small change that may have had a huge impact on the platform. They added a new feature to visibly mark questions from new contributors, as part of their effort to make the site more welcoming for new users \cite{post2018come}. Specifically, members who want to answer a question created by a new contributor are shown a notification in the answer box that this question is from a new contributor. The StackExchange team hopes that this little change encourages members to be more friendly and forgiving toward new users.
% write about the change investigated % write about the change investigated
% stackexchange new contriutor post: https://meta.stackexchange.com/questions/314287/come-take-a-look-at-our-new-contributor-indicator?cb=1 % stackexchange new contriutor post: https://meta.stackexchange.com/questions/314287/come-take-a-look-at-our-new-contributor-indicator?cb=1
% what did change intend? % what did change intend?
This thesis evaluates whether this change has a real impact on the community and if so in how the community reacts. For this analysis, this thesis utilizes Vader \cite{hutto2014vader}, a sentiment analysis tool, to measure the sentiments of the answers submitted to questions of new contributors. Furhermore, this thesis includes the votes these questions recieve and the number of questions new contributors ask. Interrupted time series are then applied to these values to evalutate whether the change achieved its purpose of making the platform more welcoming. This thesis evaluates whether this change has a real impact on the community and if so how the community reacts. For this analysis, this thesis utilizes Vader \cite{hutto2014vader}, a sentiment analysis tool, to measure the sentiments of the answers submitted to questions of new contributors. Furthermore, this thesis includes the votes these questions receive and the number of questions new contributors ask. Interrupted time series are then applied to these values to evaluate whether the change achieved its purpose of making the platform more welcoming.
% how is change investigated by this thesis % how is change investigated by this thesis
% vader library % vader library
This thesis investigates the ten largest communities of the StackExchange platform measured by number of posts. This includes most prominent communities, for instance, StackOverflow, MathOverflow, Math, AskUbuntu, and SuperUser as well as some lesser known communities. This thesis investigates the ten largest communities of the StackExchange platform measured by the number of posts. This includes most prominent communities, for instance, StackOverflow, MathOverflow, Math, AskUbuntu, and SuperUser as well as some lesser-known communities.
% write about other communities (e.g. i investigated) % write about other communities (e.g. i investigated)
@@ -30,7 +30,7 @@ This thesis investigates the ten largest communities of the StackExchange platfo
%write a bit about results %write a bit about results
%write about next chapters %write about next chapters
The remaining part of this thesis is structured as follows: Section 2 explains StackExchange, how it works, and shows related work. Section 3 shows the method this thesis uses for analysis in detail. Section 4 contains the investigated datasets. Results are presented in Section 5 and discussed in Section 6. Section 7 conculdes this thesis. The remaining part of this thesis is structured as follows: Section 2 explains StackExchange and its communities, how it works, and shows related work. Section 3 shows the method this thesis uses for analysis in detail. Section 4 contains the investigated datasets. Results are presented in Section 5 and discussed in Section 6. Section 7 concludes this thesis.

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All these communities differ in their design. Wikipedia is a community-driven knowledge repository and consists of a collection of articles. Every user can create an article. Articles are edited collaboratively and continually improved an expanded. Reddit is a platform for social interaction where users create posts and comment on other posts or comments. Quora, StackExchange, and Yahoo! Answers are community questions and answer (CQA) platforms. On Quora and Yahoo! Answers users can ask any question regarding any topics whereas on StackExchange users have to post their questions in the appropriate subcommunity, for instance, StackOverflow for programming related questions or MathOverflow for math related questions. CQA sites are very effective at code review \cite{treude2011programmers}. Code may be understood in the traditional sense of source code in programming related fields but this also translates to other fields, for instance, mathematics where formulas represent code. CQA sites are also very effective at solving conceptual questions. This is due to the fact that people have different areas of knowledge and expertise \cite{robillard1999role} and due to the large user base established CQA sites have, which again increases the variety of users with experise in different fields. All these communities differ in their design. Wikipedia is a community-driven knowledge repository and consists of a collection of articles. Every user can create an article. Articles are edited collaboratively and continually improved an expanded. Reddit is a platform for social interaction where users create posts and comment on other posts or comments. Quora, StackExchange, and Yahoo! Answers are community questions and answer (CQA) platforms. On Quora and Yahoo! Answers users can ask any question regarding any topics whereas on StackExchange users have to post their questions in the appropriate subcommunity, for instance, StackOverflow for programming related questions or MathOverflow for math related questions. CQA sites are very effective at code review \cite{treude2011programmers}. Code may be understood in the traditional sense of source code in programming related fields but this also translates to other fields, for instance, mathematics where formulas represent code. CQA sites are also very effective at solving conceptual questions. This is due to the fact that people have different areas of knowledge and expertise \cite{robillard1999role} and due to the large user base established CQA sites have, which again increases the variety of users with experise in different fields.
Despite the differences in purpose and manifestation of these communities, they are social communities and they have to follow certain laws. Despite the differences in purpose and manifestation of these communities, they are social communities and they have to follow certain laws.
In their book on ''Building successful online communities: Evidence-based social design`` \cite{kraut2012building} Kraut lie out five equally important criteria online platforms have to fulfill in order to thrive. 1) When starting a community, it has to have a critical mass of users who create content. StackOverflow already had a critical mass of users from the beginning due to the StackOverflow team already being experts in the domain \cite{mamykina2011design} and the private beta \cite{atwood2008stack}. Both aspects ensured a strong community core early on. In their book on ''Building successful online communities: Evidence-based social design`` \cite{kraut2012building} \citeauthor{kraut2012building} lie out five equally important criteria online platforms have to fulfill in order to thrive. 1) When starting a community, it has to have a critical mass of users who create content. StackOverflow already had a critical mass of users from the beginning due to the StackOverflow team already being experts in the domain \cite{mamykina2011design} and the private beta \cite{atwood2008stack}. Both aspects ensured a strong community core early on.
2) The platform must attract new users to grow as well as to replace leaving users. Depending on the type of community new users should bring certain skills, for example, programming background in open source software developement, or extended knowledge on certain domains; or qualities, for example, a certain illness in medical communities. New users also bring the challenge of onboarding with them. Most newcomers will not be familiar with all the rules and nuances of the community \cite{yazdanian2019eliciting, hanlon2018stack}. 3) The platform should encourage users to commit to the community. Online communities are often based on voluntary commitment of their users \cite{ipeirotis2014quizz}, hence the platform has to ensure users are willing to stay. Most platforms do not have contracts with their users, so users should see benefits for staying with the community. 4) Contribution by users to the community should be encouraged. Content generation and engagement are the backbone of an online community. 5) The community needs regulation to sustain it. Not every user in a community is interested in the wellbeing of the community. Therefore, every community has to deal with trolls and inappropriate or even destructive behavior. Rules need to be established and enforced to limit and mitigate the damage malicious users cause. 2) The platform must attract new users to grow as well as to replace leaving users. Depending on the type of community new users should bring certain skills, for example, programming background in open source software developement, or extended knowledge on certain domains; or qualities, for example, a certain illness in medical communities. New users also bring the challenge of onboarding with them. Most newcomers will not be familiar with all the rules and nuances of the community \cite{yazdanian2019eliciting, hanlon2018stack}. 3) The platform should encourage users to commit to the community. Online communities are often based on voluntary commitment of their users \cite{ipeirotis2014quizz}, hence the platform has to ensure users are willing to stay. Most platforms do not have contracts with their users, so users should see benefits for staying with the community. 4) Contribution by users to the community should be encouraged. Content generation and engagement are the backbone of an online community. 5) The community needs regulation to sustain it. Not every user in a community is interested in the wellbeing of the community. Therefore, every community has to deal with trolls and inappropriate or even destructive behavior. Rules need to be established and enforced to limit and mitigate the damage malicious users cause.
%new structure: %new structure:
@@ -109,7 +109,7 @@ One-day-flies may partially be a result of lurking. Lurking is consuming content
The StackOverflow team acknowledged the one-time-contributors trend \cite{hanlon2018stack, silge2019welcome} and took efforts to make the site more welcoming to new users \cite{friend2018rolling}. They lied out various reasons: Firstly, they have sent mixed messages whether the site is an expert site or for everyone. Secondly, they gave too little guidance to new users which resulted in poor questions from new users and in the unwelcoming behavior of more integrated users towards the new users. New users do not know all the rules and nuances of communication of the communities. An example is that ''Please`` and ''Thank you`` is not well received on the site as they are deemed unnecessary. Also the quality, clearness and language quality of the questions of new users is lower than more experienced users which leads to unwelcoming or even toxic answers and comments. Moreover, users who gained moderation tool access could close questions with predefined reasons which often are not meaningful enough for the poster of the question \cite{hanlon2013war}. Thirdly, marginalized groups, for instance, women and people of color \cite{hanlon2018stack, stackoversurvey2019, ford2016paradise}, are more likely to drop out of the community due to unwelcoming behavior from other users \cite{hanlon2018stack}. They feel the site is an elitist and hostile place. The StackOverflow team acknowledged the one-time-contributors trend \cite{hanlon2018stack, silge2019welcome} and took efforts to make the site more welcoming to new users \cite{friend2018rolling}. They lied out various reasons: Firstly, they have sent mixed messages whether the site is an expert site or for everyone. Secondly, they gave too little guidance to new users which resulted in poor questions from new users and in the unwelcoming behavior of more integrated users towards the new users. New users do not know all the rules and nuances of communication of the communities. An example is that ''Please`` and ''Thank you`` is not well received on the site as they are deemed unnecessary. Also the quality, clearness and language quality of the questions of new users is lower than more experienced users which leads to unwelcoming or even toxic answers and comments. Moreover, users who gained moderation tool access could close questions with predefined reasons which often are not meaningful enough for the poster of the question \cite{hanlon2013war}. Thirdly, marginalized groups, for instance, women and people of color \cite{hanlon2018stack, stackoversurvey2019, ford2016paradise}, are more likely to drop out of the community due to unwelcoming behavior from other users \cite{hanlon2018stack}. They feel the site is an elitist and hostile place.
The team suggested several steps to mitigate these problems. Some of these steps include appealing to the users to be more welcoming and forgiving towards new users \cite{hanlon2018stack, silge2019welcome, spolsky2012kicking}, other steps are geared towards changes to the platform itself: The \emph{Be nice policy} (code of conduct) was updated with feedback from the community \cite{jaydles2014the}. This includes: new users should not be judged for not knowing all things. Furthermore, the closing reasons were updated to be more meaningful to the poster, and questions that are closed are shown as ''on hold`` instead of ''closed`` for the first 5 days \cite{hanlon2013war}. Moreover, the team investigates how the comment sections can be improved to lessen the unwelcomeness and hostility and keep the civility up. The team suggested several steps to mitigate these problems. Some of these steps include appealing to the users to be more welcoming and forgiving towards new users \cite{hanlon2018stack, silge2019welcome, spolsky2012kicking}, other steps are geared towards changes to the platform itself: The \emph{Be nice policy} (code of conduct) was updated with feedback from the community \cite{jaydles2014the}. This includes: new users should not be judged for not knowing all things. Furthermore, the closing reasons were updated to be more meaningful to the poster, and questions that are closed are shown as ''on hold`` instead of ''closed`` for the first 5 days \cite{hanlon2013war}. Moreover, the team investigates how the comment sections can be improved to lessen the unwelcomeness and hostility and keep the civility up.
The StackOverflow team partnered with \citeauthor{ford2018we} and implemented the Mentorship Research Project \cite{ford2018we, hanlon2017mentorship}. The project lasted one month and aimed to help newcomers improve their first questions before they are posted publicly. The program went as follows: When a user is about to post a question the user is asked whether they want their question to be reviewed by a mentor. If they confirmed they are forward to a help room with a mentor who is an experienced user. The question is then reviewed and the mentor suggests some changes if applicable. These changes may include narrowing the question for more precise answers, adding a code example or adjusting code, or removing of \emph Please and \emph{Thank you} from the question. After the review and editing, the question is posted by publicly the user. The authors found that mentored questions are received significantly better by the community than non-mentored questions. The questions also received higher scores and were less likely to be off-topic and poor in quality. Furthermore, newcomers are more comfortable when their question is reviewed by a mentor. The StackOverflow team partnered with \citeauthor{ford2018we} and implemented the Mentorship Research Project \cite{ford2018we, hanlon2017mentorship}. The project lasted one month and aimed to help newcomers improve their first questions before they are posted publicly. The program went as follows: When a user is about to post a question the user is asked whether they want their question to be reviewed by a mentor. If they confirmed they are forward to a help room with a mentor who is an experienced user. The question is then reviewed and the mentor suggests some changes if applicable. These changes may include narrowing the question for more precise answers, adding a code example or adjusting code, or removing of \emph{Please} and \emph{Thank you} from the question. After the review and editing, the question is posted publicly by the user. The authors found that mentored questions are received significantly better by the community than non-mentored questions. The questions also received higher scores and were less likely to be off-topic and poor in quality. Furthermore, newcomers are more comfortable when their question is reviewed by a mentor.
For this project four mentors were hand selected and therefore the project would not scale very well as the number of mentors is very limited but it gave the authors an idea on how to pursue their goal of increasing the welcomingness on StackExchange. The project is followed up by a \emph{Ask a question wizard} to help new users as well as more experienced users improve the structure, quality, and clearness of their questions \cite{friend2018rolling}. For this project four mentors were hand selected and therefore the project would not scale very well as the number of mentors is very limited but it gave the authors an idea on how to pursue their goal of increasing the welcomingness on StackExchange. The project is followed up by a \emph{Ask a question wizard} to help new users as well as more experienced users improve the structure, quality, and clearness of their questions \cite{friend2018rolling}.

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% sentiment calculation via vaderlib, write whole paragraph and explain, also add ref to paper \cite{hutto2014vader} % sentiment calculation via vaderlib, write whole paragraph and explain, also add ref to paper \cite{hutto2014vader}
\section{Data gathering and preprocessing} \section{Data gathering and preprocessing}
StackExchange provides anonymized data dumps of all their communities for researchers to investigate at no cost on archive.org \cite{archivestackexchange}. These data dumps contain users, posts (questions and answers), badges, comments, tags, votes, and a post history containing all versions of posts. Each entry contains the necessary information, for instance, id, creation date, title, body, and how the data is linked together (which user posted a question/answer/comment). However, not all data entries are valid and therefore cannot be used in the analysis, for instance, questions or answers of which the user is unknown but this only affects a very small amount entries. So before the actual analysis, the data has to be cleaned. Moreover, the answer texts are in HTML format, containing tags that could skew the sentiment values, and they need to be stripped away beforehand. Additionally, answers may contain code sections which also would skew the results and are therefore omitted. StackExchange provides anonymized data dumps of all their communities for researchers to investigate at no cost on archive.org \cite{archivestackexchange}. These data dumps contain users, posts (questions and answers), badges, comments, tags, votes, and a post history containing all versions of posts. Each entry contains the necessary information, for instance, id, creation date, title, body, and how the data is linked together (which user posted a question/answer/comment). However, not all data entries are valid and therefore cannot be used in the analysis, for instance, questions or answers of which the user is unknown, but this only affects a very small amount entries. So before the actual analysis, the data has to be cleaned. Moreover, the answer texts are in HTML format, containing tags that could skew the sentiment values, and they need to be stripped away beforehand. Additionally, answers may contain code sections which also would skew the results and are therefore omitted.
% data sets as xml files from archive.org \cite{archivestackexchange} % data sets as xml files from archive.org \cite{archivestackexchange}
%cleaning data %cleaning data
% broken entries, missing user id % broken entries, missing user id
% answers in html -> strip html and remove code sections, no contribution to sentiment % answers in html -> strip html and remove code sections, no contribution to sentiment
After preprocessing the raw data, relevant data is filtered and computed. Questions and answers in the data are mixed together and have to be separated and answers have to be linked to their questions. Also, questions in these datasets do not have the \emph{new contributor} indicator attached to them and neither do users. So, the first contribution date and time of users have to be calculated via the creation dates of the questions and answers the user has posted. Then, questions are filtered per user and by whether they are created within the 7-day window after the first contribution of the user. These questions were created during the period where the \emph{new contributor} indicator would have been displayed, in case the questions had been posted before the change, or has been displayed after the change. From these questions, all answers which arrived within the 7-day window are considered for the analysis. Answers which arrived at a later point are excluded as the answerer most likely has not seen the disclaimer shown in figure \ref{newcontributor}. Included answers are then analyzed with Vader and the resulting sentiments are stored. Furhtermore, votes to questions of new contributors are counted if they arrived within the 7-day window and count 1 if it is an upvote and -1 if it is a downvote. Moreover, number of questions new contributors ask are counted and divided into two classes: 1st-question of a user and follow up questions of a new contributor. After preprocessing the raw data, relevant data is filtered and computed. Questions and answers in the data are mixed together and have to be separated and answers have to be linked to their questions. Also, questions in these datasets do not have the \emph{new contributor} indicator attached to them and neither do users. So, the first contribution date and time of users have to be calculated via the creation dates of the questions and answers the user has posted. Then, questions are filtered per user and by whether they are created within the 7-day window after the first contribution of the user. These questions were created during the period where the \emph{new contributor} indicator would have been displayed, in case the questions had been posted before the change, or has been displayed after the change. From these questions, all answers which arrived within the 7-day window are considered for the analysis. Answers which arrived at a later point are excluded as the answerer most likely has not seen the disclaimer shown in figure \ref{newcontributor}. Included answers are then analyzed with Vader and the resulting sentiments are stored. Furhtermore, votes to questions of new contributors are counted if they arrived within the 7-day window and count 1 if it is an upvote and -1 if it is a downvote. Moreover, number of questions new contributors ask are counted and divided into two classes: 1st-question of a user and follow-up questions of a new contributor.
% calc sentiment for answers % calc sentiment for answers
% questions do not have a tag if from a new contribtor -> calc first contributor % questions do not have a tag if from a new contribtor -> calc first contributor

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@@ -27,7 +27,7 @@ These datasets are selected due to their size as larger datasets yield more cons
%sections 1 per site %sections 1 per site
\section{StackOverflow.com} \section{StackOverflow.com}
StackOverflow is the largest and oldest community of the StackExchange platform. StackOverflow is a community about software development and programming knowledge and is the largest and oldest community of the StackExchange platform.
The community has 11867244 registered users of which 297192 were active between December 2019 and February 2020. The community has 11867244 registered users of which 297192 were active between December 2019 and February 2020.
Members asked 18699974 questions in total and gave 27981749 answers with an average answer density of 1.496 answers per question. Members asked 18699974 questions in total and gave 27981749 answers with an average answer density of 1.496 answers per question.
New users asked 2880039 questions with an average of 1.240 questions per new user during their first week after their first contribution. New users asked 2880039 questions with an average of 1.240 questions per new user during their first week after their first contribution.

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\chapter{Results} \chapter{Results}
%TODO some text here %TODO some text here
This section shows the results of the experiments described in section 3 on the data sets described in section 4. In the following diagrams, the blue line states the (a) average sentiment of the answers to questions from new contributors/(b) average vote score of questions from new contributors and (c) the number of 1st and follow up questions of new contributors. These lines also have numbers attached to it at every datapoint and shows (a) the number of answers that formed the sentiment average/(b) the number of questions that formed the average vote score. The orange line shows ITS analysis as a 3-segment line. This section shows the results of the experiments described in section 3 on the data sets described in section 4. In the following diagrams, the blue line states the (a) average sentiment of the answers to questions from new contributors, (b) average vote score of questions from new contributors, and (c) the number of 1st and follow-up questions of new contributors. These lines also have numbers attached to it at every data point and each shows (a) the number of answers that formed the sentiment average, and (b) the number of questions that formed the average vote score. The orange line shows ITS analysis as a 3-segment line.
% pvalues ... % pvalues ...
@@ -11,7 +11,7 @@ This section shows the results of the experiments described in section 3 on the
%TODO write some text to each result %TODO write some text to each result
\pagebreak \pagebreak
\section{StackOverflow.com} \section{StackOverflow.com}
StackOverflow shows a very slight decrease in average sentiment of time before the change had been introduced. When the change occured the average sentiment jumped up. After the change the sentiments reached higher levels and kept rising. The average vote score rose right before and stay fairly constant after the change. this indicates that the vote score was not affected by the change. However, the number of questions from new contributors increased after the change while prir to the change the was fairly constant. The number of follow up questions from new contributors were declinied prior to the change and rose after the change. StackOverflow shows a very slight decrease in the average sentiment of time before the change is introduced. When the change occurs the average sentiment jumps up. After the change, the sentiments reach higher levels and keep rising. The average vote score rises right before and stays fairly constant after the change. This indicates that the vote score is not affected by the change. However, the number of questions from new contributors increases after the change while before the change is fairly constant. The number of follow-up questions from new contributors declines before the change and rise after the change.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../stackoverflow.com/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../stackoverflow.com/output/its/average_sentiments-i1.png}
@@ -34,7 +34,7 @@ StackOverflow shows a very slight decrease in average sentiment of time before t
% sentiments rising after change % sentiments rising after change
\section{math.stackexchange.com} \section{math.stackexchange.com}
The math.stackexchange.com community shows a decrease in average sentiments, vote score, and number of questions prior to the change. The measurements make a small jump upward when the change is introduced, however, they continue their downward trend after the introduction of the change. Only the number of follow up questions stabilized and began increasing after the change. The math.stackexchange.com community shows a decrease in average sentiments, vote score, and the number of questions prior to the change. The measurements make a small jump upward when the change is introduced, however, they continue their downward trend after the introduction of the change. Only the number of follow-up questions stabilizes and begins to increase after the change.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../math.stackexchange.com/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../math.stackexchange.com/output/its/average_sentiments-i1.png}
@@ -56,7 +56,7 @@ The math.stackexchange.com community shows a decrease in average sentiments, vot
% sentiments falling faster than before the change % sentiments falling faster than before the change
\section{MathOverflow.net} \section{MathOverflow.net}
MathOverflow shows a constant regresssion before the change, however, average sentiments are low at about 10 months before the change and spiked high directly before the change. When the change is introduced regression makes a small jump up and decreases thereafter. The votes score steadily increased prior to the change and then quickly returned to the level from 3 years before the change. The number of 1st questions are falling prior the change and stabilized thereafter. This data set is sparse compared to the other datasets. Also the vote scores are high compared to other datasets. MathOverflow shows a constant regression before the change, however, average sentiments are low at about 10 months before the change and spike high directly before the change. When the change is introduced the regression makes a small jump up and decreases thereafter. The votes score steadily increases prior to the change and then quickly returns to the level from 3 years before the change. The number of 1st questions falls prior to the change and stabilizes thereafter. This data set is sparse compared to the other datasets. Also, the vote scores are high compared to other datasets.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../mathoverflow.net/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../mathoverflow.net/output/its/average_sentiments-i1.png}
@@ -78,7 +78,7 @@ MathOverflow shows a constant regresssion before the change, however, average se
% falling after the change % falling after the change
\section{AskUbuntu.com} \section{AskUbuntu.com}
AskUbuntu saw a decrease in average sentiments prior to the change. After the introduction of the change the regression dipped but sentiments keep rising drastically since then. The vote score has a huge range of values prior and after the change, hoewever the graph indicates the the vote score is declining after the change. The number of 1st questions slightly decreased prior the change and started rising after the change. AskUbuntu sees a decrease in average sentiments prior to the change. After the introduction of the change, the regression dips but sentiments keep rising drastically since then. The vote score has a huge range of values prior to and after the change, however, the graph indicates the vote score declines after the change. The number of 1st questions slightly decreases prior to the change and starts rising after the change.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../askubuntu.com/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../askubuntu.com/output/its/average_sentiments-i1.png}
@@ -101,7 +101,7 @@ AskUbuntu saw a decrease in average sentiments prior to the change. After the in
%maybe: sentiments did not change drastically as seen in maths communities %maybe: sentiments did not change drastically as seen in maths communities
\section{ServerFault.com} \section{ServerFault.com}
ServerFault shows gradually rising average sentiments prior to the change. At the time of the change the regession makes a jump upward and the average sentiment decrease slowly afterward. The vote score fell prior to the change, made a huge jump upward and quickly returned to the levels just prior to the change. The number of 1st questions, however, saw a drastic change. Prio to the change the number of 1st questions decreased steadily, while after the change the numbers increase at the same pace as they were falling prior to the change. The number of follow up questions also saw the same course direction, falling prior and raising after the change. ServerFault shows gradually rising average sentiments prior to the change. At the time of the change, the regression makes a jump upward and the average sentiment decreases slowly afterward. The vote score falls prior to the change, made a huge jump upward, and quickly returns to the levels just prior to the change. The number of 1st questions, however, sees a drastic change. Prior to the change, the number of 1st questions decreases steadily, while after the change the numbers increase at the same pace as they fall prior to the change. The number of follow-up questions also sees the same course direction, falling prior and raising after the change.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../serverfault.com/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../serverfault.com/output/its/average_sentiments-i1.png}
@@ -123,7 +123,7 @@ ServerFault shows gradually rising average sentiments prior to the change. At th
% small jump in avg sentiments at change date % small jump in avg sentiments at change date
\section{SuperUser.com} \section{SuperUser.com}
SuperUser shows only sightly decreasing average sentiment and vote score up to the change. At the change time the regressions take a dip down and the regression shows a downward trend after the change. Indeed the average sentiments and vote score dipped considerably when the change is introducted. The average sentiment recovers about 13 months later, while the vote score does not recover as well. The number of 1st questions are decrasing prior to the change and then went through the roof indicating a huge wave of new users. This drastic influx of new users may explain the crash of the average sentiment and vote score that occured at the same time. Data available in the future will show if the recovery is persistent. SuperUser shows only sightly decreasing average sentiment and vote score up to the change. At the change time the regressions take a dip down and the regression shows a downward trend after the change. Indeed the average sentiments and vote score dipped considerably when the change is introduced. The average sentiment recovers about 13 months later, while the vote score does not recover as well. The number of 1st questions decreases prior to the change and then goes through the roof indicating a huge wave of new users. This drastic influx of new users may explain the crash of the average sentiment and vote score that occurs at the same time. Data available in the future will show if the recovery is persistent.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../superuser.com/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../superuser.com/output/its/average_sentiments-i1.png}
@@ -146,7 +146,7 @@ SuperUser shows only sightly decreasing average sentiment and vote score up to t
% recovery after after 13 months to not quite the previous levels % recovery after after 13 months to not quite the previous levels
\section{electronics.stackexchange.com} \section{electronics.stackexchange.com}
On electronics.stackexchange.com the average sentiment and votes decrease continuously prior to the change. At the change date the regressions makes a little jump upward but the trend from before the change continues afterward. Similarly to SuperUser, the average sentiment recover at about 12 months after the change is introduced and future data will be necessary to determine if the recovery is persistent. The number of 1st questions rose continuously prior the the change and decreased thereafter. The number of follow up questions fell slightly prior to the change and stabilized afterward. On electronics.stackexchange.com the average sentiment and votes decrease continuously prior to the change. At the change date, the regression makes a little jump upward but the trend from before the change continues afterward. Similarly to SuperUser, the average sentiment recovers at about 12 months after the change is introduced and future data will be necessary to determine if the recovery is persistent. The number of 1st questions rises continuously prior to the change and decreases thereafter. The number of follow-up questions falls slightly prior to the change and stabilizes afterward.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../electronics.stackexchange.com/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../electronics.stackexchange.com/output/its/average_sentiments-i1.png}
@@ -169,7 +169,7 @@ On electronics.stackexchange.com the average sentiment and votes decrease contin
% more data in the future will be required to determine if upward trend in the end continues % more data in the future will be required to determine if upward trend in the end continues
\section{stats.stackexchange.com} \section{stats.stackexchange.com}
On stats.stackexchange.com the average sentiment is steadily decreasing prior to the change. The regression dips when the change is introduced. However, the average sentiment after the change indicate a slight upward trend. The vote score also decreased prior to the change but does not recover afterward. However, the number of 1st questions and follow up questions are raising prior to the change and increase even faster after the change. On stats.stackexchange.com the average sentiment decreases steadily prior to the change. The regression dips when the change is introduced. However, the average sentiment after the change indicates a slight upward trend. The vote score also decreases prior to the change but does not recover afterward. However, the number of 1st questions and follow-up questions rise prior to the change and increase even faster after the change.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../stats.stackexchange.com/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../stats.stackexchange.com/output/its/average_sentiments-i1.png}
@@ -192,7 +192,7 @@ On stats.stackexchange.com the average sentiment is steadily decreasing prior to
% sight upward trend after the change % sight upward trend after the change
\section{tex.stackexchange.com} \section{tex.stackexchange.com}
On tex.stackexchange.com the average sentiment is low compared to the other investigated data sets. Prior to the change the average sentiment only slightly decreases. When the change is introduced the regression takes a dip down and after the change the average sentiment increases drastically. Future data will be required to see if this upward trend continues or evens out. In stark contrast, the vote score shows a downward trend, although there is a short window around the change date where vote scores are higher compared to before and after change. The number of 1st questions have downward trend before the change and an upward trend afterward. The downward trend of the number of follow up questions is uninterrupted by the change. On tex.stackexchange.com the average sentiment is low compared to the other investigated data sets. Prior to the change the average sentiment only slightly decreases. When the change is introduced the regression takes a dip down and after the change, the average sentiment increases drastically. Future data will be required to see if this upward trend continues or evens out. In stark contrast, the vote score shows a downward trend, although there is a short window around the change date where vote scores are higher compared to before and after the change. The number of 1st questions has a downward trend before the change and an upward trend afterward. The downward trend of the number of follow-up questions is uninterrupted by the change.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../tex.stackexchange.com/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../tex.stackexchange.com/output/its/average_sentiments-i1.png}
@@ -216,7 +216,7 @@ On tex.stackexchange.com the average sentiment is low compared to the other inve
% trend after change strongly upward % trend after change strongly upward
\section{unix.stackexchange.com} \section{unix.stackexchange.com}
On unix.stackexchange.com the average sentiment is decreasing prior to the change. When the change is introduced the regression take a small dip down, however, the average sentiment increases fast after the change. The vote score shows continuous downward trend. The number of 1st and follow up questions are falling slightly prio to the change and increase afterward. On unix.stackexchange.com the average sentiment decreases prior to the change. When the change is introduced the regression takes a small dip down, however, the average sentiment increases fast after the change. The vote score shows a continuous downward trend and the number of 1st and follow-up questions fall slightly prior to the change and increase afterward.
\begin{figure}[H] \begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth} \begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../unix.stackexchange.com/output/its/average_sentiments-i1.png} \includegraphics[scale=0.37]{../unix.stackexchange.com/output/its/average_sentiments-i1.png}

11
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@@ -1,8 +1,9 @@
\chapter{Discussion} \chapter{Discussion}
The ITS analysis of the investigated communities show mixed results. Some communities show an increase in sentiment while others are not affected at all or show a decrease in sentiment. The StackOverflow community has a fairly stable average sentiment prior to the change. The average sentiment jumps into a higher level and keeps rising after the change is introducted. The change has a positive effect on the StackOverflow community. Beside StackOverflow, 4 other communities seem to profit from the change: AskUbuntu, stats.stackexchange.com, tex.stackexchange.com, and unix.stackexchange.com. AskUbuntu shows an interestion zig-zag pattern in the average sentiment graph. Also, the average sentiment is falling prior to the change and raising thereafter, indicating that the change worked for this community. On stats.stackexchange.com the average sentiment is falling prior to the change but since the change the downward trends stopped and the sentiment started to rise slowly, suggesting the change has a positive effect on the community. In the tex.stackexchange.com community sentiments are stable prior to the change and show a stark rising pattern after the change. The change seems to work for this community but future data will be neccessary to see if the rising pattern continues in the shown manner. unix.stackexchange.com also shows a decreasing pattern prior and a rising pattern after the change. So this community also profits from the change. The ITS analysis of the investigated communities shows mixed results. Some communities show an increase in sentiment while others are not affected at all or show a decrease in sentiment. The StackOverflow community has a fairly stable average sentiment before the change. The average sentiment jumps to a higher level and keeps rising after the change is introduced. Furthermore, the number of 1st questions from new contributors starts rising drastically after the change while prior levels stagnate. Also, the follow-up questions start increasing slightly. The votes score trend takes a new direction 9 months before the change and is unrelated to it. The change has a positive effect on the StackOverflow community. Beside StackOverflow, 5 other communities seem to profit from the change: AskUbuntu, ServerFault, stats.stackexchange.com, tex.stackexchange.com, and unix.stackexchange.com. AskUbuntu shows an interesting zig-zag pattern in the average sentiment graph. Also, the average sentiment falls before the change and raises thereafter, indicating that the change works for this community. However, further data is needed to see if the zig-zag pattern repeats itself. The number of 1st questions starts increasing again after the change stopping the downward trend before that. On stats.stackexchange.com the average sentiment falls before the change but since the change, the downward trend stops and the sentiment starts to rise slowly, suggesting the change has a positive effect on the community. This is supported by the increase in the number of 1st and followup questions by new contributors. The vote score takes a dip after the change but starts to recover after 12 months which could be the result of another change. In the tex.stackexchange.com community sentiments are stable before the change and show a stark rising pattern after the change. The change seems to work for this community but future data will be necessary to see if the rising pattern continues in the shown manner. The votes score ITS does not fit the model and values before and after the change indicate a linear downward trend. However, the number of 1st questions increases slightly after the change while the prior trend shows a decreasing development. unix.stackexchange.com also shows a decreasing pattern prior and a rising pattern after the change. The vote score analysis shows a fairly linear downward trend before and after the change and is not affected by it. However, the number of 1st questions by new contributors starts to drastically increase while before the change the levels are constant, indicating this community also profits from the change. On ServerFault the sentiment rises gradually before the change, jumps upward by a small value when the change is introduced and the sentiment falls slowly thereafter but the levels are pretty stable over the analyzed period. The vote scores show the change has a huge impact on the community. The previously decreasing trend jumps up by a large amount. However, the vote score rapidly returns to levels right before the change. Contrary, the number of first questions turns direction and starts increasing at the same rate it is falling previously.
The other communities do not seem to profit from the change directly. ServerFault is an example where the change does not have a significant impact. The sentiment rises gradually prior to the change, jumps upward by a small value when the change ist introduced and the sentiment is falling slowly thereafter. The data does not inidicate a significant rise or fall in the average sentiment, so this community seem to be largly unaffected by the change. MathOverflow, math.stackexchange.com, and electronics.stackexchange.com show similar results. The average sentiment stay constant on MathOverflow and are falling for math.stackexchange.com and electronics.stackexchange.com. After the change these communities see a decrease in sentiment. These communities seem to not profit from the change. However math.stackexchange.com has group below average sentiment values at the end which could be a result from another unknown influence. Also the average sentiment on electronics.stackexchange.com seem to recover after about 12 months and future data is required to see if the rise in the end is a long term trend. SuperUser shows a really odd pattern. The average sentiment is stable prior to the change and decreased dramatically shortly afterward. However the sentiment recovers after 12 months. The ITS model chosen in this thesis is not able to capture the apparent pattern. Future data will be necessary to see if the sentiment recovers long term. %~ - -
The other communities do not seem to profit from the change so clearly. The average sentiment stays constant on MathOverflow before the change and decreases afterward. However, the sentiment levels start increasing six months before the change and are unrelated, indicating the sentiment values are not particularly affected by the change. The vote score is steadily increasing before the change and the crashes down shortly after the change. However, the vote score is very high compared to other communities. The number of 1st questions stabilizes after the change compared to the slight downward previously. math.stackexchange.com shows a downward trend before and after the change for sentiment and vote score. The sentiment ITS is particularly affected by the low sentiment values at the end and future data is required to determine if this trend continues. However, the number of 1st questions stabilizes a bit after changes and follow up questions even see and a slight increase after the change. The electronics.stackexchange.com community has a similar pattern for the sentiment value and vote scores compared to math.stackexchange.com. However, the sentiment values seem to recover after about 12 months and future data is required to see if the rise at the end of the period is a long term trend. The rising number of first questions of new contributors stops at the change date and transition into a decreasing pattern. SuperUser shows an odd pattern. The average sentiment values and votes scores are stable before the change and decrease dramatically shortly afterward. However, the sentiment recovers after 12 months. The ITS model chosen in this thesis is not able to capture the apparent pattern. However, the number of 1st question skyrockets indicating a huge influx of new users. The time frames of the falling sentiment values and vote scores, and the rising number of first questions overlap, indicating the huge influx of new users is responsible for the falling patterns.
% similarities in results and differences % similarities in results and differences
% so: only community that shows a clear improvement when comapred to prior to change sentiment % so: only community that shows a clear improvement when comapred to prior to change sentiment
@@ -16,16 +17,16 @@ The other communities do not seem to profit from the change directly. ServerFaul
% tex: sentiments took up a bit after the change; change seems to works % tex: sentiments took up a bit after the change; change seems to works
% unix: sentiments falling prior but gainig after; change seems to work % unix: sentiments falling prior but gainig after; change seems to work
By and large, the change introduced by the StackExchange team has a clear positive effect on the average sentiment of half of the investigated communities. Two of the communities have a delayed temporary decrease in sentiment which recovers after about 12 months. The selected ITS model is not designed to capture the sentiment pattern of these communities. For the other three communities the ITS analysis does not show a significant change in the sentiment trend. By and large, the change introduced by the StackExchange team has a clear positive effect on more than half of the investigated communities. Two of the communities, SuperUser and stats.stackexchange.com, have a delayed temporary decrease in sentiment which recovers after about 12 months, which may be attributable to the larger influx of new contributors. The selected ITS model is not designed to capture the sentiment pattern of these communities. math.stackexchange.com is not really affected by the change, although the number of 1st questions stabilized a bit and follow-up questions from new contributors increase again. MathOverflow shows a similar picture.
% expectations from before the experiment and how they match with results % expectations from before the experiment and how they match with results
% did change from SE produce the desired results? % did change from SE produce the desired results?
Some investigated data sets show intresting patterns. StackOverflow shows the clearest results of all the investigated communities and closely resembles the example ITS shown in section 3. The result matches the expectations and shows that the change introduced by the StackExchange team works well for this community. The AskUbuntu community shows interesting zig-zag pattern where sentiment gradually rises over time and then falls apruptly. Some investigated data sets show interesting patterns. StackOverflow shows the clearest results of all the investigated communities and closely resembles the example ITS shown in section 3. The result matches the expectation, that advising answerers to remember the code of conduct when answering questions from new contributors will increase the welcomingness and friendliness of the community, and shows that the change introduced by the StackExchange team works well for this community. The AskUbuntu community shows an interesting zig-zag pattern where sentiment gradually rises over time and then falls abruptly.
% interesting single results? % interesting single results?
The average sentiment of the StackOverflow community is the most stable in terms of deviation from the regression. This is expected as StackOverflow is the largest community by far and has the most questions created by new comers. On the other hand MathOverflow is the sparsed community and has the least amount questions from new contributors. The level of the average sentiment also varies greatly between communities. stats.stackexchange.com has the highest level of average sentiment compared to the other communities, whereas, tex.stackexchange.com has the lowest level average sentiment. Also, in every community the number of questions from new contributors slowly decreases over time. This may be a result of the filling of gaps in the knowledge repository over time. The average sentiment of the StackOverflow community is the most stable in terms of deviation from the regression. This is expected as StackOverflow is the largest community by far and has the most questions created by newcomers. On the other hand, MathOverflow is the sparsest community and has the least amount of questions from new contributors. The level of the average sentiment also varies greatly between communities. stats.stackexchange.com has the highest level of average sentiment compared to the other communities, whereas, tex.stackexchange.com has the lowest level average sentiment. MathOverflow has the highest level of vote scores by far. Also, in most communities, the number of questions from new contributors slowly decreases over time. This may be a result of the filling of gaps in the knowledge repository over time.
% as expected #answers per month vary greatly -> mabye into data sets section % as expected #answers per month vary greatly -> mabye into data sets section
% some communties have a high average sentiment compared to others % some communties have a high average sentiment compared to others

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@@ -1,6 +1,6 @@
\chapter{Conclusion} \chapter{Conclusion}
The change introduced by the StackExchange team produced desired results in half of the investigated communities. The results of the StackOverflow community most closely resembles the expectation of improving the welcomingness. AskUbuntu, stats.stackexchange.com, tex.stackexchange.com, and unix.stackexchange also profit from this change. ServerFault is mostly unaffected by the change. MathOverflow, SuperUser, math.stackexchange.com, and electronics.stackexchange.com do not profit from the change and show not an increase but decrease or continuation in decrease of sentiment. The change introduced by the StackExchange team produced desired results in more than half of the investigated communities. The results of the StackOverflow community most closely resembles the expectation of improving the welcomingness. AskUbuntu, ServerFault, stats.stackexchange.com, tex.stackexchange.com, and unix.stackexchange also profit from this change. MathOverflow, SuperUser, math.stackexchange.com, and electronics.stackexchange.com do not profit as much from the change and show not an increase but decrease or continuation in the decrease of sentiment. However, the falling number of questions from new contributors stabilized a bit for the math communities and the vote score increased for electronics.stackexchange.com. SuperUser saw a huge influx of new contributors shortly after the change who asked a lot of questions and dropping the sentiment and vote score value during that period.
%sum up findings %sum up findings
% change did something? % change did something?

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text/main.tex
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@@ -143,9 +143,9 @@
\newcommand{\myauthorwithexistingtitles}{\myauthor{}, Bsc.} %% including \newcommand{\myauthorwithexistingtitles}{\myauthor{}, Bsc.} %% including
%% university degree already held %% university degree already held
%% (BSc, MSc, ...) %% (BSc, MSc, ...)
\newcommand{\mytitle}{TITLE} %% also used for PDF metadata (hyperref) \newcommand{\mytitle}{StackExchange: The effectiveness of the new contributor indicator, an analysis} %% also used for PDF metadata (hyperref)
\newcommand{\mysubject}{SUBJECT} %% also used for PDF metadata (hyperref) \newcommand{\mysubject}{SUBJECT} %% also used for PDF metadata (hyperref)
\newcommand{\mykeywords}{KEYWORDS} %% also used for PDF metadata (hyperref) \newcommand{\mykeywords}{stackexchange, stackoverflow, vader, interrupted time series, sentiment, votes, questions, $21^{st}$ August 2018, welcomingness} %% also used for PDF metadata (hyperref)
%% this information is used only for generating the title page: %% this information is used only for generating the title page:
\newcommand{\myworktitle}{Master's Thesis} %% official type of work like ``Master theses'' \newcommand{\myworktitle}{Master's Thesis} %% official type of work like ``Master theses''
@@ -154,9 +154,9 @@
\newcommand{\mydegreeprogramme}{Master's degree programme: \mystudy} %% Bachelors's, Master's or PhD degree programme \newcommand{\mydegreeprogramme}{Master's degree programme: \mystudy} %% Bachelors's, Master's or PhD degree programme
\newcommand{\myuniversity}{Graz University of Technology} %% your university/school \newcommand{\myuniversity}{Graz University of Technology} %% your university/school
\newcommand{\myinstitute}{Institute for Interactive Systems and Data Science} %% affiliation \newcommand{\myinstitute}{Institute for Interactive Systems and Data Science} %% affiliation
\newcommand{\myinstitutehead}{Univ.-Prof.\,Dipl-Ing.\,Dr.techn.~Some One} %% head of institute, e.g. Univ.-Prof. Dipl.-Inf. Dr. Stefanie Lindstaedt \newcommand{\myinstitutehead}{Univ.-Prof.~Dipl.-Inf.~Dr.~Stefanie~Lindstaedt} %% head of institute, e.g. Univ.-Prof. Dipl.-Inf. Dr. Stefanie Lindstaedt
\newcommand{\mysupervisor}{Dr.~Some Body} %% your supervisor, e.g. Dipl.-Ing. Dr.techn. Roman Kern \newcommand{\mysupervisor}{Assoc.Prof.~Dipl.-Ing.~Dr.techn.~Denis~Helic} %% your supervisor, e.g. Dipl.-Ing. Dr.techn. Roman Kern
\newcommand{\myevaluator}{Prof.~Some Genius} %% your evaluator \newcommand{\myevaluator}{Assoc.Prof.~Dipl.-Ing.~Dr.techn.~Denis~Helic} %% your evaluator
\newcommand{\myhomestreet}{Petergasse~23/13} %% your home street (with house number) \newcommand{\myhomestreet}{Petergasse~23/13} %% your home street (with house number)
\newcommand{\myhometown}{Graz} %% your home town \newcommand{\myhometown}{Graz} %% your home town
\newcommand{\myhomepostalnumber}{8010} %% your postal number of home town \newcommand{\myhomepostalnumber}{8010} %% your postal number of home town