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%TODO inconsistent tenses
This section shows the results of the experiments described in section 3 on the data sets described in section 4. In the following pages, there 3 diagrams for each community. The diagrams capture 3 different aspects: the sentiment of answers, the vote score of questions, and the number of questions. These aspects are all measured concerning questions from new users.
This section shows the results of the experiments described in section 3 on the data sets described in section 4. In the following pages, there are 3 diagrams for each community. The diagrams capture 3 different aspects: the sentiment of answers, the vote score of questions, and the number of questions. These aspects are all measured concerning questions from new users.
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.
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The vote score also decreases prior to the change and decreases even faster afterward. However, 4 to 5 months after the change, the vote score falls into a valley for about 10 months before recovering. This can be the result of another outside factor.
By looking at the number of 1st questions, it can be said that the vote score dipped because the number of first questions spiked during the previously stated time frame. As the community suddenly receives over-proportionally many new users, the quality of interactions in the community drops for a short time. This theory would be supported by \cite{lin2017better}. While the trends for 1st and follow-up questions are stagnant before the change they improved after the change. This indicates an increase in contributions from new users and that the change works for this community.
By looking at the number of 1st questions, it can be said that the vote score dipped because the number of first questions spiked during the previously stated time frame. As the community suddenly receives a disproportionally many new users, the quality of interactions in the community drops for a short time. This theory would be supported by \cite{lin2017better}. While the trends for 1st and follow-up questions are stagnant before the change they improved after the change. This indicates an increase in contributions from new users and that the change works for this community.
In summary, the sentiment improves after the change, the vote score is inconclusive, and the number of 1st and follow-up questions improves, indicating the community benefits from the change.
\begin{figure}[H]
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\section{tex.stackexchange.com}
On tex.stackexchange.com the average sentiment is low compared to the other investigated data sets. Prior to the change the average sentiment only slightly decreases. When the change is introduced the regression takes a dip down and after the change, the average sentiment increases drastically, indicating the change has a positive effect on the community. Future data will be required to see if this upward trend continues or evens out.
In stark contrast, the vote score shows a downward trend. 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.
In stark contrast, the vote score shows a downward trend. The vote score is on a continuous downward trend with a peak 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}. 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.
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 months of -44, -32, -20, -8, 4, and 16 are local minima for the number of 1st questions, indicating seasonality in the data \cite{bernal2017interrupted}. These months are all in December when 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]
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\section{unix.stackexchange.com}
On unix.stackexchange.com the average sentiment decreases prior to the change. When the change is introduced the regression takes a small dip down, however, the average sentiment increases fast after the change, indicating the change has a positive effect.
The vote score shows a continuous downward trend. At the change date, the trend does not even move by much and continues downward at about the rate, indicating the change does not affect the vote score in this community.
The vote score shows a continuous downward trend. At the change date, the trend does not move by much and continues downward at about the same rate, indicating the change does not affect the vote score in this community.
The number of 1st questions improved after the change and turned the stagnant trend into an increasing trend. The number of follow-up questions also improved in a similar manner. This shows that new contributors ask more questions than before.
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% more data in the future will be required to determine if upward trend in the end 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.
SuperUser shows only sightly decreasing average sentiment and vote score up to the change. At the change time the regressions takes 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, 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.
The number of 1st questions decreases prior to the change and then increases dramatically, 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.
In summary, the sentiment and vote score analysis does not yield a meaningful result as the time frame after the change includes an outside factor with a huge impact. The number of follow-up questions does not seem to increase despite the number of first questions doubling, indicating that a lot of the new users are one-day-files\cite{slag2015one}. The results of this analysis are inconclusive.
\begin{figure}[H]
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\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 and the magnitude of the 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 the number of (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.