wip

text/2_relwork.tex

@@ -1,14 +1,12 @@
 \chapter{Related Work}
 
-This section is divided into three parts. The first part explains what StackExchange is, how it developed since its inception, and how it works. The second part shows previous and related work. The third section covers approches to analyze sentiment as well as methods to analyze trends over time.
+This section is divided into three parts. The first part explains what StackExchange is, how it developed since its inception, and how it works. The second part shows previous and related work. The third section covers approaches to analyze sentiment as well as methods to analyze trends over time.
 
 \section{Background}
 
-StackExchange\footnote{\url{https://stackexchange.com}} is a community question and answering (CQA) platform where users can ask and answer questions, accept answers as an appropriate solution to the question, and up-/downvote questions and answers. StackExchange uses a community-driven knowledge creation process by allowing everyone who registers to participate in the community. Invested users also get access to moderation tools to help maintain the vast community. All posts on the StackExchange platform are publicly visible, allowing non-users to benefit from the community as well. Posts are also accessible for web search engines so users can find questions and anwsers easily with a simple web search. StackExchange keeps an archive of all questions and answers posted, creating a knowledge archive for future visitors to look into.
+StackExchange\footnote{\url{https://stackexchange.com}} is a community question and answering (CQA) platform where users can ask and answer questions, accept answers as an appropriate solution to the question, and up-/downvote questions and answers. StackExchange uses a community-driven knowledge creation process by allowing everyone who registers to participate in the community. Invested users also get access to moderation tools to help maintain the vast community. All posts on the StackExchange platform are publicly visible, allowing non-users to benefit from the community as well. Posts are also accessible for web search engines so users can find questions and answers easily with a simple web search. StackExchange keeps an archive of all questions and answers posted, creating a knowledge archive for future visitors to look into.
 
-Originally, StackExchange started with StackOverflow\footnote{\url{https://stackoverflow.com}} in 2008\footnote{\label{atwood2008stack}\url{https://stackoverflow.blog/2008/08/01/stack-overflow-private-beta-begins/}}.
+Originally, StackExchange started with StackOverflow\footnote{\url{https://stackoverflow.com}} in 2008\footnote{\label{atwood2008stack}\url{https://stackoverflow.blog/2008/08/01/stack-overflow-private-beta-begins/}}. Since then StackExchange grew into a platform hosting sites for 174 different topics\footnote{\label{stackexchangetour}\url{https://stackexchange.com/tour}}, for instance, programming (StackOverflow), maths (MathOverflow\footnote{\url{https://mathoverflow.net}} and Math StackExchange\footnote{\url{https://math.stackexchange.com}}), and typesetting (TeX/LaTeX\footnote{\url{https://tex.stackexchange.com}}). Questions on StackExchange are stated in natural English language and consist of a title, a body containing a detailed description of the problem or information need, and tags to categorize the question. After a question is posted the community can submit answers to the question. The author of the question can then accept an appropriate answer which satisfies their question. The accepted answer is then marked as such with a green checkmark and shown on top of all the other answers. Figure \ref{soexamplepost} shows an example of a StackOverflow question. Questions and answers can be up-/downvoted by every user registered on the site. Votes typically reflect the quality and importance of the respective question or answers. Answers with a high voting score raise to the top of the answer list as answers are sorted by the vote score in descending order by default. Voting also influences a user's reputation \cite{movshovitz2013analysis}\footref{stackexchangetour}. When a post (question or answers) is voted upon the reputation of the poster changes accordingly. Furthermore, downvoting of answers also decreases the reputation of the user who voted\footnote{\url{https://stackoverflow.com/help/privileges/vote-down}}.
-Since then StackExchange grew into a platform hosting sites for 174 different topics\footnote{\label{stackexchangetour}\url{https://stackexchange.com/tour}}, for instance, programming (StackOverflow), maths (MathOverflow\footnote{\url{https://mathoverflow.net}} and Math StackExchange\footnote{\url{https://math.stackexchange.com}}), and typesetting (TeX/LaTeX\footnote{\url{https://tex.stackexchange.com}}).
-Questions on StackExchange are stated in natural English language and consist of a title, a body containing a detailed description of the problem or information need, and tags to categorize the question. After a question is posted the community can submit answers to the question. The author of the question can then accept an appropriate answer which satisfies their question. The accepted answer is then marked as such with a green checkmark and shown on top of all the other answers. Figure \ref{soexamplepost} shows an example of a StackOverflow question. Questions and answers can be up-/downvoted by every user registered on the site. Votes typically reflect the quality and importance of the respective question or answers. Answers with a high voting score raise to the top of the answer list as answers are sorted by the vote score in descending order by default. Voting also influences a user's reputation \cite{movshovitz2013analysis}\footref{stackexchangetour}. When a post (question or answers) is voted upon the reputation of the poster changes accordingly. Furthermore, downvoting of answers also decreases the reputation of the user who voted\footnote{\url{https://stackoverflow.com/help/privileges/vote-down}}.
 
 Reputation on StackExchange indicates how trustworthy a user is. To gain a high reputation value a user has to invest a lot of time and effort to reach a high reputation value by asking good questions and posting good answers to questions. Reputation also unlocks privileges which may differ slightly from one community to another\footnote{\url{https://mathoverflow.com/help/privileges/}}\mfs\footnote{\url{https://stackoverflow.com/help/privileges/}}. 
 With privileges, users can, for instance, create new tags if the need for a new tag arises, cast votes on closing or reopening questions if the question is off-topic or a duplicate of another question, or when a question had been closed for no or a wrong reason, or even get access to moderation tools. 
@@ -50,8 +48,8 @@ For each community on StackExchange, a \emph Meta page is offered where members
 
 \section{State of the Art}
 
-Since the introduction of Web 2.0 and the subsequential spawning of platforms for social interaction, researchers started investigating the emerging online communities. Research strongly focuses on the interactions of users on various platforms. Community knowledge platforms are of special interest, for instance, StackExchange/StackOverflow \cite{slag2015one, ford2018we, bazelli2013personality, movshovitz2013analysis, bosu2013building, yanovsky2019one, kusmierczyk2018causal, anderson2013steering, immorlica2015social, tausczik2011predicting}, Quora \cite{wang2013wisdom}, Reddit \cite{lin2017better, chandrasekharan2017you}, Yahoo! Answers \cite{bian2008finding, kayes2015social}, and Wikipedia \cite{yazdanian2019eliciting}.
+Since the introduction of Web 2.0 and the subsequential spawning of platforms for social interaction, researchers started investigating emerging online communities. Research strongly focuses on the interactions of users on various platforms. Community knowledge platforms are of special interest, for instance, StackExchange/StackOverflow \cite{slag2015one, ford2018we, bazelli2013personality, movshovitz2013analysis, bosu2013building, yanovsky2019one, kusmierczyk2018causal, anderson2013steering, immorlica2015social, tausczik2011predicting}, Quora \cite{wang2013wisdom}, Reddit \cite{lin2017better, chandrasekharan2017you}, Yahoo! Answers \cite{bian2008finding, kayes2015social}, and Wikipedia \cite{yazdanian2019eliciting}.
-These platforms allow communication over large distances and facilitate fast and easy knowledge exchange and aquisition by connecting thousands or even millions of users and create valuable repositories of knowledge in the process. Users create, edit, and consume little pieces of information and collectively build a community and knowledge repository. However, not every piece of information is factual \cite{wang2013wisdom, bian2008finding} and platforms often employ some kind of moderation to keep up the value of the platform and to ensure a certain standard within the community.
+These platforms allow communication over large distances and facilitate fast and easy knowledge exchange and acquisition by connecting thousands or even millions of users and create valuable repositories of knowledge in the process. Users create, edit, and consume little pieces of information and collectively build a community and knowledge repository. However, not every piece of information is factual \cite{wang2013wisdom, bian2008finding} and platforms often employ some kind of moderation to keep up the value of the platform and to ensure a certain standard within the community.
 %allow communitcation over large distances
 %fast and easy knowledge exchange
 %many answers to invaluable \cite{bian2008finding}
@@ -61,10 +59,10 @@ These platforms allow communication over large distances and facilitate fast and
 % DONE How Do Programmers Ask and Answer Questions on the Web? \cite{treude2011programmers} qa sites very effective at code review and conceptual questions
 % DONE The role of knowledge in software development \cite{robillard1999role} people have different areas of knowledge and expertise
 
-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 and 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. 
+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 and 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 question 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. 
 
 %TODO move this elsewhere
-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.
+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 expertise in different fields.
 
 \subsection{Running an online community}
 Despite the differences in purpose and manifestation of these communities, they are social communities and they have to follow certain laws.
@@ -72,13 +70,13 @@ In their book on ''Building successful online communities: Evidence-based social
 
 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\footref{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}\footnote{\label{hanlon2018stack}\url{https://stackoverflow.blog/2018/04/26/stack-overflow-isnt-very-welcoming-its-time-for-that-to-change/}}. 
+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 development, 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}\footnote{\label{hanlon2018stack}\url{https://stackoverflow.blog/2018/04/26/stack-overflow-isnt-very-welcoming-its-time-for-that-to-change/}}. 
 
-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.
+3) The platform should encourage users to commit to the community. Online communities are often based on the 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. 
+4) Contribution by users to the community should be encouraged. Content generation and engagement are the backbones 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.
+5) The community needs regulation to sustain it. Not every user in a community is interested in the well-being 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.
 
 
 
@@ -92,11 +90,11 @@ In their book on ''Building successful online communities: Evidence-based social
 % - regualting behavior: maintain a funtioning community, prevent troll, inappropiate behavior, limit damage if it occurs, ease of entry & exit -> high turnover
 
 %TODO remove this
-All these criteria are heavily intertwined. Attracting new users often depends on the welcomingness and support of users that are already on the platform. 
+All these criteria are heavily intertwined. Attracting new users often depends on the welcoming ness and support of users that are already on the platform. 
-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.
+Keeping users committed 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 laid 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} %TODO add subsubsections or bold headers, e.g. onday flies, lurking, mentot ship program ...
-The onboarding process of new users is a permanent challenge for online communities and differs from one platform to another. New users should be welcomed by the community and helped to integrate themselves into the community. This is a countiuous process. It is not enough for a user to make one contribution and then revert to a non-contributing state. The StackExchange team took efforts to onboard new users better by making several changes to the site. However, there are still problems where further actions are required.
+The onboarding process of new users is a permanent challenge for online communities and differs from one platform to another. New users should be welcomed by the community and helped to integrate themselves into the community. This is a continuous process. It is not enough for a user to make one contribution and then revert to a non-contributing state. The StackExchange team took efforts to onboard new users better by making several changes to the site. However, there are still problems where further actions are required.
 %TODO short intro into folling paragraphs
 %on day flies, on multiple platforms, solutions on other platforms
 %bad comment section
@@ -111,17 +109,17 @@ One-day-flies are not unique to StackOverflow. \citeauthor{steinmacher2015social
 \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.
 
 \textbf{Lurking}\\
-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 \cite{kollock1996managing, morris1996internet} 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}.
+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 \cite{kollock1996managing, morris1996internet} 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
 
 \textbf{Reflection}\\
-The StackOverflow team acknowledged the one-time-contributors trend\footref{hanlon2018stack}\footref{silge2019welcome} and took efforts to make the site more welcoming to new users\footnote{\label{friend2018rolling}\url{https://stackoverflow.blog/2018/06/21/rolling-out-the-welcome-wagon-june-update/}}. 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\footnote{\label{hanlon2013war}\url{https://stackoverflow.blog/2013/06/25/the-war-of-the-closes/}}. Thirdly, marginalized groups, for instance, women and people of color \cite{ford2016paradise}\footref{hanlon2018stack}\mfs\footnote{\label{stackoversurvey2019}\url{https://insights.stackoverflow.com/survey/2019}}, are more likely to drop out of the community due to unwelcoming behavior from other users\footref{hanlon2018stack}. They feel the site is an elitist and hostile place. 
+The StackOverflow team acknowledged the one-time-contributors trend\footref{hanlon2018stack}\footref{silge2019welcome} and took efforts to make the site more welcoming to new users\footnote{\label{friend2018rolling}\url{https://stackoverflow.blog/2018/06/21/rolling-out-the-welcome-wagon-june-update/}}. 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`` are 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\footnote{\label{hanlon2013war}\url{https://stackoverflow.blog/2013/06/25/the-war-of-the-closes/}}. Thirdly, marginalized groups, for instance, women and people of color \cite{ford2016paradise}\footref{hanlon2018stack}\mfs\footnote{\label{stackoversurvey2019}\url{https://insights.stackoverflow.com/survey/2019}}, are more likely to drop out of the community due to unwelcoming behavior from other users\footref{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\footref{hanlon2018stack}\footref{silge2019welcome}\mfs\footnote{\url{https://stackoverflow.blog/2012/07/20/kicking-off-the-summer-of-love/}}, 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\footnote{\url{https://meta.stackexchange.com/questions/240839/the-new-new-be-nice-policy-code-of-conduct-updated-with-your-feedback}}. 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\footref{hanlon2013war}. Moreover, the team investigates how the comment sections can be improved to lessen the unwelcomeness and hostility and keep the civility up. 
 
 \textbf{Mentorship Research Project}\\
 The StackOverflow team partnered with \citeauthor{ford2018we} and implemented the Mentorship Research Project \cite{ford2018we}\footnote{\url{https://meta.stackoverflow.com/questions/357198/mentorship-research-project-results-wrap-up}}. 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\footref{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\footref{friend2018rolling}. 
 
 
 % DONE One-day flies on StackOverflow \cite{slag2015one}, 1 contribution during whole registration, only user with 6 month of registration
@@ -156,16 +154,16 @@ Unwelcomeness is a large problem on StackExchange \cite{ford2016paradise}\footre
 
 
 \subsection{Invoke commitment}
-While attracting and onboarding new users is an important step for growing a community, keeping them on the platform and turning them long lasting community members is equally as important for growth as well as sustainability. Users have to feel the benefits of staying with the community. Without the benefits a user has little to no motivation to interact with the community and will most likely drop out of it. Benefits are diverse, however, they can be grouped into 5 categories: information exchange, social support, social interaction, time and location flexibility, and permanency \cite{iriberri2009life}. 
+While attracting and onboarding new users is an important step for growing a community, keeping them on the platform and turning them into long-lasting community members is equally as important for growth as well as sustainability. Users have to feel the benefits of staying with the community. Without the benefits, a user has little to no motivation to interact with the community and will most likely drop out of it. Benefits are diverse, however, they can be grouped into 5 categories: information exchange, social support, social interaction, time and location flexibility, and permanency \cite{iriberri2009life}. 
-As StackExchange is a CQA platform, the benefits from information exchange, time and location flexibility, and permanency are more prevalent, while social support, and social interaction are more in the background. Social support and social interaction are more relevant in communities where individuals communicyte about topics reguarding themselves, for instance, communities where health aspects are the main focus \cite{maloney2005multilevel}. Time and location flexibility is important for all online communities. Information exchange, and permanency are important for StackExchange as it is a large collection of knowledge which mostly does not change over time or from one individual to another. StackExchange' content is driven by the community and therefore depends on the voluntarism of its users, making benefits even more important.
+As StackExchange is a CQA platform, the benefits from information exchange, time and location flexibility, and permanency are more prevalent, while social support and social interaction are more in the background. Social support and social interaction are more relevant in communities where individuals communicate about topics regarding themselves, for instance, communities where health aspects are the main focus \cite{maloney2005multilevel}. Time and location flexibility is important for all online communities. Information exchange and permanency are important for StackExchange as it is a large collection of knowledge that mostly does not change over time or from one individual to another. StackExchange' content is driven by the community and therefore depends on the voluntarism of its users, making benefits even more important.
 
-The backbone of a community is always the user base and its volunarism to participate with the community. Even if the community is lead by a commerical core team, the community is almost always several orders of magnitude greater than the number of the paid employees forming the core team \cite{butler2002community}. The core team often provides the infrastructur the community and does some community. However, most of the community work is done by volunteers of the community.
+The backbone of a community is always the user base and its voluntarism to participate with the community. Even if the community is lead by a commercial core team, the community is almost always several orders of magnitude greater than the number of the paid employees forming the core team \cite{butler2002community}. The core team often provides the infrastructure for the community and does some community work. However, most of the community work is done by volunteers of the community.
 This is also true for the StackExchange platform where the core team of paid employees is between 200 to 500\footnote{\url{https://www.linkedin.com/company/stack-overflow}} (this includes employees working on other products) and the number of voluntary community members (these users have access to moderation tools) performing community work is around 10,000 \footnote{\url{https://data.stackexchange.com/stackoverflow/revision/1412005/1735651/users-with-rep-20k}}.
 
 
 
 \subsection{Encourage contribution}
-In a community, users can generally be split in 2 groups by motivation to voluntarily contribute: One group acts out of altruism, where users contribute with the reason to help others and do good to the community; the second group acts out of egoism and selfish reasons, for instance, getting recognition from other people \cite{ginsburg2004framework}. Users of the second group still help the community but their primary goal not neccessarily the health of the community but gaining reputation and making a name for themselves. Contrary, users of the first group primarly focus on helping the community and see reputation as a positive side effect which also feeds back in their ability to help others. While these groups have different objectives, both groups need recognition of their efforts \cite{iriberri2009life}. There are several methods for recognizing the value a member provides to the community: reputation, awards, trust, identity, etc. \cite{ginsburg2004framework}. Reputation, trust, and identity are often reached gradually over time by continuously working on them, awards are reached at discrete points in time. Awards often take some time and effort to achive. However, awards should not be easily achievable as their value come from the work that is required for them\cite{lawler2000rewarding}. They should also be meaningful in the community they are used in. Most importantly, award have to be visible to the public, so other members can see them. In this way, awards become a powerful motivator to users.
+In a community, users can generally be split into 2 groups by motivation to voluntarily contribute: One group acts out of altruism, where users contribute with the reason to help others and do good to the community; the second group acts out of egoism and selfish reasons, for instance, getting recognition from other people \cite{ginsburg2004framework}. Users of the second group still help the community but their primary goal is not necessarily the health of the community but gaining reputation and making a name for themselves. Contrary, users of the first group primarily focus on helping the community and see reputation as a positive side effect which also feeds back in their ability to help others. While these groups have different objectives, both groups need recognition of their efforts \cite{iriberri2009life}. There are several methods for recognizing the value a member provides to the community: reputation, awards, trust, identity, etc. \cite{ginsburg2004framework}. Reputation, trust, and identity are often reached gradually over time by continuously working on them, awards are reached at discrete points in time. Awards often take some time and effort to achieve. However, awards should not be easily achievable as their value come from the work that is required for them\cite{lawler2000rewarding}. They should also be meaningful in the community they are used in. Most importantly, awards have to be visible to the public, so other members can see them. In this way, awards become a powerful motivator to users.
 
 
 %TODO maybe look at finding of https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.3093&rep=rep1&type=pdf , in discussion bullet point list: subgroups, working and less feature > not working and more features, selfmoderation
@@ -211,7 +209,7 @@ In a community, users can generally be split in 2 groups by motivation to volunt
 StackExchange employes serveral features to engage users with the platform, for instance, the reputation system and the badge (award) system. These systems reward contributing users with achievements and encourages further contribution to the community. Both systems try to keep and increase the quality of the posts on the platform.
 
 \textbf{Reputation}\\
-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 registration. Graph analysis also allowed the authors to find spamming users or users with other extreme behavior. 
+Reputation plays an 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-reputation users, high-reputation 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-reputation 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 registration. 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, srba2016stack}. \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. This behavior may, however, decrease answer quality when users focus too much on reputation collection and disregard the quality of their posts\cite{srba2016stack}.
 
@@ -224,12 +222,12 @@ Although gaining reputation takes time and effort, users can take certain advant
 % DONE Design Lessons from the Fastest Q&A Site in the West \cite{mamykina2011design} understanding SO success, 1) productive competition (gamification reputation), 2) founders were already experts on site the created (ensured success early on, founders involved in community not external), 3) meta page for discussion and voting on features (same mechanics as on SO page)
 
 \textbf{Badges}\\
-Complementary to the reputation system StackOverflow also employs a badge system\footref{stackoverflowbadges} to stimulate contributions by users \cite{cavusoglu2015can}. The goal of badges is to keep users engaged with the community \cite{li2012quantifying}. Therefore, badges are often used in a gamification setting where users contribute to the community and are rewarded for their behavior if it alignes with the requirements of the badges. Badges are visible in questions and answers as well as the profile page of the user and can be earned by performing certain actions. Badges are often seen as a steering mechanism by researchers \cite{yanovsky2019one, kusmierczyk2018causal, anderson2013steering}. Although users want to achieve badges and are therefore steered to perform certain actions, steering also occurs in the reputation system. However, badges allow a wider variety of goals, for instance, asking and answering questions, voting on questions and answers, or writing higher quality answers.
+Complementary to the reputation system StackOverflow also employs a badge system\footref{stackoverflowbadges} to stimulate contributions by users \cite{cavusoglu2015can}. The goal of badges is to keep users engaged with the community \cite{li2012quantifying}. Therefore, badges are often used in a gamification setting where users contribute to the community and are rewarded for their behavior if it aligns with the requirements of the badges. Badges are visible in questions and answers as well as the profile page of the user and can be earned by performing certain actions. Badges are often seen as a steering mechanism by researchers \cite{yanovsky2019one, kusmierczyk2018causal, anderson2013steering}. Although users want to achieve badges and are therefore steered to perform certain actions, steering also occurs in the reputation system. However, badges allow a wider variety of goals, for instance, asking and answering questions, voting on questions and answers, or writing higher-quality answers.
 
 Badges also work as a motivator for users \cite{anderson2013steering}. Users often put in non-trivial amounts of work and effort to achieve badges and so badges become powerful incentives. However, not all users are equal and therefore do not pursue badges in the same way \cite{yanovsky2019one}. Contrary to \cite{anderson2013steering}, \citeauthor{yanovsky2019one} \cite{yanovsky2019one} found that users do not necessarily increase their activity prior to achieving a badge followed by an immediate decrease in contribution thereafter but users behave differently based on their type of contribution. The authors found users can be categorized into three groups: Firstly, some users are not affected at all by the badge system and still contribute a lot to the community. Secondly, users increase their activity too before gaining a badge and keep their level of contribution afterward. Finally, users increase their activity before achieving a badge and return to their previous level of engagement thereafter. 
 
 Different badges also create status classes \cite{immorlica2015social}. The harder a badge can be earned by users, the more unique it is within the community and therefore the badge symbolizes some sort of status. Often rare badges are hard to achieve and take significant effort. For some users, depending on their type, this can be a huge motivator. 
-\citeauthor{kusmierczyk2018causal} found first-time badges play an important role in steering users \cite{kusmierczyk2018causal}. The steering effect only takes place if the benefit to the user is greater than the effort the user has to put into to obtain the badge. If the effort is greater the user will likely not pursue the badge and therefore the steering effect will not occur. 
+\citeauthor{kusmierczyk2018causal} found first-time badges play an important role in steering users \cite{kusmierczyk2018causal}. The steering effect only takes place if the benefit to the user is greater than the effort the user has to put in to obtain the badge. If the effort is greater the user will likely not pursue the badge and therefore the steering effect will not occur. 
 
 
 
@@ -244,24 +242,24 @@ Different badges also create status classes \cite{immorlica2015social}. The hard
 
 
 \subsection{Regulation}
-Regulation evolves around the user actions and the content a community creates. It is required to steer the community and keep the community civil.  Naturally, some users will not have the best intensions for the community in mind. These actions of such must be accounted for, and harmful actions must be delt with. Otherwise the community and its content will deteriorate.
+Regulation evolves around the user actions and the content a community creates. It is required to steer the community and keep the community civil.  Naturally, some users will not have the best intentions for the community in mind. These actions of such must be accounted for, and harmful actions must be dealt with. Otherwise, the community and its content will deteriorate.
 
 \textbf{Content qualtity}\\
-Quality is 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 the subsequent unwelcoming answers and comments\footref{silge2019welcome}.
+Quality is 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 the subsequent unwelcoming answers and comments\footref{silge2019welcome}.
 
-\citeauthor{lin2017better} investigated how growth affects a community\cite{lin2017better}. 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{srba2016stack} did a similar study on the StackOverflow community \cite{srba2016stack}. They found similar pattern in the quality of posts. The quality of questions dipped momentarily due to the huge influx of new users. However, the quality did recover after 3 months. 
+\citeauthor{lin2017better} investigated how growth affects a community\cite{lin2017better}. 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{srba2016stack} did a similar study on the StackOverflow community \cite{srba2016stack}. They found a similar pattern in the quality of posts. The quality of questions dipped momentarily due to the huge influx of new users. However, the quality did recover after 3 months. 
 
 \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. \citeauthor{lin2017better} showed that expert sites who charge fees, for instance, library reference services, have higher quality answers compared to free sites\cite{lin2017better}. 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.
 
 \textbf{Content abuse}\\
-\citeauthor{srba2016stack} identified 3 types of users causing the lowering of quality: \emph{Help Vampires} (these spend litte to no effort to research their questions, which leads to many duplicates), \emph{Noobs} (they create mostly trivial questions), and \emph{Reputation Collectors}\cite{srba2016stack}. They try to gain repuation as fast as possible by methods described by \citeauthor{bosu2013building}\cite{bosu2013building} but often with no reguard of what effects their behavior has on the community, for instance, lowering overall content quality, turning other user away from the platform, and encouraging the behavior of \emph{Help Vampires} and \emph{Noobs} even more.
+\citeauthor{srba2016stack} identified 3 types of users causing the lowering of quality: \emph{Help Vampires} (these spend little to no effort to research their questions, which leads to many duplicates), \emph{Noobs} (they create mostly trivial questions), and \emph{Reputation Collectors}\cite{srba2016stack}. They try to gain reputation as fast as possible by methods described by \citeauthor{bosu2013building}\cite{bosu2013building} but often with no regard of what effects their behavior has on the community, for instance, lowering overall content quality, turning other users away from the platform, and encouraging the behavior of \emph{Help Vampires} and \emph{Noobs} even more.
 
-Questions of \emph{Help Vampires} and \emph{Noobs} direct answerers away from much more demanding questions. On one hand this leads to knowledgable answerers answering questions for which they are overqualified to answer, and on the other hand to a lack of adequate quality answers for more difficult questions. \citeauthor{srba2016stack} suggest a system which tries to match questions with answerers that satify the knowledge requirement but are not grossly overqualified to answer the question. A system with this quality would prevent suggesting simple questions to overqualified answerers, and prevent an answer vacuum for questions with more advanced topics. This would ensure a more optimal utilization of the answering capability of the community.
+Questions of \emph{Help Vampires} and \emph{Noobs} direct answerers away from much more demanding questions. On one hand, this leads to knowledgeable answerers answering questions for which they are overqualified to answer, and on the other hand to a lack of adequate quality answers for more difficult questions. \citeauthor{srba2016stack} suggest a system that tries to match questions with answerers that satisfy the knowledge requirement but are not grossly overqualified to answer the question. A system with this quality would prevent suggesting simple questions to overqualified answerers, and prevent an answer vacuum for questions with more advanced topics. This would ensure a more optimal utilization of the answering capability of the community.
 
 \textbf{Content moderation}\\
-\citeauthor{srba2016stack} proposed some solutions to improve the quality problems. One suggestion is to restrict the openness of a community. This can be accomplished in different ways, for instance, introducing a posting limit for questions on a daily basis\cite{srba2016stack}. While this certainly limits the amount of low quality posts, it does not eliminate the problem. Furthermore, this limitation would also hurt engaged users which would create a large volume of higher quality content. A much more intricate solution which adapts to user behavior would be required, otherwise the limitation would hurt the community more than it improves.
+\citeauthor{srba2016stack} proposed some solutions to improve the quality problems. One suggestion is to restrict the openness of a community. This can be accomplished in different ways, for instance, introducing a posting limit for questions on a daily basis\cite{srba2016stack}. While this certainly limits the amount of low-quality posts, it does not eliminate the problem. Furthermore, this limitation would also hurt engaged users which would create a large volume of higher quality content. A much more intricate solution that adapts to user behavior would be required, otherwise, the limitation would hurt the community more than it improves.
 
-\citeauthor{ponzanelli2014improving} performed a study where they looked at post quality on StackOverflow\cite{ponzanelli2014improving}. They aim to improve the automatic low quality post detection system which is already in place and reduce the size of the review queue selected indivuals have to go through. Their classifier improves by including popularity metrics of the user posting and readability of post itself. With these additional factors they managed to reduce the amount of missclassified quality posts with only a minimal decrease in correctly classified low quality posts. Their improvement to the classifier reduced the review queue size by 9\%.
+\citeauthor{ponzanelli2014improving} performed a study where they looked at post quality on StackOverflow\cite{ponzanelli2014improving}. They aim to improve the automatic low-quality post detection system which is already in place and reduce the size of the review queue selected individuals have to go through. Their classifier improves by including popularity metrics of the user posting and the readability of the post itself. With these additional factors, they managed to reduce the amount of misclassified quality posts with only a minimal decrease of correctly classified low-quality posts. Their improvement to the classifier reduced the review queue size by 9\%.
 
 
 % other studies which suggest changes to improve community interaction/qualtity/sustainability
@@ -273,7 +271,7 @@ Questions of \emph{Help Vampires} and \emph{Noobs} direct answerers away from mu
 %   -> matching questions with answerers \cite{srba2016stack} (difficult questions -> expert users, easier questions -> answerers that know it but are not experts), dont overload experts, utilize capacities of the many nonexperts 
 
 
-Another solution is to find content abusers (noobs, help vampires, etc.) directly. One approach is to add a reporting system to the community, however, a system of this kind is also driven by user inputs and therefore can be manipulated as well. This would lead to excluding users flagged as false positives and miss a portion of content abusers completely. A better approach is to systematically find these users by their behavior. \citeauthor{kayes2015social} describe a classifier which achieves an accuracy of 83\% on the \emph{Yahoo! Answers} platform \cite{kayes2015social}. The classifier is based on empirical data where they looked at historical user activity, report data, and which users were banned from the platform. From these statistics they created the classifier which is able to distinguish between falsly and fairly banned users. \citeauthor{cheng2015antisocial} performed a similar study on antisocial behavior on various platforms. They too looked at historical data of users and their eventual bans as well as on their deleted posts rates. Their classifier achieved an accuracy of 80\%.
+Another solution is to find content abusers (noobs, help vampires, etc.) directly. One approach is to add a reporting system to the community, however, a system of this kind is also driven by user inputs and therefore can be manipulated as well. This would lead to excluding users flagged as false positives and miss a portion of content abusers completely. A better approach is to systematically find these users by their behavior. \citeauthor{kayes2015social} describe a classifier which achieves an accuracy of 83\% on the \emph{Yahoo! Answers} platform \cite{kayes2015social}. The classifier is based on empirical data where they looked at historical user activity, report data, and which users were banned from the platform. From these statistics, they created the classifier which is able to distinguish between falsely and fairly banned users. \citeauthor{cheng2015antisocial} performed a similar study on antisocial behavior on various platforms. They too looked at historical data of users and their eventual bans as well as on their deleted posts rates. Their classifier achieved an accuracy of 80\%.
 
 
 
@@ -300,7 +298,7 @@ Another solution is to find content abusers (noobs, help vampires, etc.) directl
 
 \section{Analysis}
 
-When analyzing a community, one typically finds 2 types of data: text, and meta data. Meta data is realively easy to quantify, while text is much more complicated and intricate to quantify. Text contains a large variety of features and depending on the research in question, researchers have to decide which features they want to include. This thesis investigates the (un-)friendlyness in the communication between users an will therefore perform sentiment analysis on the texts. The next section will go into more detail on sentiment analysis. After the data (text and meta data) is quantified, one often want to know how the data has changed over time. The trend analysis section follows the sentiment analysis section. 
+When analyzing a community, one typically finds 2 types of data: text, and metadata. Metadata is relatively easy to quantify, while text is much more complicated and intricate to quantify. Text contains a large variety of features and depending on the research in question, researchers have to decide which features they want to include. This thesis investigates the (un-)friendliness in the communication between users and will therefore perform sentiment analysis on the texts. The next section will go into more detail on sentiment analysis. After the data (text and metadata) is quantified, one often wants to know how the data has changed over time. The trend analysis section follows the sentiment analysis section. 
 
 %
 %assign values to text
@@ -313,11 +311,11 @@ When analyzing a community, one typically finds 2 types of data: text, and meta
 % alle sentiment methoden + vader
 \subsection{Sentiment analysis}
 
-Researchers put forth many tools for sentiment analysis over the years. Each tool has is advantages and drawbacks and there is not a silber bullet solution that fits all research questions. Researches have to choose a tool which best fits their need and they need to be aware of the drawbacks of their choice. Sentiment analysis poses three important challenges:
+Researchers put forth many tools for sentiment analysis over the years. Each tool has its advantages and drawbacks and there is not a silver bullet solution that fits all research questions. Researchers have to choose a tool that best fits their needs and they need to be aware of the drawbacks of their choice. Sentiment analysis poses three important challenges:
 \begin{itemize}
  \item Coverage: detecting as many features as possible from a given piece of text
  \item Weighting: assigning one or multiple values (value range and granularity) to detected features
- \item Creation: creating and maintaining a sentiment analysis tool is a time and labor intensive process
+ \item Creation: creating and maintaining a sentiment analysis tool is a time and labor-intensive process
 \end{itemize}
 
 % many different methods
@@ -345,7 +343,7 @@ In general, sentiment analysis tools can be grouped into two categories: handcra
 %nachvolliziehbare results
 
 \textbf{Handcrafted Approches}\\
-Creating hand crafted tools is often a huge undertaking. They depend on a hand crafted lexicon (gold standard, human-curated lexicons), which maps features of a text to a value. In the simplest sense these just map a word to a binary value -1 (negative word) or 1 (positive word). However, most tools use a more complex lexicon to capture more features of piece of text. By design they allow a fast computation of the sentiment of a given piece of text. Also, hand crafted lexicons are easy to update and extend. Furthermore, hand crafted tools produce easily comprehensible results. The following paragraphs explain some of the analysis tools in this category.
+Creating hand-crafted tools is often a huge undertaking. They depend on a hand-crafted lexicon (gold standard, human-curated lexicons), which maps features of a text to a value. In the simplest sense, these just map a word to a binary value -1 (negative word) or 1 (positive word). However, most tools use a more complex lexicon to capture more features of a piece of text. By design, they allow a fast computation of the sentiment of a given piece of text. Also, hand-crafted lexicons are easy to update and extend. Furthermore, hand-crafted tools produce easily comprehensible results. The following paragraphs explain some of the analysis tools in this category.
 
 
 %liwc (Linguistic Inquiry and Word Count) \cite{pennebaker2001linguistic,pennebakerdevelopment}, 2001 %TODO refs wrong?
@@ -356,7 +354,7 @@ Creating hand crafted tools is often a huge undertaking. They depend on a hand c
 % - TODO list some application examples 
 % ...
 
-Linguistic Inquiry and Word Count (LIWC) \cite{pennebaker2001linguistic,pennebakerdevelopment} is one of the more popular tools. Due to its widespread usage, LIWC is well verfied, both internally and externally. Its lexicon consists of about 6,400 words where words are categorized into one or more of the 76 defined categories \cite{pennebaker2015development}. 620 words have a positive and 744 words have a negative emotion. Examples for positive words are: love, nice, sweet; examples for negative words are: hurt, ugly, nasty. LIWC also has some drawbacks, for instance, it does not capture acronyms, emoticons, or slang words. Furthermore, LIWC's lexicon uses a polarity-based approach, meaning that it cannot distinguish between the scentences ''This pizza is good`` and ''This pizza is excellent``\cite{hutto2014vader}. \emph Good and \emph excellent are both in the category of positive emotion but LIWC does not distinguish between single words in the same category.
+Linguistic Inquiry and Word Count (LIWC) \cite{pennebaker2001linguistic,pennebakerdevelopment} is one of the more popular tools. Due to its widespread usage, LIWC is well verified, both internally and externally. Its lexicon consists of about 6,400 words where words are categorized into one or more of the 76 defined categories \cite{pennebaker2015development}. 620 words have a positive and 744 words have a negative emotion. Examples for positive words are: love, nice, sweet; examples for negative words are: hurt, ugly, nasty. LIWC also has some drawbacks, for instance, it does not capture acronyms, emoticons, or slang words. Furthermore, LIWC's lexicon uses a polarity-based approach, meaning that it cannot distinguish between the sentences ''This pizza is good`` and ''This pizza is excellent``\cite{hutto2014vader}. \emph Good and \emph excellent are both in the category of positive emotion but LIWC does not distinguish between single words in the same category.
 
 %General Inquirer (GI) \cite{stone1966general} 1966 TODO ref wrong?
 % - 11k words, 1900 pos, 2300 neg, all approx (vader)
@@ -364,7 +362,7 @@ Linguistic Inquiry and Word Count (LIWC) \cite{pennebaker2001linguistic,pennebak
 % - misses lexical feature detection (acronyms, ...) and sentiment intensity (vader)
 
 General Inquirer (GI)\cite{stone1966general} is one of the oldest sentiment tools still in use. It was originally designed in 1966 and has been continuously refined and now consists of about 11000 words where 1900 positively rated words and 2300 negatively rated words.
-Like LIWC, GI uses a polarity-based lexicon and therefore is not able to capture sentiment intensity\cite{hutto2014vader}. Also, GI does not recognize lexical features, such as, acronyms, initalisms, etc..
+Like LIWC, GI uses a polarity-based lexicon and therefore is not able to capture sentiment intensity\cite{hutto2014vader}. Also, GI does not recognize lexical features, such as acronyms, initialisms, etc.
 
 
 %Hu-Liu04 \cite{hu2004mining,liu2005opinion}, 2004
@@ -375,7 +373,7 @@ Like LIWC, GI uses a polarity-based lexicon and therefore is not able to capture
 % - bootstrapped from wordnet (wellknown english lexical database) (vader, hu2004mining)
 
 %TODO refs
-Hu-Liu04 \cite{hu2004mining,liu2005opinion} is a opinion mining tool. It searches for features in multiple pieces of text, for instance, product reviews, and rates the opinion of the feature by using a binary classification\cite{hu2004mining}. Crutially Hu-Liu04 does not summarize the texts but summarizes ratings of the opinions about features mentioned in the texts. Hu-Liu04 was bootstrapped from WordNet\cite{hu2004mining} and then extended further. It now uses a lexicon consisting of about 6800 words where 2000 words have a positive sentiment and 4800 word have a negative sentiment attached\cite{hutto2014vader}. This tools is, by design, better suited for social media texts, although it also misses emiticons, acronyms and initialisms.
+Hu-Liu04 \cite{hu2004mining,liu2005opinion} is a opinion mining tool. It searches for features in multiple pieces of text, for instance, product reviews, and rates the opinion of the feature by using a binary classification\cite{hu2004mining}. Crucially Hu-Liu04 does not summarize the texts but summarizes ratings of the opinions about features mentioned in the texts. Hu-Liu04 was bootstrapped from WordNet\cite{hu2004mining} and then extended further. It now uses a lexicon consisting of about 6800 words where 2000 words have a positive sentiment and 4800 words have a negative sentiment attached\cite{hutto2014vader}. This tool is, by design, better suited for social media texts, although it also misses emoticons, acronyms, and initialisms.
 
 %SenticNet \cite{cambria2010senticnet} 2010
 % - concept-level opinion and sentiment analysis tool (vader)
@@ -385,7 +383,7 @@ Hu-Liu04 \cite{hu2004mining,liu2005opinion} is a opinion mining tool. It searche
 % - lexicon: 14250 common-sense concepts, with polarity scores [-1,1] continuous, and many other values (vader)
 % - TODO list some concepts (vader) or maybe not
 
-SenticNet \cite{cambria2010senticnet} is also an opinion mining tool but it focuses on concept-level opinions. SenticNet is based on a paradigm called \emph{Sentic Mining} which uses a combination of concepts from artificial integelligence and the Semantic Web. More specifically, it uses graph mining and dimentionality reduction. SenticNets lexicon consists of about 14250 common-sense concepts which a have rating on many scales of which one is a polarity score with a continuous range from -1 to 1\cite{hutto2014vader}. This continuous range of polarity scores enables SenticNet to be sentiment-intensity aware.
+SenticNet \cite{cambria2010senticnet} is also an opinion mining tool but it focuses on concept-level opinions. SenticNet is based on a paradigm called \emph{Sentic Mining} which uses a combination of concepts from artificial intelligence and the Semantic Web. More specifically, it uses graph mining and dimensionality reduction. SenticNets lexicon consists of about 14250 common-sense concepts which have ratings on many scales of which one is a polarity score with a continuous range from -1 to 1\cite{hutto2014vader}. This continuous range of polarity scores enables SenticNet to be sentiment-intensity aware.
 
 
 %ANEW (Affective Norms for English Words) \cite{bradley1999affective} 1999
@@ -394,14 +392,14 @@ SenticNet \cite{cambria2010senticnet} is also an opinion mining tool but it focu
 % - words get value 1-9 (neg-pos, continuous), 5 neutral (TODO maybe list word examples with associated value) (vader, bradley1999affective)
 % - therefore captures sentiement intensity (vader, bradley1999affective)
 % - misses lexical features (e.g. acronyms, ...) (vader)
-Affective Norms for English Words (ANEW) \cite{bradley1999affective} is sentiment analysis tool and was introducted to standardize research and offer a way to compare research. Its lexicon is fairly small and consists of only 1034 words which are ranked pleasure, arousal, and dominance. However, ANEW uses a continuous scale from 1 to 9 where 1 represents the negative end, 9 represents the positive end, and 5 is considered neutral. With this design, ANEW is able to capture sentiment intensity. However, ANEW still misses lexical features, for instance, acronyms\cite{hutto2014vader}.
+Affective Norms for English Words (ANEW) \cite{bradley1999affective} is a sentiment analysis tool and was introduced to standardize research and offer a way to compare research. Its lexicon is fairly small and consists of only 1034 words which are ranked pleasure, arousal, and dominance. However, ANEW uses a continuous scale from 1 to 9 where 1 represents the negative end, 9 represents the positive end, and 5 is considered neutral. With this design, ANEW is able to capture sentiment intensity. However, ANEW still misses lexical features, for instance, acronyms\cite{hutto2014vader}.
 
 %wordnet \cite{miller1998wordnet} 1998, TODO maybe exlcude or just mention briefly in sentiwordnet
 % - well-known English lexical database (vader)
 % - group synonyms (synsets) together (vader)
 % - 
 
-WordNet analyzes text with a dictionary which contains lexical contepts \cite{miller1995wordnet,miller1998wordnet}. Each lexical concept contains multiple words which are synonyms, called synsets. These synsets are then linked by semantic relations. With this lexicon, text can be queried in multiple different ways.
+WordNet analyzes text with a dictionary which contains lexical concepts \cite{miller1995wordnet,miller1998wordnet}. Each lexical concept contains multiple words which are synonyms, called synsets. These synsets are then linked by semantic relations. With this lexicon, text can be queried in multiple different ways.
 
 
 %sentiwordnet \cite{baccianella2010sentiwordnet}
@@ -412,7 +410,7 @@ WordNet analyzes text with a dictionary which contains lexical contepts \cite{mi
 % - lexicon very noisy, most synset not pos or neg but mix (vader)
 % - misses lexical features (vader)
 
-SentiWordNet \cite{baccianella2010sentiwordnet} is an extension of WordNet and adds sentiment scores to the synsets. Its lexicon consists of about 147000 synsets, each having 3 values (positive, neutral, negative) attached to them. The each value has a continuous range from 0 to 1 and the sum of these 3 values is set to be 1. The values of each synset are calculated by a mix of semi supervised algorithms, mostly propergation and classifiers. This distinguishes SentiWordNet from previously explained sentiment tools, where the lexica are exclusively created by humans (except for simple mathemtical operations, for instance, averaging of values). Therefore, SentiWordNet's lexicon is not considered to be a human-curated gold standard. Furthermore, the lexicon is very noisy and most of the synsets neigher positive or negative but a mix of both\cite{hutto2014vader}. Moreover, SentiWordNet misses lexical features, for instance, acronyms, initalisms and emoticons.
+SentiWordNet \cite{baccianella2010sentiwordnet} is an extension of WordNet and adds sentiment scores to the synsets. Its lexicon consists of about 147000 synsets, each having 3 values (positive, neutral, negative) attached to them. Each value has a continuous range from 0 to 1 and the sum of these 3 values is set to be 1. The values of each synset are calculated by a mix of semi-supervised algorithms, mostly propagation, and classifiers. This distinguishes SentiWordNet from previously explained sentiment tools, where the lexica are exclusively created by humans (except for simple mathematical operations, for instance, averaging of values). Therefore, SentiWordNet's lexicon is not considered to be a human-curated gold standard. Furthermore, the lexicon is very noisy and most of the synsets are neither positive nor negative but a mix of both\cite{hutto2014vader}. Moreover, SentiWordNet misses lexical features, for instance, acronyms, initialisms, and emoticons.
 
 %Word-Sense Disambiguation (WSD) \cite{akkaya2009subjectivity}, 2009
 % - TODO
@@ -421,7 +419,7 @@ SentiWordNet \cite{baccianella2010sentiwordnet} is an extension of WordNet and a
 % - derive meaning from context -> disambiguation (vader, akkaya2009subjectivity)
 % - distinguish subjective and objective word usage, sentences can only contain negative words used in object ways -> sentence not negative, TODO example sentence (akkaya2009subjectivity)
 
-Word-Sense Disambiguation (WSD)\cite{akkaya2009subjectivity} is not a sentiment analysis tool per se but it can be used to enhance others. In languages certain words have different meanings depending on the context they are used in. When sentiment tools, which do not use WSD, analyze a piece of text, some words which have different meanings depending on the context may skew the resulting sentiment. Some words can even change from positive to negative or vice versa depending on the context. WSD tries to distinguish between subjective and objective word usage. For example: \emph{The party was great.} and \emph{The party lost many votes}. Although \emph party is written exactly the same it has 2 completly different meanings. Depending on the context, ambiguous words can have different sentiment.
+Word-Sense Disambiguation (WSD)\cite{akkaya2009subjectivity} is not a sentiment analysis tool per se but it can be used to enhance others. In languages certain words have different meanings depending on the context they are used in. When sentiment tools, which do not use WSD, analyze a piece of text, some words which have different meanings depending on the context may skew the resulting sentiment. Some words can even change from positive to negative or vice versa depending on the context. WSD tries to distinguish between subjective and objective word usage. For example \emph{The party was great.} and \emph{The party lost many votes}. Although \emph party is written exactly the same it has 2 completely different meanings. Depending on the context, ambiguous words can have different sentiments.
 
 
 %%%%% automated (machine learning)
@@ -434,27 +432,27 @@ Word-Sense Disambiguation (WSD)\cite{akkaya2009subjectivity} is not a sentiment
 %updateing (extend/modify) hard (e.g. new domain) (vader)
 
 \textbf{Machine Learning Approches}\\
-Because hand crafting sentiment analysis requires a lot of effort, researches turned to approaches which offload the labor intensive part to machine learning (ML). However, this results in a new challenge, namely: gathering a \emph good data set to feed the machine learning algorithms for training. Firstly, \emph good data set needs to represent as many features as possible, or otherwise the algorithm will not recognise it. Secondly, the the data set has to be unbiased and representative for all the data of which the data set is a part of. The data set has to represent each feature in an appropiate amount, or otherwise the algorithms may discrimate a feature in favor of other more represented features. These requirements are hard to fulfill and often they are not\cite{hutto2014vader}. After a data set is aquired, a model has to be learned by the ML algorithm, which is, depending on the complexity of the alogrithm, a very computational-intensive and memory-intensive process. After training is completed, the algorithm can predict sentiment values for new pieces of text, which it has never seen before. However, due to the nature of this appraoch, the results cannot be comprehended by humans easily if at all. ML approaches also suffer from an generalization problem and therefore cannot be transfered to other domains without accepting a bad performance, or updating the training data set to fit the new domain. Updating (extending or modify) the training also require a complete training from scratch. These drawbacks make ML algorithms useful only in narrow situations where changes are not required and the training data is static and unbiased.
+Because handcrafting sentiment analysis requires a lot of effort, researchers turned to approaches that offload the labor-intensive part to machine learning (ML). However, this results in a new challenge, namely: gathering a \emph good data set to feed the machine learning algorithms for training. Firstly, \emph good data set needs to represent as many features as possible, otherwise, the algorithm will not recognize it. Secondly, the data set has to be unbiased and representative for all the data of which the data set is a part of. The data set has to represent each feature in an appropriate amount, otherwise, the algorithms may discriminate a feature in favor of other more represented features. These requirements are hard to fulfill and often they are not\cite{hutto2014vader}. After a data set is acquired, a model has to be learned by the ML algorithm, which is, depending on the complexity of the algorithm, a very computational-intensive and memory-intensive process. After training is completed, the algorithm can predict sentiment values for new pieces of text, which it has never seen before. However, due to the nature of this approach, the results cannot be comprehended by humans easily if at all. ML approaches also suffer from a generalization problem and therefore cannot be transferred to other domains without accepting a bad performance, or updating the training data set to fit the new domain. Updating (extending or modify) the model also requires complete retraining from scratch. These drawbacks make ML algorithms useful only in narrow situations where changes are not required and the training data is static and unbiased.
 
 % naive bayes
 % - simple (vader)
 % - assumption: feature probabilties are indepenend of each other (vader)
-The Naive Bayes (NB) classifier is one of the simplest ML algorithms. It uses Bayesion probabilty to classify samples. This requires the assumption that the propabilities of the features are independend of oneanother. %which often they are not because languages have certain structures of features.
+The Naive Bayes (NB) classifier is one of the simplest ML algorithms. It uses Bayesian probability to classify samples. This requires the assumption that the probabilities of the features are independent of one another. %which often they are not because languages have certain structures of features.
 
 % Maximum Entropy
 % - exponential model + logistic regression (vader)
 % - feature weighting through not assuming indepenence as in naive bayes (vader)
-Maximum Entropy (ME) is a more sophisticated algorithm. It uses a an exponential model and logistic regression. It distinguishes itself from NB by not assuming conditional indepenence of features. It also supported weighting of features by using the entropy of features.
+Maximum Entropy (ME) is a more sophisticated algorithm. It uses an exponential model and logistic regression. It distinguishes itself from NB by not assuming conditional independence of features. It also supported weighting of features by using the entropy of features.
 
 %svm
 %- mathemtical anspruchsvoll (vader)
 %- seperate datapoints using hyper planes (vader)
 %- long training period (other methods do not need training at all because lexica) (vader)
-Support Vector Machines (SVM) uses a different approach. SVM put datapoints in an $n$-dimentional space and differentiates them with hyperplanes ($n-1$ dimentional planes), so datapoints fall in 1 of the 2 halfs of the space divided by the hyper plane. This approach is usually very memory and computation intensive as each datapoint is represented by an $n$-dimentional vector where $n$ denotes the number of trained features.
+Support Vector Machines (SVM) uses a different approach. SVMs put data points in an $n$-dimentional space and differentiates them with hyperplanes ($n-1$ dimensional planes), so data points fall in 1 of the 2 halves of the space divided by the hyperplane. This approach is usually very memory and computation-intensive as each data point is represented by an $n$-dimentional vector where $n$ denotes the number of trained features.
 
 %generall blyabla, transition to vader
 
-In general, ML approaches do not provide an improvment over hand crafted lexicon approaches as they only shift the time intensive process to training data set collections. Furthermore, lexicon based approaches seem to progressed further in terms of coverage and feature weighting. However, many tools are not specifically tailored to social media text analysis and leak in coverage of feature detection. 
+In general, ML approaches do not provide an improvement over hand-crafted lexicon approaches as they only shift the time-intensive process to training data set collections. Furthermore, lexicon-based approaches seem to have progressed further in terms of coverage and feature weighting. However, many tools are not specifically tailored to social media text analysis and leak in coverage of feature detection. 
 
 %vader (Valence Aware Dictionary for sEntiment Reasoning)(grob) \cite{hutto2014vader}
 % - 2014
@@ -465,7 +463,7 @@ In general, ML approaches do not provide an improvment over hand crafted lexicon
 %   - disabliguation of words if they have multiple meanings (contextual meaning)
 
 \textbf{VADER}\\
-This shortcoming was addressed by \citeauthor{hutto2014vader} who introducted a new sentiment analysis tool: Valence Aware Dictionary for sEntiment Reasoning (VADER)\cite{hutto2014vader}. \citeauthor{hutto2014vader} acknowledged the problems that many tools have and designed VADER to leverage the shortcomings. Their aim was to introduce a tool which works well in the social media domain, provides a good coverage of features occuring in the social media domain (acronyms, initialisms, slang, etc.), and is able to work with online streams (live processing) of texts. VADER is also able to distinguish between different meanings of words (WSD) and it is able to take sentiment intensity into account. These properties make VADER an excellent choice when analysing sentiment in the social media domain.
+This shortcoming was addressed by \citeauthor{hutto2014vader} who introduced a new sentiment analysis tool: Valence Aware Dictionary for sEntiment Reasoning (VADER)\cite{hutto2014vader}. \citeauthor{hutto2014vader} acknowledged the problems that many tools have and designed VADER to leverage the shortcomings. Their aim was to introduce a tool that works well in the social media domain, provides good coverage of features occurring in the social media domain (acronyms, initialisms, slang, etc.), and is able to work with online streams (live processing) of texts. VADER is also able to distinguish between different meanings of words (WSD) and it is able to take sentiment intensity into account. These properties make VADER an excellent choice when analyzing sentiment in the social media domain.
 %The authors used a lexicon based approach as performance was one of the most important reuqirements. 
 
 
@@ -480,13 +478,13 @@ This shortcoming was addressed by \citeauthor{hutto2014vader} who introducted a
 % ursprüngliches paper ITS, wie hat man das früher (davor) gemacht
 \subsection{Trend analysis}
 
-When introducing a change to a system (experiment), one often wants to know whether the intervention achieves its intended purpose. This leads to 3 possible outcomes: a) the intervention shows effect and the system changes in the desired way, b) the intervention shows effect and the system changes in an undesired way, or c) the system did not react at all to the change. There are multiple ways to determine which of these outcomes occur. To analyze the behavior of the system, data from before and after the intervention as well as the nature of the intervation has be aquired. The are multiple ways to run such an experiment and one has to choose which type of experiment fits best. There are 2 categories of approaches: actively creating an experiment where one design the experiment before it is executed (for example randomized control trials in medical fields), or using existing data of an experiment which was not designed beforehand or where setting up a designed experiment is not possible (quasi-experiment).
+When introducing a change to a system (experiment), one often wants to know whether the intervention achieves its intended purpose. This leads to 3 possible outcomes: a) the intervention shows an effect and the system changes in the desired way, b) the intervention shows an effect and the system changes in an undesired way, or c) the system did not react at all to the change. There are multiple ways to determine which of these outcomes occur. To analyze the behavior of the system, data from before and after the intervention as well as the nature of the intervention has to be acquired. The are multiple ways to run such an experiment and one has to choose which type of experiment fits best. There are 2 categories of approaches: actively creating an experiment where one design the experiment before it is executed (for example randomized control trials in medical fields), or using existing data of an experiment that was not designed beforehand, or where setting up a designed experiment is not possible (quasi-experiment).
 
-As this thesis investigates a change which has already been implemented by another party, this thesis covers quasi-experiments. A tool that is often used for this purpose is an \emph{Interrupted Time Series} (ITS) analysis. The ITS analysis is a form of segmented regression analysis, where data from before, after and during the intervention is regressed with seperate line segements\cite{mcdowall2019interrupted}. ITS requires data at (regular) intervals from before and after the intervention (time series). The interrupt signifies the intervention and the time of when it occured must be known. The intervention can be at a single point in time or it can be streched out over a certain time span. This property must also be known to take it into account when designing the regression. Also, as the data is aquired from an quasi-experiment, it may be baised\cite{bernal2017interrupted}, for example seasonality, time-varying confunders (for example, a change in measuring data), variance in the number of single observations grouped together in an interval measurement, etc.. These biases need to be addressed if present. Seasonality can be accounted for by subtracting the average value of each of the months in succesive years (i.e. subtract the average value of all Januaries in the data set from the the values in Januaries).
+As this thesis investigates a change that has already been implemented by another party, this thesis covers quasi-experiments. A tool that is often used for this purpose is an \emph{Interrupted Time Series} (ITS) analysis. The ITS analysis is a form of segmented regression analysis, where data from before, after and during the intervention is regressed with separate line segements\cite{mcdowall2019interrupted}. ITS requires data at (regular) intervals from before and after the intervention (time series). The interrupt signifies the intervention and the time of when it occurred must be known. The intervention can be at a single point in time or it can be stretched out over a certain time span. This property must also be known to take it into account when designing the regression. Also, as the data is acquired from a quasi-experiment, it may be baised\cite{bernal2017interrupted}, for example, seasonality, time-varying confounders (for example, a change in measuring data), variance in the number of single observations grouped together in an interval measurement, etc. These biases need to be addressed if present. Seasonality can be accounted for by subtracting the average value of each of the months in successive years (i.e. subtract the average value of all Januaries in the data set from the values in Januaries).
 %\begin{lstlisting}
 % deseasonalized = datasample - average(dataSamplesInMonth(month(datasample)))
 %\end{lstlisting}
-This removes the differences between different months of the same year thereby filtering out the effect of seasonality. The variance in data density per interval (data samples in an interval) can be addressed by using the each single data point in the regression instead of an average.
+This removes the differences between different months of the same year thereby filtering out the effect of seasonality. The variance in data density per interval (data samples in an interval) can be addressed by using each single data point in the regression instead of an average.