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text/0_abstract.tex
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%%%% Time-stamp: <2013-02-25 10:31:01 vk>
\chapter*{Abstract}
\label{cha:abstract}
@@ -24,16 +21,3 @@ The abstract is typically written in the past tense.
It is uncommon to put references directly into the abstract.
%\glsresetall %% all glossary entries should be used in long form (again)
%% vim:foldmethod=expr
%% vim:fde=getline(v\:lnum)=~'^%%%%\ .\\+'?'>1'\:'='
%%% Local Variables:
%%% mode: latex
%%% mode: auto-fill
%%% mode: flyspell
%%% eval: (ispell-change-dictionary "en_US")
%%% TeX-master: "main"
%%% End:

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text/1_intro.tex
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@@ -9,8 +9,8 @@ StackExchange is a Q\&A platform and consists of 174 communities \cite{stackexch
% goto place for programming questions (SO)
% social media
% knowledge archive
% different to top-down forums
% write new numbers about so
% different to top-down forums %TODO success of SO paper
% 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.
@@ -18,11 +18,11 @@ In August of 2018, the StackExchange team introduced a small change which may ha
% 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?
This thesis evaluates whether this change has a real impact on the community and if so in which direction the community reacts. For this analysis, this thesis utilizes Vader \cite{hutto2014vader}, a sentiment analysis tool, to quantify the sentiments of the answers submitted to questions of new contributors. An interrupted time series is 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 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. An interrupted time series is then applied to these values to evalutate whether the change achieved its purpose of making the platform more welcoming.
% how is change investigated by this thesis
% vader library
This thesis investigates the ten largest communities of the StackExchange platform measured by number of posts. This includes StackOverflow, MathOverflow, Math, AskUbuntu, SuperUser, and some lesser known communities.
This thesis investigates the ten largest communities of the StackExchange platform measured by number of posts. This includes 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)

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text/2_relwork.tex
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@@ -69,7 +69,7 @@ These platforms allow communication over large distances and facilitate fast and
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.
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 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} 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.
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:
@@ -86,17 +86,28 @@ All these criteria are heavily intertwined. Attracting new users often depends o
Keeping users commited to the platform depends on the engagement with the community and how well the system design supports this. For the purpose of this thesis, the criteria layed out by \citeauthor{kraut2012building} can be grouped into two main categories: 1) onboarding of new users, 2) keeping users engaged, contributing, and well behaved.
\subsection{Onboarding of new users}
The onboarding process is a permanent challenge for online communities and differs from one platform to another. \citeauthor{slag2015one} investigated why many users on StackOverflow only post once after their registration \cite{slag2015one}. They found that 47\% of all users on StackOverflow posted only once and called them one-day-flies. They suggest that code example quality is lower than that of more involved users, which often leads to answers and comments to first improve the question and code instead of answering the stated question. This likely discourages new users from using the site further. Negative feedback instead of constructive feedback is another cause for discontinuation of usage. The StackOverflow staff also conducted their own research on negative feedback of the community \cite{silge2019welcome}. They investigated the comment sections of questions by recruiting their staff members to rate a set of comments and they found more than 7\% of the reviewed comments are unwelcoming.
The onboarding process is a permanent challenge for online communities and differs from one platform to another.
%TODO short intro into folling paragraphs
%on day flies, on multiple platforms, solutions on other platforms
%bad comment section
%lurking
%several project by SE to improve site
%- mentorship program, ...
%marginalized groups
\citeauthor{slag2015one} investigated why many users on StackOverflow only post once after their registration \cite{slag2015one}. They found that 47\% of all users on StackOverflow posted only once and called them one-day-flies. They suggest that code example quality is lower than that of more involved users, which often leads to answers and comments to first improve the question and code instead of answering the stated question. This likely discourages new users from using the site further. Negative feedback instead of constructive feedback is another cause for discontinuation of usage. The StackOverflow staff also conducted their own research on negative feedback of the community \cite{silge2019welcome}. They investigated the comment sections of questions by recruiting their staff members to rate a set of comments and they found more than 7\% of the reviewed comments are unwelcoming.
One-day-flies are not unique to StackOverflow. \citeauthor{steinmacher2015social} investigated the social barriers newcomers face when they submit their first contribution to an open-source software project \cite{steinmacher2015social}. They based their work on empirical data and interviews and identified several social barriers preventing newcomers to place their first contribution to a project. Furthermore, newcomers are often on their own in open source projects. The lack of support and peers to ask for help hinders them. \citeauthor{yazdanian2019eliciting} found that new contributors on Wikipedia face challenges when editing articles. Wikipedia hosts millions of articles \cite{sizeofwikipedia} and new contributors often do not know which articles they could edit and improve. Recommender systems can solve this problem by suggesting articles to edit but they suffer from the cold start problem because they rely on past user activity which is missing for new contributors. \citeauthor{yazdanian2019eliciting} proposed a solution by establishing a framework that automatically creates questionnaires to fill this gap. This also helps matching new contributors with more experienced contributors that could help newcomers when they face a problem.
\citeauthor{allen2006organizational} showed that the one-time-contributors phenomenon also translates to workplaces and organizations \cite{allen2006organizational}. They found out that socialization with other members of an organization plays an important role in turnover. The better the socialization within the organization the less likely newcomers are to leave. This socialization process has to be actively pursued by the organization.
One-day-flies may partially be a result of lurking. Lurking is consuming content generated by a community but not contributing content to it. \citeauthor{nonnecke2006non} investigated lurking behavior on Microsoft Network (MSN) \cite{nonnecke2006non} and found that contrary to previous studies lurking is not necessarily a bad behavior. Lurkers show passive behavior and are more introverted and less optimistic than actively posting members of a community. Previous studies suggested lurking is free riding, a taking-rather-than-giving process. However, the authors found that lurking is important in getting to know a community, how a community works and learning the nuances of social interactions on the platform. This allows for better integration into the community when a person decides to join the community. StackExchange, and especially the StackOverflow community, probably has a large lurking audience. Many programmers do not register on the site and those who do only ask one question and revert to lurking, as suggested by \cite{slag2015one}.
% DONE Non-public and public online community participation: Needs, attitudes and behavior \cite{nonnecke2006non} about lurking, many programmers do that probably, not even registering, lurking not a bad behavior but observing, lurkers are more introverted, passive behavior, less optimistic and positive than posters, prviously lurking was thought of free riding, not contributing, taking not giving to comunity, important for getting to know a community, better integration when joining
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}. Furthermore, 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.
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}.
@@ -117,10 +128,8 @@ For this project four mentors were hand selected and therefore the project would
% Rolling out the Welcome Wagon: June Update \cite{friend2018rolling} “Ask a Question Wizard” prototype, reduce exclusion (negative feelings, expectations and experiences), improve inclusion (learn from other communities facing similar problems), classification of abusive and unwelcoming comments
Unwelcomeness is a large problem on StackExchange \cite{hanlon2018stack, friend2018rolling, ford2016paradise}.
Although unwelcomeness affects all new users, users from marginalized groups suffer significantly more \cite{hanlon2018stack, vasilescu2014gender}. \citeauthor{ford2016paradise} investigated barriers users face when contributing to StackOverflow. The authors identified 14 barriers in total hindering newcomers to contribute and five barriers were rated significantly more problematic for women than men.
On StackOverflow only 5.8\% (2015 \cite{stackoversurvey2015}, 7.9\% 2019 \cite{stackoversurvey2019}) of active users identify as women. \citeauthor{david2008community} found similar results of 5\% women in their work on \emph{Community-based production of open-source software} \cite{david2008community}. These numbers are comparatively small to the number of degrees in Science, Technology, Engineering, and Mathematics (STEM) \cite{clark2005women} where 20\% are achieved by women \cite{hill2010so}. Despite the difference, the percentage of women on StackOverflow has increased in recent years.
%TODO Unwelcomeness is a large problem on StackExchange; not so strong; maybe other sentence
Unwelcomeness is a large problem on StackExchange \cite{hanlon2018stack, friend2018rolling, ford2016paradise}.Although unwelcomeness affects all new users, users from marginalized groups suffer significantly more \cite{hanlon2018stack, vasilescu2014gender}. \citeauthor{ford2016paradise} investigated barriers users face when contributing to StackOverflow. The authors identified 14 barriers in total hindering newcomers to contribute and five barriers were rated significantly more problematic for women than men. On StackOverflow only 5.8\% (2015 \cite{stackoversurvey2015}, 7.9\% 2019 \cite{stackoversurvey2019}) of active users identify as women. \citeauthor{david2008community} found similar results of 5\% women in their work on \emph{Community-based production of open-source software} \cite{david2008community}. These numbers are comparatively small to the number of degrees in Science, Technology, Engineering, and Mathematics (STEM) \cite{clark2005women} where 20\% are achieved by women \cite{hill2010so}. Despite the difference, the percentage of women on StackOverflow has increased in recent years.
%discrimitation
% DONE Paradise Unplugged: Identifying Barriers for Female Participation on Stack Overflow \cite{ford2016paradise} gender gap, females only 5\%, contribution barriers, found 5 gender specific (women) barriers among 14 barrier in total, barriers also affect groups like industry programmers
@@ -135,6 +144,10 @@ On StackOverflow only 5.8\% (2015 \cite{stackoversurvey2015}, 7.9\% 2019 \cite{s
\subsection{Keeping users engaged, contributing and well behaved}
%intro .. se employes serveral features to engage/keep contributing users
%reputation
%badge system
%quality
Reputation plays a important role on StackExchange and indicates the credibility of a user as well as a primary source of answers of high quality \cite{movshovitz2013analysis}. Although the largest chunk of all questions is posted by low-reputated users, high-reputated users post more questions on average. To earn a high reputation a user has to invest a lot of effort and time into the community, for instance, asking good questions or providing useful answers to questions of others. Reputation is earned when a question or answer is upvoted by other users, or if an answer is accepted as the solution to a question by the question creator. \citeauthor{mamykina2011design} found that the reputation system of StackOverflow encourages users to compete productively \cite{mamykina2011design}. But not every user participates equally, and participation depends on the personality of the user \cite{bazelli2013personality}. \citeauthor{bazelli2013personality} showed that the top-reputated users on StackOverflow are more extroverted compared to users with less reputation. \citeauthor{movshovitz2013analysis} found that by analyzing the StackOverflow community network, experts can be reliably identified by their contribution within the first few months after their registeration. Graph analysis also allowed the authors to find spamming users or users with other extreme behavior.
Although gaining reputation takes time and effort, users can take certain advantages to gain reputation faster by gaming the system \cite{bosu2013building}. \citeauthor{bosu2013building} analyzed the reputation system and found five strategies to increase the reputation in a fast way: Firstly, answering questions with tags that have a small expertise density. This reduces competitiveness against other users and increases the chance of upvotes and answer acceptance. Secondly, questions should be answered promptly. The question asker will most likely accept the first arriving answer that solves the question. This is also supported by \cite{anderson2012discovering}. Thirdly, answering first also gives the user an advantage over other answerers. Fourthly, activity during off-peak hours reduces the competition from other users. Finally, contributing to diverse areas will also help in developing a higher reputation.
@@ -164,7 +177,7 @@ Different badges also create status classes \cite{immorlica2015social}. The hard
% DONE Steering user behavior with badges \cite{anderson2013steering} # all abount badges, steering users, motivation, user may put in non trivial amounts of work to achieve badges -> powerful incentives, badges used in multiple ways (steer users to ask/answer more questions, voting, etc.)
Quality is often a concern in online communities. Platform moderators and admins want to keep a certain level of quality or even raise it. However, higher-quality posts take more time and effort than lower-quality posts. In the case of CQA platforms, this is an even bigger problem as higher quality posts fight against fast responses. Despite that, StackOverflow also has a problem with low quality and effort questions and subsequent unwelcoming answers and comments \cite{silge2019welcome}. StackOverflow has grown into a large community and larger communities are harder to control. \citeauthor{lin2017better} investigated how growth affects a community. They looked at Reddit communities that were added to the default set of subscribed communities of every new user (defaulting) which lead to a huge influx of new users to these communities as a result. The authors found that contrary to expectations, the quality stays largely the same. The vote score dips shortly after defaulting but quickly recovers or even raises to higher levels than before. The complaints of low-quality content did not increase, and the language used in the community stayed the same. However, the community clustered around fewer posts than before defaulting.
Quality is often a concern in online communities. Platform moderators and admins want to keep a certain level of quality or even raise it. However, higher-quality posts take more time and effort than lower-quality posts. In the case of CQA platforms, this is an even bigger problem as higher quality answers fight against fast responses. Despite that, StackOverflow also has a problem with low quality and effort questions and subsequent unwelcoming answers and comments \cite{silge2019welcome}. StackOverflow has grown into a large community and larger communities are harder to control. \citeauthor{lin2017better} investigated how growth affects a community. They looked at Reddit communities that were added to the default set of subscribed communities of every new user (defaulting) which lead to a huge influx of new users to these communities as a result. The authors found that contrary to expectations, the quality stays largely the same. The vote score dips shortly after defaulting but quickly recovers or even raises to higher levels than before. The complaints of low-quality content did not increase, and the language used in the community stayed the same. However, the community clustered around fewer posts than before defaulting.
\citeauthor{tausczik2011predicting} found reputation is linked to the perceived quality of posts in multiple ways \cite{tausczik2011predicting}. They suggest reputation could be used as an indicator of quality.
Quality also depends on the type of platform. \cite{lin2017better} showed that expert sites who charge fees, for instance, library reference services, have higher quality answers compared to free sites. Also, the higher the fee the higher the quality of the answers. However, free community sites outperform expert sites in terms of answer density and responsiveness.

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text/5_results.tex
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@@ -1,7 +1,7 @@
\chapter{Results}
%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 average sentiment of the answers to questions from new contributors. This line also has numbers attached to it at every datapoint and shows the number of answers that formed the sentiment average. 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. This line also has 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.
% pvalues ...
@@ -11,30 +11,52 @@ This section shows the results of the experiments described in section 3 on the
%TODO write some text to each result
\section{StackOverflow.com}
\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../stackoverflow.com/output/its/average_sentiments-i1.png}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../stackoverflow.com/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on StackOverflow.com}
\label{stackoverflow_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../stackoverflow.com/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on StackOverflow.com}
\label{stackoverflow_votesits}
\end{subfigure}
\end{figure}
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 by about 0.003. After the change the sentiments reached higher levels and kept rising.
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.
% sentiment falling prior to change
% jump upward at the change
% sentiments rising after change
\section{math.stackexchange.com}
The math.stackexchange.com community shows a decrease in average sentiments prior to the change. The sentiment make a small jump upward when the change is introduced, however, the sentiments decrease faster after the indroduction of the change compared to before the change.\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../math.stackexchange.com/output/its/average_sentiments-i1.png}
The math.stackexchange.com community shows a decrease in average sentiments prior to the change. The sentiment make a small jump upward when the change is introduced, however, the sentiments decrease faster after the introduction of the change compared to before the change.
\begin{figure}[H]
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../math.stackexchange.com/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on math.stackexchange.com}
\label{math_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../math.stackexchange.com/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on math.stackexchange.com}
\label{math_votesits}
\end{subfigure}
\end{figure}
% sentiments falling prior to the change
% sentiments falling faster than before the change
\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 jumps up and decreases thereafter. This data set is sparse compared to the other datasets.
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. This data set is sparse compared to the other datasets.
\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../mathoverflow.net/output/its/average_sentiments-i1.png}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../mathoverflow.net/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on MathOverflow.com}
\label{matho_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../mathoverflow.net/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on MathOverflow.com}
\label{math_votesits}
\end{subfigure}
\end{figure}
% senitments stable/constant prior to the change
% falling after the change
@@ -42,9 +64,16 @@ MathOverflow shows a constant regresssion before the change, however, average se
\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.
\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../askubuntu.com/output/its/average_sentiments-i1.png}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../askubuntu.com/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on AskUbuntu.com}
\label{ubuntu_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../askubuntu.com/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on AskUbuntu.com}
\label{math_votesits}
\end{subfigure}
\end{figure}
%senitments have gradually fallen prior to the change
% sentiments increased after the change
@@ -53,9 +82,16 @@ AskUbuntu saw a decrease in average sentiments prior to the change. After the in
\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.
\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../serverfault.com/output/its/average_sentiments-i1.png}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../serverfault.com/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on ServerFault.com}
\label{fault_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../serverfault.com/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on ServerFault.com}
\label{math_votesits}
\end{subfigure}
\end{figure}
%sentiments fairly stable before and after the change
% small jump in avg sentiments at change date
@@ -63,9 +99,16 @@ ServerFault shows gradually rising average sentiments prior to the change. At th
\section{SuperUser.com}
SuperUser shows only sightly decreasing average sentiment up to the change. At the change time the regression takes a dip down and the regression shows a downward trend after the change. Indeed the average sentiments dipped considerably when the change is introducted the average sentiment recovers about 13 months later. Data available in the future will show if the recovery is persistent.
\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../superuser.com/output/its/average_sentiments-i1.png}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../superuser.com/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on SuperUser.com}
\label{super_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../superuser.com/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on SuperUser.com}
\label{math_votesits}
\end{subfigure}
\end{figure}
%sentiments fairly stable until the change date
%after change sentiments took a samll dive
@@ -74,9 +117,16 @@ SuperUser shows only sightly decreasing average sentiment up to the change. At t
\section{electronics.stackexchange.com}
On electronics.stackexchange.com the average sentiment decreases 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 recover at about 12 months after the change is introduced and future data will be necessary to determine if the recovery is persistent.s
\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../electronics.stackexchange.com/output/its/average_sentiments-i1.png}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../electronics.stackexchange.com/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on electronics.stackexchange.com}
\label{ele_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../electronics.stackexchange.com/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on electronics.stackexchange.com}
\label{math_votesits}
\end{subfigure}
\end{figure}
%sentiments were falling continuously before and after the change
% recovery started after 12 month after the change
@@ -85,9 +135,16 @@ On electronics.stackexchange.com the average sentiment decreases continuously pr
\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.
\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../stats.stackexchange.com/output/its/average_sentiments-i1.png}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../stats.stackexchange.com/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on stats.stackexchange.com}
\label{stats_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../stats.stackexchange.com/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on stats.stackexchange.com}
\label{math_votesits}
\end{subfigure}
\end{figure}
% sentiments steadily decreasing prior to the change
% dip in avg sentiment at the change date
@@ -96,9 +153,16 @@ On stats.stackexchange.com the average sentiment is steadily decreasing prior to
\section{tex.stackexchange.com}
On tex.stackexchange.com the average sentiment is low comapred to the other investigated data sets. Prior to the change the average sentiment only slightly decreases. When the change is introduced the regreesion takes a dip down. After the change the analysis indicates a strong increase in average sentiment. Future data will be required to see if this upward trend continues or evens out.
\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../tex.stackexchange.com/output/its/average_sentiments-i1.png}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../tex.stackexchange.com/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on tex.stackexchange.com}
\label{tex_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../tex.stackexchange.com/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on tex.stackexchange.com}
\label{math_votesits}
\end{subfigure}
\end{figure}
%avg sentiment fairly low compared to the other investigated communities
% avg sentiment slowly decreasing prior to the change
@@ -108,9 +172,16 @@ On tex.stackexchange.com the average sentiment is low comapred to the other inve
\section{unix.stackexchange.com}
On unix.stackexchange.com the average sentiment is decreasing prior to the change. When the change is introduced the regreesion take a small dip down, however, the average sentiment increases fast after the change.
\begin{figure}[H]
\centering\includegraphics[scale=0.47]{../unix.stackexchange.com/output/its/average_sentiments-i1.png}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../unix.stackexchange.com/output/its/average_sentiments-i1.png}
\caption{An interrupted time series analysis of the sentiments of answer to questions created by new contributors on unix.stackexchange.com}
\label{unix_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
\includegraphics[scale=0.37]{../unix.stackexchange.com/output/votesits/average_votes-i1.png}
\caption{An interrupted time series analysis of the vote score of questions created by new contributors on unix.stackexchange.com}
\label{math_votesits}
\end{subfigure}
\end{figure}
%sentiments decreasing prior to the change
%snetiments rising after the change