Why Stack Overflow Fails?
Preservation of Sustainability in Community Question
Ivan Srba and Maria Bielikova
Faculty of Informatics and Information Technologies
Slovak University of Technology in Bratislava
Ilkovičova 2, 842 16 Bratislava, Slovakia
Abstract — Enormous amount of knowledge sharing occurs every day in Community Question Answering
(CQA) sites, some of which became popular also among software developers and end users (e.g. Stack Overflow
or Ask Ubuntu). In spite of their overall success, we can witness emerging problems in some CQA systems – an
increasing failure and churn rate. In order to investigate this trend, we conducted a case study focused on Stack
Overflow. At first, we evaluated a community perception that indicates that the emerging problems are highly
related to the growing amount of low quality content created by undesired groups of users (i.e. help vampires,
noobs and reputation collectors). Consequently, we supported these findings by reproducible data analyses of
content and community evolution. In order to face the emerging problems, we suggest to provide the users with
novel answerer-oriented adaptive support that, in addition, involves a whole community in question answering.
These approaches represent an eminent attitude change in the existing question-answering support methods
with the aim to preserve a long-term sustainability of CQA ecosystems.
Keywords— H.3.4.e Question-answering systems; N.3.d Knowledge sharing; H.5.3.c Computer-supported
COMMUNITY QUESTION ANSWERING
With increasing popularity of online communities gathered in knowledge sharing systems (e.g.
Wikipedia, forums, and mailing lists), their new forms constantly emerge. One of the most popular among
them is the concept of Community Question Answering (CQA) sites, such as Yahoo! Answers or Stack
Overflow. Members of these communities can ask various questions, which cannot be usually answered
easily by standard information retrieval tools , while other members can provide answers to them. Besides
general CQA systems, various domain-specific communities appeared. Some of them gained high popularity
among software developers and end users, such as Stack Overflow, which is even considered as one of the
most successful CQA systems ever . Stack Overflow is particularly effective for novices to obtain answers
on conceptual or code review questions and, moreover, it can serve even as a supplement for official software
documentation if it does not exist or is only very sparse .
Currently, the existing CQA systems are perceived mainly as a successful example of collective
intelligence due to their high popularity, millions of answered questions, fast question answering process as
well as universal availability. In spite of that, some CQA systems become not as successful as they used to
be. As an example, we can take the recent evolution of composition of content and community on Stack
While the total number of new questions asked each month was growing gradually from the beginning,
we witness a noticeable change in 2014 (see Figure 1). At first, we can a see a sharp peak in the total number
of new questions around March 2014 followed by a significant drop of 13% over the next three months. At
the same time, the number of questions for which information needs of askers were not fulfilled (i.e.
questions either deleted due to their poor quality/violation of community rules, or unanswered for more than
one month after their posting) overcomes the number of questions for which askers’ information needs were
fulfilled (i.e. questions with accepted answer - AA). In the second view by means of relative proportions, we
can see the constantly increasing proportion of these deleted or unanswered questions among all new
questions, which we denote as a failure rate. It is growing rapidly – in 2011, three years after site’s
establishment, it was only 22.45% while in 2014, it was even 39.43%. The development of the failure rate
can be precisely predicted by a linear regression with a high significance (0.192 + 0.0048x; where x
corresponds to the order of month starting from January 2011; F(1,46) = 3144, p < 0.001, R2 = 0.986). It
means that the failure rate increases in average by 0.48% each month.
Fig. 1. Evolution of answering success of new questions posted each month [source: Query 1].
Not only content but also community behavior is constantly changing. Only a small fraction of the whole
community actively participates in question answering opposing the majority of lurkers (non-contributing
members of the community). In 2011, about 15.18% of all registered users posted at least one question or
answer each month in average, while in 2014, it was only 5.05% [source: Query 2]. In the detailed view on
active users (Figure 2), we can see that the number of active users was increasing until March 2014 and from
Number of questions
Deleted or Unanswered Answered without AA Answered with AA Total Count
Relative proportion of all questions
that time, it remains rather constant. The proportion of the most important stable users (users who were and
remain active) is steadily decreasing (from 41.05% in 2011 to 34.89% in 2014) in contrast to the proportion
of one-time users (users who became active just in one month), which is on the contrary increasing (from
30.80% in 2011 to 33.12% in 2014). As the result, the number of one-time users even overcame the number
of stable users for the first time in November 2014. The outflow of stable users is related to a growing number
of churn users (users who were active at least once during previous three months and became inactive for
following three months; the definition was adopted from Pudipeddi et al. ). From April to June 2014, the
number of churn users for the first time notably overcame the number of newcomers (i.e. users who became
active in the particular month, opposite to churn users). This negative trend is reflected in a churn rate (a
relative proportion of users who churned in a particular month from all active users), which increased from
12.52% in 2011 to 15.85% in 2014. Similarly as the failure rate, also the churn rate can be modelled by a
linear regression (0.121 + 0.0009x; where x corresponds to the order of month starting from January 2011;
F(1,46) = 149, p < 0.001, R2 = 0.764). It means that the churn rate increases in average by 0.09% each month.
Fig. 2. Evolution of composition of active users [source: Query 3].
Moreover, besides the aggregate numbers also direct feedback from community members in various
Internet discussions and blogs (e.g. http://michael.richter.name/blogs/why-i-no-longer-contribute-to-
stackoverflow) points out the emerging problems that prevent the long-term sustainability of Stack Overflow.
Despite seriousness of this phenomenon (i.e. the increasing failure and churn rate), it has not been well-
described yet and furthermore, we are not aware of any particular works aiming to effectively face it.
Therefore, we conducted a case study on Stack Overflow to provide a deeper insight into the evolution of
CQA communities. Following the obtained results, analyses of state-of-the-art approaches as well as our
experiences with an educational CQA system Askalot , we propose a shift in providing adaptive
collaboration support that can contribute to preserve a long-term sustainability of CQA ecosystems.
Number of users
Newcomers One-time Stable Churn Total Count
Relative proportion of all active users
CHARACTERIZING CONTENT AND USERS IN CQA
Openness of CQA systems is closely connected with the diversity of users’ expertise and activity levels
as well as with quality of the created content. This diversity is fruitful for efficient knowledge sharing among
people with different levels of expertise, but at the same time, it also prevents CQA systems from becoming
trustful archives with entirely unique and quality content. In general, users in CQA systems can be
categorized in three dimensions: (1) according to their preferred activities (askers vs. answerers); (2)
according to an amount of activity carried out in a system (active vs. passive persons, so called lurkers); (3)
and according to their knowledge (level of expertise). Since these dimensions are perpendicular, it is possible
to combine them and thus categorize users according to behavior to various user stereotypes. In order to
achieve successful question answering, it is essential that the community comprises particular types of users
(e.g. active answerers with a high level of expertise). On the other hand, some types of users are not very
desirable, although they represent a natural element of each community (e.g. users who ask many low-quality
questions). A previous study  examined dynamics of these stereotypes on the basis of data from Super
User system collected before the end of August 2011. They recognized that the composition of the
community is constantly changing – the proportion of some stereotypes in the total number of users is stable
while the proportion of other stereotypes (e.g. expert answerers and low-activity users) dynamically changes.
We suppose that the increasing failure and churn rate on Stack Overflow can be explained by a constant
evolution of proportionality of content quality and user stereotypes in time. More specifically, we
hypothesize that the undesirable types of users and their content has become too widespread and overloaded
potential answerers. In spite of several studies, which investigate evolution of individuals’ behavior (e.g.
churn prediction ), the existing research has not focused on time evolution of whole CQA communities
and their composition so far. We believe, that this perspective on CQA systems can provide us with deeper
insight into the emerging problems.
CASE STUDY: EVOLUTION OF COMMUNITY ON STACK OVERFLOW
In order to verify our hypothesis, we performed a case study on Stack Overflow, where the emerging
problems are probably most eminent. In addition, Stack Overflow plays a model role for Stack Exchange
platform, which unites a dozen of CQA sites with various topics (e.g. Ask Ubuntu, Mathematics).
Context of the Study. At first, we analyze discussions on Meta Stack Overflow, which is a specific part
of Stack Overflow with questions related to the system itself. We manually evaluated 150 questions with the
highest number of votes provided by the community (with voting score at least 7). We identified 12
questions, which directly pointed to the negative development of the community. All were posted after
March 2014, i.e. after the negative changes in the total number of questions and active users appeared. The
importance of these problems is even more highlighted by the fact that 1st and 3rd highest ranking questions
(by number of votes as of May 2015) were concerned with these topics: Why is Stack Overflow so negative
of late? (http://meta.stackoverflow.com/questions/251758/) and Question quality is dropping on Stack
Community perception indicates that the decline on Stack Overflow is a result of its increasing popularity
and openness, which has resulted into a massive arrival of users with low level of expertise. Consequently
the system was flooded with too many questions that were not interesting for other users. Furthermore, the
community identified and denominated three groups of users who are the major origin of this undesired
1. Help Vampires – users who ask a great number of questions without spending any effort to find a
required knowledge (e.g. from search engines or archives of already solved questions). Consequently,
the posted questions are often tedious or even duplicated. Help vampires are interested only in getting
answers to their questions and they do not return the help received back to the community.
2. Noobs – users with low level of expertise who create mainly trivial questions with poor quality. Noobs
overload the system with a significant amount of low-quality content and make finding of the unique
and interesting questions very difficult.
3. Reputation Collectors – users who answer as many questions as possible (commonly regardless their
insufficient knowledge in the question’s topic), primarily to gain the reputation. On one side, these
users contribute to the system (by means of assisting experts in answering uninteresting questions), on
the other side, they mutually reinforce and motivate help vampires and noobs in asking more low-
Opposing these undesired groups of users, another part of Stack Overflow community exists: the Care
Takers – experts, who want to keep the system clean and with valuable content. Care takers regularly search
for interesting questions and provide good answers to them. Their presence is essential and it is important to
provide adequate motivation to keep these users active and devoted to the community.
The community–based perception points out that the proportionality of different types of users forming
the community has put the CQA ecosystem off balance what also supports our hypothesis. We consider
community feedback as relevant, yet we decided to support it by quantitative analyses.
Quantitative Study. We employed a dataset from Stack Overflow which contains all non-anonymous
activities. The dataset is publicly available and distributed under creative commons license by means of a
dataset dump or by Data Explorer tool, which allows us to investigate data by means of SQL queries. We
decided to employ Data Explorer tool to make the analyses presented in this paper easily reproducible at any
time (all presented results are accompanied by references to queries that are publicly available and executable
at the latest versions of datasets at http://data.stackexchange.com/users/16409/ivan-srba/). Due to this
solution, it is possible to use the same methodology and continue in monitoring the further evolution of Stack
Overflow as well as all CQA systems in Stack Exchange platform.
At first, we investigated evolution of content quality in time. We consider votes provided by the
community as a relatively precise estimation of content quality. However, overall score (a difference between
positive and negative votes) can be influenced by the various time lapse between content creation and time
when the analyses are performed. Thus to preserve reproducibility of our analyses, we take into consideration
only those votes that were created within one month after the content was posted. In addition, number of
votes can be influenced by popularity of particular topics. Therefore, to estimate quality of questions/answers
more accurately, we normalized their score by a mean score obtained by other questions/answers with the
same user-assigned tags (if a post has more than one tag assigned, the score is normalized by means of each
of these tags separately and an average from all partially normalized scores is calculated). According to this
score, we divided questions and answers into four groups: low quality (with negative score); neutral quality
(with score equal to 0); good quality (with positive score which is below 1.5 multiple of the average score);
and high quality (with positive score above 1.5).
The absolute numbers of new questions asked each month with good and high quality remained stable
regardless the long-term increase in the total number of all questions (see Figure 3). As the result, their
relative proportions decreased from 30.79% in 2011 to 18.43% in 2014 and from 5.96% to 2.02%,
respectively. This decline is associated with the growth in the number as well in the proportion of low-quality
questions that constituted only 4.99% of all questions in 2011 and in 2014, it was even 16.72% (the greatest
rise was between April and May 2014 by 5.24% when also the number of low-quality questions overcame
the number of good-quality questions). The proportion of neutral questions was relatively stable during all
four years (59.94% in average). It means that the most of the increase in the total number of questions is
created by uninteresting questions with zero or even negative score. This finding confirms our hypothesis as
well as the community perception that the system is flooded by content that nobody cares about, while really
interesting content is getting rarer.
Fig. 3. Evolution of questions’ quality [source: Query 4].
Number of questions
Low Quality Neutral Quality Good Quality High Quality Total Count
Relative proportion of all questions
The increasing number of questions is not, however, reflected in the number of answers (see Figure 4).
Quite the opposite, it is possible to notice a three-month long decrease by 23.90% in the number of answers
from April to June 2014 (there are also other declines, e.g. between May and June 2013 by 8.73%,
nevertheless they are not so extensive). During this rapid decrease, the answering capacity of Stack Overflow
community returned to an average achieved in 2012 (about 280 thousands of answers per month). This
indicates that the community was not able to handle so many incoming (mainly low-quality and
uninteresting) questions what finally resulted in the continuous increase of the failure rate. Relative
proportions reveal the decrease of good and high-quality answers as well as the increase of low-quality
answers, however, they are not as significant as for questions.
Fig. 4. Evolution of answers’ quality [source: Query 5].
Secondly, we investigated the evolution of quantity of user stereotypes, which were denoted by the
community. To assign stereotypes to users, we employed a set of rules that are based on the previously
provided characteristics, which we additionally supported with the results from Furtado et al. : 1) help
vampire is a user who asked during a particular month at least two questions and did not provide any answer;
2) noob is a user who asked at least of two questions of low or neutral quality; 3) reputation collector is a
user who provided at least three answers on low-quality questions and was systematically active during at
least three days; and finally, 4) care taker is a user who was similarly active at least three days and provided
high-quality answers on high-quality questions. We identified a long-term outflow of care takers (see Figure
5). Despite the overall community growth, the proportion of care takers among all active users decreased
from 3.70% in 2011 to 1.24% in 2014. The proportion of help vampires is relatively stable with average
about 13.15%. On the other side, the proportion of noobs increased from 6.23% in 2011 to 10.11% in 2014.
Reputation collectors also became more common from 4.11% in 2011 to 5.98% in 2014. These results
support the community perception that important care takers tend to leave the system. Also we confirmed
Number of answers
Low Quality Neutral Quality Good Quality High Quality Total Count
Relative proportion of all answers
the rise of noobs and reputation collectors, nevertheless, it seems that help vampires always were a natural
element of CQA community.
Fig. 5. Evolution of stereotypes assigned to active users [source: Query 6].
In order to evaluate the relation between the failure/churn rate and content/user evolution numerically,
we calculated cross-correlation between time series representing the failure/churn rate (see Figure 1 and 2)
and relative proportions of each kind of content/users (see Figure 3, 4 and 5). To calculate cross-correlation,
the R script was employed (cross correlation was calculated by means of ccf function available in pracma
library; lag was set to -1 month to measure how content/user evolution leads the failure/churn rate; in
addition, a linear trend was removed from each time series by detrend function). It means that the obtained
results measure how well content/user development can predict changes in the failure/churn rate in the
The significant cross-correlations confirm that low-quality questions and low-quality answers lead to the
higher failure rate (see Table 1). Moreover, the correlations obtained for the churn rate confirmed that the
proportion of help vampires, noobs and reputation collectors leads to higher user churn. On the other side,
good and high quality content keeps users active and devoted to the community, nevertheless, the correlations
are in these cases just a little bit below the significance level.
Number of users
Help Vampires Noobs Reputation Collectors Care Takers
Relative proportion of all active users
Table 1. Cross-correlations between time series representing the failure/churn rate and proportion of various kinds of
content/users analyzed in the quantitative study (significant correlations at 5% level of significance are highlighted in bold).
To conclude, the results from the quantitative study confirmed the community perception that the low-
quality content and the undesired user groups are closely connected with the emerging problems. Thanks to
the common database structure in the whole Stack Exchange platform, Data Explorer tool allowed us to
perform the same analyses also with other CQA systems. For those systems that are recently undergoing
through a rapid expansion similarly as Stack Overflow previously did (e.g. Ask Ubuntu), we identified very
A SUGGESTION TO PRESERVE CQA SUSTAINABILITY
We identified several possibilities how to deal with the emerging problems. One option is to change
community rules and restrict the overall openness of CQA systems (e.g. limit a number of questions which
users can ask per week). However, this solution will solve the problems only partially (as there would be
still low-quality content, although its amount would be reduced) and temporally (as restrictions often pose
new unexpected problems). We emphasize that it is not possible to get completely rid of low quality content.
Instead of that, we suggest that CQA systems should take various content quality and users’ expertise into
Only very recently, approaches to automatic detection of low quality posts  and content abusers ,
 have been proposed to help moderators with deletion of these posts or banning these users, respectively.
These solutions might be really effective, however, in spite of their overall precision (varying from 0.70 to
0.80), they can misclassify good quality content or innocent users and as the result, taking the extreme actions
can finally lead to even more undesired and antisocial behavior . Therefore, we suggest to solve the
emerging problems in alternative and less invasive ways. At first, it is possible to adjust a reputation system
to reflect a value of contributions more accurately and thus motivate users to provide good answers on good
questions (e.g. reputation received for providing an answer can reflect corresponding question difficulty).
We introduced an example of this kind of reputation system in our recent paper .
Another possibility, which we consider even more promising, is to provide users with an appropriate
adaptive collaboration support. In the CQA domain, several approaches have been already proposed to
adaptively support effective knowledge sharing (e.g. personalized recommendation of questions ).
Unfortunately, adaptive support methods still do not reflect the emerging problems appropriately. Moreover,
some of these methods even indirectly support the undesired user groups by giving preference to their goals
instead of other users (e.g. care takers). For preserving a long-term sustainability of CQA communities, it is,
however, necessary to change an attitude in providing an adaptive support. We propose two basic approaches
how to achieve this shift.
Answerer-oriented Approaches – At first, a majority of the existing adaptive support methods can be
characterized as asker-oriented as they are either explicitly dedicated to askers or they are primarily focused
on askers’ goals while answerers’ preferences and expectations are suppressed.
The asker-oriented approach is visible especially in question routing methods (i.e. recommendation of
new questions to potential answerers). Most of the existing methods recommend questions to experts
regardless of the real question difficulty (and required minimum level of expertise for proper answering).
This approach is really successful in achieving askers’ goals (to receive a high quality answer), although it
completely overlooks those experts who prefer to answer more difficult and challenging questions within
their limited time capacities. In addition, existing methods usually route questions to potential answerers
with the same or very similar topics and thus answerers can lose motivation easily. In order to prevent this
filter bubble and to meet answerers’ expectations, diversification of recommendation should be applied .
Involvement of the Whole Community – Another drawback of existing adaptive methods is that they
involve and motivate only a small portion of community to actively participate in the question answering
process. To maintain CQA ecosystem, it is necessary to satisfy expectations of all types of users .
For illustration, we continue with an example of the question routing problem. As the existing methods
prefer users with a high level of expertise, other users are involved only very rarely while experts are easily
overloaded and capacity of other users is left unutilized. Trivial questions (especially those asked by help
vampires or noobs) can be usually answered by other users who have sufficient knowledge, but are not
necessary experts with the highest level of expertise. Moreover, existing methods usually rely on previous
user activities in the CQA system and thus they are not able to route questions to newcomers (due to a well-
known cold start problem) or to lurkers (due to lack of their sufficient activity).
In our previous work , we demonstrated a complex example how the given recommendations to
preserve CQA sustainability can be applied in the case of question routing. We introduced a novel question
routing method which involves all possible answerers by employing their public non-QA data (i.e. about me
descriptions, blogs) in order to supplement QA data (i.e. data from the question answering process in CQA
system itself) and thus provide question recommendations more precisely and even to all kinds of users.
To sum it up, positive outcomes of CQA systems (e.g. the number of questions, the average time to the
first answer and the great archives of answered questions) outperform the emerging problems that are
reflected in the increasing failure and churn rate. Their negative impact is, however, significantly growing
in the recent time. A set of easily executable analyses, which we published in the Data Explorer tool, allow
to monitor the future development of Stack Overflow and other CQA systems based on Stack Exchange
platform. The conclusion from the performed study is that openness of CQA communities leads to increasing
numbers of users who create mainly low-quality and uninteresting questions. As the result, these questions
remain very often unanswered and demotivate experts who slowly leave the community.
We propose the attitude change in adaptive support methods to deal with the emerging problems and thus
to contribute to the long-time sustainability of CQA ecosystems. This shift aims to prevent expert
overloading, to make answerers more satisfied and to optimally utilize knowledge embedded in all
community members, and can be described by two approaches:
1. instead of giving a focus only on askers and their goals, preferences and expectations of answerers
should be considered as well;
2. instead of involving only a subset of active and expert users, a whole community should be engaged in
a question answering process.
This work was partially supported by grant No. VG 1/0646/15 and KEGA 009STU-4/2014, and is the
partial result of the Research & Development Operational Programme for the project Research of methods
for acquisition, analysis and personalized conveying of information and knowledge, ITMS 26240220039,
co-funded by the ERDF.
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Ivan Srba is currently a doctoral student at the Institute of Informatics and Software Engineering,
Slovak University of Technology in Bratislava. His research interests are in the area of web-based
systems, which utilize concepts of collaboration and collective intelligence. Contact him at
Maria Bielikova is a full professor at the Institute of Informatics and Software Engineering, Slovak
University of Technology in Bratislava. Her research interests are in the area of web-based
systems with special emphasis on personalization, context awareness and collaboration. She is a
senior member of IEEE, a member of its Computer Society, senior member of ACM and member
of International Society for Web Engineering. Contact her at email@example.com.
SUMMARY QUESTIONS AND HIGHLIGHTS
1. Why Stack Overflow fails? Answering questions about sustainability in Community Question Answering.
2. Analyses of recent evolution of Stack Overflow revealed new emerging problems: increasing failure and
3. Help vampires, noobs, reputation collectors and their influence on successfulness of Stack Overflow.
4. Proposing suggestions to preserve a long-term sustainability of Community Question Answering.
5. Answerer-oriented approaches and involvement of the whole community - how to maintain Community