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@@ -6,7 +6,7 @@ This section shows the results of the experiments described in section 3 on the
In diagrams (a), the blue line states the average sentiment (\emph{average sentiment} in diagram legend) of the answers to questions from new contributors. Also, the numbers attached to the blue line indicate the number of answers to questions from new users that formed the average sentiment. The orange line (\emph{sm single ITS} in the diagram legend) represents the ITS over the whole period of the available data. As stated in section 3.2, data density variability is a factor to take into account, therefore, the orange line represents the weighted ITS. The green, red, purple, and brown lines also represent weighted ITS, however, the time periods considered for ITS before and after the change are limited to 6, 9, 12, and 15 months respectively.
Similarly, in diagram (b), the blue line represents the average vote score of the questions of new users. The number attached to the blue line indicates the number of questions that formed the average vote score. The ITS (orange, green, red, purple, and brown lines) are computed the same way as in diagrams (a).
Similarly, in diagrams (b), the blue line represents the average vote score of the questions of new users. The number attached to the blue line indicates the number of questions that formed the average vote score. The ITS (orange, green, red, purple, and brown lines) are computed the same way as in diagrams (a).
In diagrams (c), the blue line represents the number of 1st questions from new users, whereas the orange line denotes the follow-up questions from new users. The green and red lines
represent the ITS of the blue and orange lines respectively. In these diagrams, no weighting is performed as each data point has equivalent weight.
@@ -18,14 +18,14 @@ StackOverflow shows a very slight decrease in the average sentiment of time befo
The average vote score rises right before and stays fairly constant after the change. The rise of the vote score before the change indicates an outside factor other than the inspected change that improved the vote score. By looking at the ITS itself, the change heightens the base level of the vote score, but the trend is the same after the change, indicating the change did not bring a long-term effect. Either way, the vote score is not affected by the change.
The number of questions from new contributors increases after the change while before the change is fairly constant. The number of follow-up questions from new contributors declines before the change and rises after the change. Both ITS show that new contributors ask more questions than before. The graph shows a good example of seasonality in data \cite{bernal2017interrupted}. The month 0 indicates August. For the 1st questions, the months -44, -32, -20, -8, 4, and 16 are all local minima. These months are all in December. The months -31 to -27, -19 to -15, -7 to -3, and 5 to 9 show a pattern with 3 upward spikes. During December, the people of large portions of the world are going through a holiday season which may likely explain these regular dips in contribution. The number of follow up questions also shows dips in months of December.
The number of questions from new contributors increases after the change while before the change is fairly constant. The number of follow-up questions from new contributors declines before the change and rises after the change. Both ITS show that new contributors ask more questions than before. The graph shows a good example of seasonality in data \cite{bernal2017interrupted}. The month 0 indicates August. For the 1st questions, the months -44, -32, -20, -8, 4, and 16 are all local minima. These months are all in December. The months -31 to -27, -19 to -15, -7 to -3, and 5 to 9 show a pattern with 3 upward spikes. During December, the people of large portions of the world are going through a holiday season which may likely explain these regular dips in contribution. The number of follow-up questions also shows dips in the months of December.
In summary, the sentiment improves, the vote score is unaffected, and the number of questions asked by new contributors improves, suggesting that the community benefits from the change.
\begin{figure}[H]
\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}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on StackOverflow.com}
\label{stackoverflow_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
@@ -50,7 +50,7 @@ In summary, the sentiment improves, the vote score is unaffected, and the number
\section{AskUbuntu.com}
AskUbuntu sees a decrease in average sentiments prior to the change. After the introduction of the change, the ITS dips but sentiments keep rising drastically since then, indicating the change has a positive effect.
The vote score has a huge range of values prior to and after the change. Prior to the change, the vote score averages to a nearly constant trend. However, after the change, the trend takes a turn downwards. The graph indicates the vote score is lower after the change, however, due to the huge value fluctuation, a clear conclusion, whether the change does improve the vote score or not, cannot be reached.
The vote score has a huge range of values prior to and after the change. Prior to the change, the vote score averages a nearly constant trend. However, after the change, the trend takes a turn downwards. The graph indicates the vote score is lower after the change, however, due to the huge value fluctuation, a clear conclusion, whether the change does improve the vote score or not, cannot be reached.
Contrary, the number of questions asked by new users improve after the change.
The number of 1st questions slightly decreases prior to the change and starts rising after the change. The number of follow-up questions stabilizes from a slightly decreasing trend. This indicates more new users ask their first question.
@@ -59,7 +59,7 @@ In summary, the sentiment does improve after the change, as well as the number o
\begin{figure}[H]
\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}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on AskUbuntu.com}
\label{ubuntu_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
@@ -82,18 +82,18 @@ In summary, the sentiment does improve after the change, as well as the number o
% maybe: sentiments did not change drastically as seen in maths communities
\section{ServerFault.com}
ServerFault shows gradually rising average sentiments prior to the change. At the time of the change, the regression makes a jump upward and the average sentiment decreases slowly afterward. The sentiment stays largly the same before and after the change. Even though it is slowly rising at first and falling after the change, due to the small jump in sentiment at the change date, overall the sentiment value is pretty stable.
ServerFault shows gradually rising average sentiments prior to the change. At the time of the change, the regression makes a jump upward and the average sentiment decreases slowly afterward. The sentiment stays largely the same before and after the change. Even though it is slowly rising at first and is falling after the change, due to the small jump in sentiment at the change date, overall the sentiment value is pretty stable.
The vote score falls prior to the change, made a huge jump upward, and quickly returns to the levels just prior to the change. Even though, the leap at the change date is big and the ITS fits the data very well, the vote score does not improve in the long-term after the change.
The vote score falls prior to the change, made a huge jump upward, and quickly returns to the levels just prior to the change. Even though the leap at the change date is big and the ITS fits the data very well, the vote score does not improve in the long term after the change.
Despite, sentiment and vote score not being affected in the long run, the number of 1st questions sees a drastic change and improve dramatically. Prior to the change, the number of 1st questions decreases steadily, while after the change the numbers increase at the same pace as they fall prior to the change.
Despite, sentiment and vote score not being affected in the long run, the number of 1st questions sees a drastic change and improves dramatically. Prior to the change, the number of 1st questions decreases steadily, while after the change the numbers increase at the same pace as they fall prior to the change.
The number of follow-up questions also sees the same course direction, falling prior to and raising after the change, albeit not the change is not as drastic.
In summarizing, even though the sentiment and vote score are not really affected, the turn in the number of first question and follow-up questions indicates that the change positively affected the community.
\begin{figure}[H]
\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}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on ServerFault.com}
\label{fault_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
@@ -125,7 +125,7 @@ In summary, the sentiment improves after the change, the vote score is inconclus
\begin{figure}[H]
\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}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on stats.stackexchange.com}
\label{stats_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
@@ -152,13 +152,13 @@ On tex.stackexchange.com the average sentiment is low compared to the other inve
In stark contrast, the vote score shows a downward trend. The vote score is on a continuous downward trend with a peek around the change date but the vote score does not improve in the long term. Although there is a short window around the change date where vote scores are higher compared to before and after the change, this is not a result of the change but a coincidence. The vote score increases several months before the change actually occurs. The continuous downward trend with a peek around the change date does not indicate that the vote score improves in the long term. Either way, this indicates the change did not affect the vote score.
The amount of 1st questions improved after the change and turned the downward trend into an upward trend with the same grade. The number of follow-up questions does not see an improvement and continues the downward trend like before the change. This shows that more new contributors ask their 1st question than before, however, they still tend to become one-day-flies \cite{slag2015one}. Alos, the number of the 1st questions, the months of -44, -32, -20, -8, 4, and 16 are local minima, indicating seasonality in the data \cite{bernal2017interrupted}. Theses months are all in December where the people of large parts of the world are on holiday.
The amount of 1st questions improved after the change and turned the downward trend into an upward trend with the same grade. The number of follow-up questions does not see an improvement and continues the downward trend like before the change. This shows that more new contributors ask their 1st question than before, however, they still tend to become one-day-flies \cite{slag2015one}. Also, the number of the 1st questions, the months of -44, -32, -20, -8, 4, and 16 are local minima, indicating seasonality in the data \cite{bernal2017interrupted}. These months are all in December when the people of large parts of the world are on holiday.
In summary, the sentiment improves, the vote score is unaffected, and the number of 1st questions does improve, suggesting that the community benefits from the change.
\begin{figure}[H]
\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}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on tex.stackexchange.com}
\label{tex_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
@@ -192,7 +192,7 @@ In summary, the sentiment improves, the vote score is unaffected, and the number
\begin{figure}[H]
\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}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on unix.stackexchange.com}
\label{unix_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
@@ -233,13 +233,13 @@ On math.stackexchange.com the sentiment decreased before and after the change. E
Similarly, the vote score does not improve either and keeps decreasing after the change. The decrease slows down a little after the change. Also, the vote score rises several months before the change, indicating an effect of an unrelated cause. The vote score analysis is inconclusive.
Contrary, the number of questions asked by new contributors does improve. The number of 1st questions seems to stabilize a bit and is only decreasing slowly. The number of follow-up question even reverses the trend and start increasing after the change. This shows a good example of seasonality in data \cite{bernal2017interrupted}. The month 0 indicates August. For the 1st questions, the months -44, -32, -20, -8, 4, and 16 are all a local minimum. These months are Decembers. Similarly, the months -38 and -37, -26 and -25, -14 and -13, -2 and -1, and 10 and 11 are all in June and July. During both these times the people large portions of the world are going through a holiday season which may likely explain these regular dips in contribution. The graph for the follow up questions also shows dips at the same times, although the dips in December are not always as decernible.
Contrary, the number of questions asked by new contributors does improve. The number of 1st questions seems to stabilize a bit and is only decreasing slowly. The number of follow-up question even reverses the trend and start increasing after the change. This shows a good example of seasonality in data \cite{bernal2017interrupted}. The month 0 indicates August. For the 1st questions, the months -44, -32, -20, -8, 4, and 16 are all a local minimum. These months are all in December. Similarly, the months -38 and -37, -26 and -25, -14 and -13, -2 and -1, and 10 and 11 are all in June and July. During both these times, the people large portions of the world are going through a holiday season which may likely explain these regular dips in contribution. The graph for the follow-up questions also shows dips at the same times, although the dips in December are not always as discernible.
In summary, the sentiment and vote score does not seem to be affected, however, the number of question from new contributors tends to improve. This shows users seem to be more willing to interact with the community, even though the sentiment of the interactions still decreases. The change does not indicate a clear improvement according to its goal.
\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}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on math.stackexchange.com}
\label{math_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
@@ -265,13 +265,13 @@ On MathOverflow the sentiment shows a constant regression before the change, how
The votes score steadily increases prior to the change and then quickly returns to the level from 3 years before the change. However, the vote score does not change in course at the change date but several months after the change is introduced, leading to an inconclusive result.
Contrary, the number of questions asked by new contributors does improve. The number of 1st questions falls prior to the change and stabilizes to a constant trend thereafter. However, the number of follow-up questions that is constant before the change starts decreasing after the change. The number of the 1st questions, the months of -41, -29, -17, -5, and 7 are local maxima, indicating seasonality in the data \cite{bernal2017interrupted}. Theses months are all in March. Also while the number of 1st questions stablized to a constant trend, the number of followup questions descreases, indicating that the new users tend more to become one-day-flies as time passed on \cite{slag2015one}.
Contrary, the number of questions asked by new contributors does improve. The number of 1st questions falls prior to the change and stabilizes to a constant trend thereafter. However, the number of follow-up questions that is constant before the change starts decreasing after the change. The number of the 1st questions, the months of -41, -29, -17, -5, and 7 are local maxima, indicating seasonality in the data \cite{bernal2017interrupted}. These months are all in March. Also while the number of 1st questions stabilized to a constant trend, the number of follow-up questions descreases, indicating that the new users tend more to become one-day-flies as time passed on \cite{slag2015one}.
In summary, the sentiment, vote score, and the number of follow-up questions are affected negatively. Only the number of 1st questions from new contributors trend stabilizes. The change does not indicate a clear improvement according to its goal. This data set is sparse compared to the other datasets. Also, the vote scores are high compared to other datasets.
\begin{figure}[H]
\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.net}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on MathOverflow.net}
\label{matho_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
@@ -297,13 +297,13 @@ On electronics.stackexchange.com the average sentiment decreases continuously pr
Similarly, the vote score trend does not improve either and keeps decreasing after the change, however, the vote score does make a big leap upwards at the change. The vote score is trend is not affected by the change.
The number of 1st questions rises continuously prior to the change but decreases thereafter. The number of follow-up questions falls slightly prior to the change and stabilizes afterward. This indicates fewer new users, and that the change negatively impacted the number of new users. However, the number of followup questions increases slighly after the change. Eventhough the number of new user decreases after the change the amount of followup questions incrases, indicating the number of one-day-flies decreases \cite{slag2015one}.
The number of 1st questions rises continuously prior to the change but decreases thereafter. The number of follow-up questions falls slightly prior to the change and stabilizes afterward. This indicates fewer new users, and that the change negatively impacted the number of new users. However, the number of follow-up questions increases slightly after the change. Even though the number of new users decreases after the change the amount of follow-up questions increases, indicating the number of one-day-flies decreases \cite{slag2015one}.
In summary, the sentiment does not seem to be affected. The vote score continues its downward trend although on a higher level than before. The number of questions from new contributors trend does not show real improvements. This indicates that the change does not clearly improve interactions within the community.
\begin{figure}[H]
\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}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on electronics.stackexchange.com}
\label{ele_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}
@@ -328,7 +328,7 @@ In summary, the sentiment does not seem to be affected. The vote score continues
\section{SuperUser.com}
SuperUser shows only sightly decreasing average sentiment and vote score up to the change. At the change time the regressions take a dip down and the regression shows a downward trend after the change. However, the huge drop in sentiment and vote score does not align with the change date but happens 4 months after the change.
In the same time frame the number of 1st questions skyrockets to more than triple the previous levels. This is similar to the feature found in the results from stats.stackexchange.com, although this example is much more pronounced. This feature also seems to be produced by the huge influx of new users to the community. As described in \cite{lin2017better}, the quality of interactions in the community dip for a while but recovers over time. The sentiment recovers after about 13 months. The vote score also starts to recover at the same time, however not as quickly as the sentiment value. Due to this spite in the number of new users, the analysis does not yield any meaningful results.
In the same time frame the number of 1st questions skyrockets to more than triple the previous levels. This is similar to the feature found in the results from stats.stackexchange.com, although this example is much more pronounced. This feature also seems to be produced by the huge influx of new users to the community. As described in \cite{lin2017better}, the quality of interactions in the community dip for a while but recovers over time. The sentiment recovers after about 13 months. The vote score also starts to recover at the same time, but not as quickly as the sentiment value. Due to this spike in the number of new users, the analysis does not yield any meaningful results.
The number of 1st questions decreases prior to the change and then goes through the roof indicating a huge wave of new users indicating a drastic influx of new users. Data available in the future will show if the recovery at the end of the timeframe is persistent. Even though a lot of new users joined the community, the number of follow-up questions stayed largely the same.
@@ -336,7 +336,7 @@ In summary, the sentiment and vote score analysis does not yield a meaningful re
\begin{figure}[H]
\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}
\caption{An interrupted time series analysis of the sentiments of answers to questions created by new contributors on SuperUser.com}
\label{super_its}
\end{subfigure}
\begin{subfigure}[t]{0.5\textwidth}