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wea_ondara
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@@ -8,8 +8,7 @@ In diagrams (a), the blue line states the average sentiment (\emph{average senti
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.
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.
\pagebreak
@@ -87,7 +86,7 @@ ServerFault shows gradually rising average sentiments prior to the change. At th
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 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.
The number of follow-up questions also sees the same course direction, falling prior to and raising after the change, albeit 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]
@@ -222,7 +221,7 @@ In summary, the sentiment improves, the vote score is unaffected, and the number
% sentiment rose in most of the communities
% the vote score is mostly uncorrelated with the change
\section*{Benefitters}
More than half of the communities show benefits from the change. The number of first questions increases in all of the 6 previously shown communities. Also, for most of these communities, the number of follow-up questions increased too. Furthermore, the sentiment ITS shows an improvement in all except 1 community. The vote score analysis yielded no meaningful results for these communities. The vote score does not change with the introduction of Stackexchange' policy, with the exception of ServerFault, however, the increase in the vote score did not last for long.
More than half of the communities show benefits from the change. The number of first questions increases in all of the 6 previously shown communities. Also, for most of these communities, the number of follow-up questions increased too. Furthermore, the sentiment ITS shows an improvement in all except 1 community. The vote score analysis yielded no meaningful results for these communities. The vote score does not change with the introduction of Stackexchange' policy, with the exception of ServerFault. However, the increase in the vote score did not last for long.
@@ -261,11 +260,11 @@ In summary, the sentiment and vote score does not seem to be affected, however,
% sentiments falling faster than before the change
\section{MathOverflow.net}
On MathOverflow the sentiment shows a constant regression before the change, however, average sentiments are low at about 10 months before the change and spike high directly before the change. When the change is introduced the regression makes a small jump up and decreases thereafter. The sentiment falls sharply at the time the change is introduced, indicating that the change negatively affected the sentiment.
On MathOverflow the sentiment shows a constant regression before the change, however, average sentiments are low at about 10 months before the change and spike high directly before the change. When the change is introduced the regression makes a small jump up and decreases thereafter. The sentiment falls sharply at the time the change is introduced, indicating that the change negatively affects the sentiment.
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}. 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}.
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 decreases, 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]
@@ -372,4 +371,4 @@ When looking at the results of SuperUser, the community stands out and shows int
\section*{Summary}
In summary, the change introduced by StackExchange clearly improved the engagement in 6 of the 10 investigated communities. Sentiment, vote score, and number (1st and follow-up) questions rose as a result. The other 4 communities do not profit from the change. Although, many statistics jump up to a higher level the downward trends are not stopped. The statistics of SuperUser show a large influx of new users about 6 months after the change sending the sentiment and vote score on a deep dive and with the decrease in new users they raise again. However, this event is not related to the change but the magnitude of the huge change in new user numbers renders the analysis incomparable.
In summary, the change introduced by StackExchange clearly improved the engagement in 6 of the 10 investigated communities. Sentiment, vote score, and number (1st and follow-up) questions rose as a result. The other 4 communities do not profit from the change. Although, many statistics jump up to a higher level the downward trends are not stopped. The statistics of SuperUser show a large influx of new users about 6 months after the change sending the sentiment and vote score on a deep dive and with the decrease in new users they raise again. However, this event is not related to the change and the magnitude of the change in new user numbers renders the analysis incomparable.