Who Stays Longer in community QA Media?
- Analysis of User Participant Behavior in cQA Yoshiyuki Shoji1 , Sumio Fujita2 , Akira Tajima2 , and Katsumi Tanaka1
1
Department of Social Informatics
Graduate School of Informatics, Kyoto University
Kyoto, Japan{shoji, tanaka}@dl.kuis.kyoto-u.ac.jp
2Yahoo Japan Corporation
Tokyo, Japan{sufujita, atajima}@yahoo-corp.jp

Abstract. Macro and micro analyses of why and when users stop asking
and/or answering questions on a community question answering (cQA)site were done for a ten years’ worth of questions and answers posted on
Yahoo! Chiebukuro (Japanese Yahoo! Answers), the biggest cQA site inJapan. The macro analysis focused on how long participants were activein the QA community from the viewpoints of several user characteristics. In turn, the micro analysis focused on how the participants behaviors changes. The behaviors of both askers and answerers were foundto change over the time of their active participation: the askers tended
to expand the range of categories for which they asked questions while
the answerers tended to contract the range of categories for which theyanswered questions.

1

Introduction

The popularity of community question answering (cQA) media seems to havedeclined. More than 15 years have passed since online cQA sites appeared. Suchsites quickly became popular and one of the most successful types of consumer
generated media, serving as a social knowledge community. Their decline in
popularly is attributed to the rapid growth of other social network services (e.g.,
Twitter and Facebook) and the appearance of specific knowledge services (e.g.,
Stack Overflow). For instance, Yahoo! Chiebukuro (Japanese Yahoo! Answers)3 ,the biggest cQA site in Japan, continues to lose users. As shown in Figure 1, thenumber of posts and new users (askers + answerers) started decreasing aroundthe beginning of 2012. The decreasing number of new answerers is particularly
important as it could indicate the beginning of a negative spiral; no one wants
to ask in a community without answerers.
Nevertheless, cQA sites continue to be considered useful information resources: cQA contents frequently appear at the top of search results, and many3

http://chiebukuro.yahoo.co.jp/

2

Yoshiyuki Shoji, Sumio Fujita, Akira Tajima, Katsumi Tanaka( # Users )( # Questions )250,000

1,600,000
1,400,000

200,000

1,200,000
1,000,000

150,000

800,000
100,000

600,000

Staying Time (month)
Average
Mode
Median9.98
1
20

Table 1. User Staying Time

400,000

50,000200,000

0

0

New Users
New Answerers# Questions

Fig. 1. Number of First Time Users and Posts%user
Asking only
52.8%
(10 times +) 2.2%
Answering only 14.9%(10 times +) 1.9%
Table 2. Only Ask/Answer

people read them. For instance, Yahoo! Japan reports that there were 714,000,000
Yahoo! Chiebukuro page views in October 2014 4 . This number is much larger
than the number of posts in the same month, 524,000. This indicates that cQA
contents are still of interest to read-only viewers. Additionally, cQA is a useful
information source for researchers. The contents of cQA sites are a good corpus for many research areas, such as question answering systems, information
retrieval, and community analysis. For cQA to continue as a useful resource, thedecline in the number of users must be reversed.
We analyzed data on cQA users to clarify their reasons for leaving the cQA
community. Such understanding should be helpful in stopping them from leaving.
We used a dataset obtain from Yahoo! Chiebukuro that contains ten years’ worthof data on users, questions, and answers. We conducted basic statistical analysis
at the macro level and sequential user behavior analysis at the micro level. The
first step was to statistically compare the data on users who soon left with thosefor users who stayed longer from several aspects. The next step was to model
the sequence of actions for users from when they started using the site to when
they stopped using the site. To clarify our analysis, we distinguished between
long-stay users and short-stay users. Moreover, we compared the user s monthly
behavior from joining to leaving the community.
The rest of this paper is organized as follows. Section 2 describes related
work. Section 3 explains our method and presents the results of basic statisticalanalysis. Section 4 explains the comparison of early-phase behavior and latephase behavior. Section 5 explains our findings and how they might be utilized.
We conclude with a summary of the key points in Section 6.

4

http://i.yimg.jp/images/marketing/portal/paper/media sheet open.pdf

Who Stays Longer in community QA Media?

2

3

Related Work

This section introduces research on cQA service analysis in Section 2.1, researchon cQA user motivation on Section 2.2, and research on sequential behavior
analysis and phase modeling in Section 2.3.
2.1

cQA Services Analysis

Community question answering is a hot research topic in many areas [20][6].
How to find experts on cQA sites is a typical topic in knowledge community
research [21][22][15]. Another typical topic is how to support cQA services. For
instance, a question recommendation function helps keep answerers connected
to a cQA site [13]. Yang et al. [23] used user lifespan as a tool for comparing cQA
communities. User lifespan is tightly related to when and why a user leaves thecommunity. Data on cQA is also used for research in other areas, such as corpus.
Several Web information retrieval conferences have included question-answering
tracks. For instance, the NTCIR (NII Test Collection for IR Systems) project
circulated a cQA dataset as a dataset for a factoid-based retrieval system [5].
There has also been research on the relationship between cQA sites and othersocial media sites. Morris et al. [19] analyzed data on users who asked questions
on an SNS site rather than on a cQA one. One of their findings was that somequestions are better suited for a cQA site (e.g., excessively private questions
and political questions) while some are better suited for an SNS site (e.g., social
invitations and context-dependent questions).
2.2

cQA User Motivation

One of the biggest research topics related to the analysis of cQA services isclassification of question types and estimation of the askers’ intents. Many researchers have proposed rules for classifying questions and asker intents. Kim etal. [11] classified questions as information, suggestion, opinion, or other for use
in estimating the best answer automatically. Harper et al. [8] classified questions
into factual, opinion, and advice for use in finding expert answerers on cQA
sites. Both classification methods cover opinion. This is because askers on cQA
sites often want not only factual information, but also the opinions of others.
Liu et al. [17] classified cQA asker intent into navigational, informational, transactional, and social. This classification method supports the conventional Web
search query classification [12]. It also highlights a characteristic of cQA sites;that is, the diﬀerence between web search users and cQA users is whether they
have social intent. Chen et al. [4] also focused on the social intent of the asker,
such as subjective, objective, and social. These research eﬀorts generally focusedon investigating the reasons that cQA users ask questions. Our work aims tobroaden this target; focusing not only on the askers but on all users, including
the answerers.
Harper et al. [7] observed that users of cQA sites have two general typesof intent. One is an informational intent, and the other is a conversational

4

Yoshiyuki Shoji, Sumio Fujita, Akira Tajima, Katsumi Tanaka

intent. Users who have an informational intent tend to ask specific questions
that have a clear answer. Those who have a conversational intent want to communicate through cQA sites, and they tend to ask questions that have no clear
answer. Harper et al. asserted that users can be classified by using category, text,
and user information. They also asserted that conversational questions have low
archival quality. Aikawa et al. [2] proposed a method for sorting subjective questions in the same way. Liu et al. [14] discovered the same direction for askers who
switch from web search to cQA asking; that is, they ask informational questions
more than sticky askers, i.e., those who ask question on a cQA site over a long
period of time.
2.3

Sequential Behavior Analysis and Phase Modeling

There has been much research on the analysis of user behavior by using sequential
action modeling techniques, not only for cQA but also for other services. One
typical example is user modeling for electronic commerce sites. Some researchers
have split the total user time into sessions and then created a sequential model
for use in estimating whether the user will buy something.
They considered many users actions to be features, such as page transitions,click-throughs, and search queries [10]. Several modeling methods have been used
for this kind of situation. For instance, the hidden Markov model has been widelyused for modeling the behaviors of web searchers and web service users [18].
Hassan et al. used relational Markov models [3] to model Web user behavior asa means to enhance Web navigation [9].

3Macro Analysis of cQA User Behaviors

In this section, we explain our macro analysis of users who stop using a cQA
service. We compared two types of users: those who use the service for a relatively
long time and those who use it for a relatively short time. To do this, we set thefollowing research question and hypotheses:
RQ1: How do users who leave soon diﬀer from those who stay longer?
H1.1: They make diﬀerent types of contributions.
H1.2: The range of categories in which they participate diﬀers.
H1.3: Their satisfactions and rewards diﬀer.
To answer this research question and to test these hypotheses, we used a massive
dataset to analyze user behavior, such as the characteristics of users who leave
the QA community. Section 3.1 describes the dataset, and section 3.2 explains
our analysis.
3.1

DatasetThe Yahoo! Chiebukuro (Japanese Yahoo! Answers) is the biggest online question answering community in Japan and is freely available to all Yahoo! Japanusers. It began operation in 2004. The dataset contained slightly more than ten
years’ worth of data (March 2004 to December 2014).

Who Stays Longer in community QA Media?

5– Questions: 84,123,965 questions that received one or more answers– Answers: 224,969,887 answers to the questions– Users: 10,391,194 anonymized users who had asked and/or answered one ormore questions
Questions that do not receive an answer are deleted from the system, so thedataset contained only questions that received one or more answers. An Asker
has the option to specify the best answer (BA). If the asker does not specify aBA, other users can specify one by voting. Yahoo! Chiebukuro has three levelsof categories. The top 16 categories are shown in Table 4.
Since our research focused on users leaving the cQA community, we neglected
users still using the site. It has been reported that a certain percentage of users
who leave for a while never come back [23]. We assumed that the threshold is six
months; the dataset contained user data from March 2004 to December 2014, so
we eliminated users who asked or answered one or more times after June 2014.
In this dataset, 87.85% of the users had already stopped using the site. The
average staying time was 9.98 months, as shown in Table 1.
3.2

Basic Statistics

To tackle research question 1, to clarify the diﬀerence between shot-stay users
and long-stay users, we verify three hypotheses.
H1.1: Diﬀerence in ContributionsFirst of all, we set a simple hypothesis: a user s contribution, such as frequency and ratio of asking and answering, reflects his or her length of stay. A
breakdown of the user contributions is shown in Table 3. One simple findingis that many Yahoo! Chiebukuro users were light users; they used it once andthen stopped using the service. A third or more of the users left the community
after asking or answering once. Some were askers who asked only once and left
the service. Apparently, they had a problem and they used the service to find asolution. Once they had a solution, they stopped using the service. There weresome heavy users as well. They used the service for a long time and frequently
post both questions and answers.
Users of cQA sites can contribute as an asker or answerer or as both. The
relationship between staying time and the ratio of asking and answering is shownin Figure 6. The horizontal axis represents the number of months that the userused the service (binned with month.). The vertical axis represents the average
ratio of questions to answers that users posted. The ratio of asking correlates
significantly with staying time; the rank correlation coeﬃcient was −0.88. Users
who stayed longer tended to answer more than to ask. Moreover, they posted
both questions and answers, as opposed to inactive short-stay users who posted
either a few questions or a few answers. Few users only asked or answered morethan ten times (see Table 2). Another phenomenon that can also be seen is thatusers in cQA diﬀerentiate into two types: asker and answerer. It is unusual tofind users who post both questions and answers in similar numbers. (see Figure5)

6

Yoshiyuki Shoji, Sumio Fujita, Akira Tajima, Katsumi Tanaka

The number of posts per month shows the characteristics of askers and answerers. Figure 4 shows the relationships between question and answer posting
frequencies and staying time. It shows that it is generally diﬃcult to continue
posting very frequently for long time. While users who stay longer post at theirown pace, the answerers tend to be more frequent posters later in the staying
time. The number of heavy answerers who posted more than 100 times per monthand stayed 3 years or more was 2011, and the number of users who stayed 5 years
or more was 861. The corresponding numbers for askers were much smaller: 82
and 37, respectively. Thus, users who use cQA sites for a longer period apparently like answering more than asking.

Answers
Category Diversity of Answers

Staying Time (months)

Staying Time (months)

Answerer
Staying Time (months)

Fig. 2. Category Diversity vs Staying TimeBA rate (receive) (%)

Category Diversity of Questions

Questions

BA rate (select) (%)

Asker

Staying Time (months)

Q / MonthAnswers

Asking Answering Both
Maximum
29,757
Median8
Average7.96
Mode1
only 1 time
35.7%
10 times or less 87.8%
50 times or less 97.6%
100 times or more 1.1%

A / Month

Both(Q or A)

QA / Month

Fig. 3. Ratio of Giving or Receiving
Best Answer vs Staying Time

Questions

Staying Time (months)

Fig. 4. Number of Posts Per Monthvs Staying Time353,126 362,692
733
741
20.36 28.32
01
14.9% 37.0%
86.9% 78.0%
95.3% 92.9%
2.8%
4.0%

Table 3. Breakdown of User Contributions

Who Stays Longer in community QA Media?

7

H1.2: Diﬀerence in Category Participation
The categories on a cQA site directly reflect the users’ interests. Moreover,the interests may aﬀect user behavior. To test this, we defined a category for eachuser on the basis of how many times the user posted answers and/or questions
in each category. The defined category was the one with the most posts. If a user
posted a similar number of questions and/or answers to two or more categories,
we randomly set the user’s category to one of them.
Table 4 shows the details for each category. The staying times ranged from 5.3
to 11.6 months. The users who were defined with a daily life category tended tostay longer: the top three categories by staying time were “Entertainment andHobbies,” “Life, Love, and Human Relationship,” and “Sports, Outdoor, andCars.” Conversely, the users who were defined with more serious categories, suchas “Business, Economics, and Money,” “Manner and Ceremony,” and “Computer
Technology,” tended to stay for a shorter time.
Another factor related to staying time was the activeness of the community:
the correlation coeﬃcient between staying time and the number of posts was
0.91, and the correlation between staying time and the number of users was
0.88. Each category had a diﬀerent distribution ratio of long-stay and short-stayusers. The columns “< 0.5 (0.5 year or less),” “0.5 – 1 (0.5 – 1 years),” “1 - 3(1 – 3 years),” and “> 3 (more than 3 years)” shows the component ratios. The
categories with many short-stay users seem better suited for askers with sudden
problems. The top three categories in this regards were “Computer Technology,”“Yahoo! Japan,” and “Manner and Ceremony.” Each of them contained manyquestions that needed a quick answer, like “My computer has broken down;
what should I do?” and “A relative has died; how much money should I give asa condolence gift.”
Moreover, the average length of posts in each category was inversely correlated with staying time. Most of the long-stay users tended to participate in shortand frequent communication rather than lengthy discussions. Several researchers
have observed that cQA users generally have two types of intent: informationaland conversational. This is highly related to the categories. Harper et al. [7]pointed that intent can be classified into informational and conversational only
by category with an accuracy of 70 %. For analysis, we classified 100 randomly
selected QA pairs for each category by hand. The ratio of informational questions(“% Info” in Table 4) did not correlate with staying time (R = −0.00). Although
the component percentages of users in the community correlated with staying
time, the ratio of long-stay users was low in the informational categories. The
percentage of users who stayed three or more years had a weak inverse correlation with % info (R = −0.28). As another perspective, we focused on category
diversity. Adamic et al. pointed that the diversity of categories in which a user
posts questions and/or answers indicates whether a user is a specialist or not [1].
That is, the diversity of questions and answers represents the range of the user’s
interests and knowledge. We calculated the entropy of each user on the basis of

8

Yoshiyuki Shoji, Sumio Fujita, Akira Tajima, Katsumi Tanaka
Category

Staying % info # users

Entertainment and Hobbies
Life, Love, and Human RelationshipSports, Outdoor, and Cars
LivingKids and Schools
Health, Beauty, and Fashion
Internet, PC, and Electronics
Local, Travel, and Trip
Knowledge, Education, and Science
News, Politics, and Global
Jobs and Career
OthersBusiness, Economics, and Money
Manner and Ceremony
Yahoo! Japan
Computer Technology

11.6
10.7
10.3
9.1
8.9
8.4
7.9
7.9
7.7
6.8
6.6
6.3
6.1
6.0
5.7
5.3

# board <0.5 0.5–1 1–3 >3 Length

66% 2,328,386 17,571,681 43%
0% 1,750,675 8,255,538 46%
48% 1,094,271 6,805,630 48%
77% 1,092,231 7,303,245 44%
25% 1,015,797 6,193,570 47%
68% 843,838 7,185,539 54%
82% 1,204,661 8,348,942 37%
56% 707,311 3,887,467 60%
80% 794,644 3,848,875 48%
27% 621,021 2,984,093 62%
41% 721,055 2,360,837 49%
9% 541,983 3,498,145 64%
51% 495,974 2,065,734 69%
28% 463,642 1,283,267 72%
31% 430,842 2,110,650 75%
92% 358,264
420,537 81%

35% 10% 13%
34% 9% 11%
38% 6% 8%
43% 5% 8%
36% 7% 11%
29% 7% 10%
53% 4% 6%
27% 6% 6%
39% 6% 7%
25% 6% 8%
38% 5% 7%
23% 6% 7%
19% 6% 6%
15% 6% 7%
11% 7% 8%
6% 6% 7%

68.6
181.9
75.3
108.9
85.3
111.7
107.9
80.7
155.2
96.9
146.8
44.3
121.7
142.3
85.6
255.7

Table 4. Category Detail
Question

Q 100 %

1.0
0.9
0.8
0.7

Question% Asking

Question-Answer Ratio

100%

Answer
50-50

0.6
0.5
0.4
0.3

0.1
0
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97
101
105

A 100 %

0.2

Staying Time (months)

Answer100%

Staying Time (months)

Fig. 6. Asking Answering Ratio vs Staying
Time

Fig. 5. Asking Answering Ratio
vs. Staying Timethe categories in which he or she posted questions and answers:
etp(u) = −∑ |p ∈ P (u)|cat(p) = c|c∈C

|P (u)|

log|C||p ∈ P (u)|cat(p) = c|,
|P (u)|(1)

where P (u) = p1 , p2 , p3 · is the set of posts by user u, cat(p) is the category ofpost p, and C = c1 , c2 , c3 . . . is the top level category. It drops between zero
to one by normalizing by taking logarithm |C|. The overall tendency is thata user who stays longer posts in a broader range of categories. We calculated
category diversity for both questions and answers. Spearman’s rank correlationcoeﬃcient for staying time and category diversity for both was 0.99. While bothwere correlated with staying time, they were markedly diﬀerent in terms of
distribution and detail. As shown in Figure 2, there was a sharp contrast between
askers and answerers. The most notable feature in this figure is the sparseness at

Who Stays Longer in community QA Media?

9the bottom-right corner of the “Questions” scattergram. Askers who stay longer
tend to post questions in a broader range of categories, so it is quite possible
that, if an asker continues asking questions in a specific category, he or she will
eventually run out of questions. In contrast, answerers who stay longer tend not
to post answers to such a broad range of categories. The distribution does notappear to converge or follow a normal distribution. The range of categories inwhich a user posts answers is assumed to reflect the answerers’ field of expertise.
Since no one is truly omniscient, no one can continue to answer questions in awide range of categories. Therefore, answerers who consistently post answers inseveral specialized categories tend to stay longer.
H1.3: Diﬀerence in satisfaction and reward
The satisfaction of askers and the reward to answerers have also been used
to estimate the motivation of users to continue using a cQA service. The bestanswer (BA) rate is related to satisfaction and reward [16].
For askers, indicating the BA is a way to indicate satisfaction. Askers typically select the BA when they are satisfied and may not select a BA if all theanswers are unsatisfactory. (As mentioned above, other users can then vote for
the BA.) Similarly, having his or her answer selected as the BA is rewarding
to the answerer. Answerers wanting to get more BA selections tend to answer
questions more frequently, and politely.
The BA ratios of askers and answerers are plotted against staying time in
Figure 3. The vertical axis represents the ratio of selecting or receiving a BA,
and the horizontal axis shows the staying time. For askers, there is no correlation
between the rate of selecting the BA and staying time (R = 0.12). This is because
there are many short stay users, and they tend to select the BA randomly. The
graph does show, however, that users who select fewer BAs tend to stay a shorter
length of time. The average probability of a user selecting the BA is 0.46. For
answerers, the correlation is not much better (R = 0.17), indicating that they
do not seem particularly concerned about the BA rate.
In our dataset, the average number of answers for a question was 2.7. It isthus natural for the BA rate to converge to 0.37. In other words, people who
answer questions over a long period of time do not seem eager for their answers
to be selected as the BA.

4

Micro Analysis of cQA User Behaviors

To analyze user behavior from the micro view, we compared the users’ actions
during the user action phase, from the month in which the user joined the service
to the month in which the user left the service. Again we set a research question
and three hypotheses.
RQ2: Do users change their actions during the period from when they start
using the site to when they stop using the site?
H2.1: The contribution type changes.
H2.2: The category in which they participate changes.
H2.3: The satisfaction and reward change.

10

Yoshiyuki Shoji, Sumio Fujita, Akira Tajima, Katsumi Tanaka

To test these hypotheses, we compared users’ monthly behaviors from when they
started using the site to when they stopped using it.
4.1

User Phase Analysis

Before analyzing the user action phase, we removed the data on users who posted
less than five questions and/or answers. The number of users who posted 5
times or more was 3,256,402, i.e., 31.24% of all users. We compared the monthly
activity of users from two viewpoints: staying time and period of time from start
using or stopped using. For instance, we focused on what users who stayed m
years did in the n-th month after starting to use the site or on the last action of
long-stay users before they left.
H2.1: Diﬀerence in Contributions
How users commit to the site may vary depending on the their period of use.
We focused on changes of their contribution, such as the frequency of posts andratio of asking and answering.
Figure 9 shows the ralation between their period of use and frequency of
posts. The upper graph shows the number of post per month, and the lower
graph shows the cumulative number of posts. The horizontal axis means how
many months have passed after users start using the site. It shows the behaviorof ten user groups divided by their staying time, from a year to 10 years. The
graph shows the overall trend, that is, users start using the cQA site in the active
state. They frequently post questions and answers. After that, they decrease thefrequency of posts, and finally they leave. For most users, the period when they
post with high frequency is right after joining. Both short-stay users and longstay users post about ten times in a month when they start using the service.
Their participation decreases as time goes on, although shor-stay users are easily
excited but quick to cool down again (see Table 5). The long-stay users tend tocontinuously use cQA even after their participation decreases, in contrast to theshort-stay users who post more frequently and leave abruptly.
Asking and answering also depends on the period of use. Figure 7 shows
the ratio of asking and answering in n-th month from joining the service. The
vertical axis shows the users’ asking ratio up to that time. As explained above,
most users start using cQA as askers, and increase their percentage of answering
during their using. Except the remarkable long-stay users (those who stay nineor more years), users start using the cQA site by posting 60 % questions and
40% answers, and leave the site if the ratio of asking becomes 55%. It is possiblethat they exhaust the questions and lose interest in the site. For the users who
leave after 1 year to 9 years, the speed of decreasing of asking ratio is slower.
On the other hand, users who use the cQA site for nine or more years are moreheterogeneous than the others. What we can say is that the users who use Yahoo!
Chiebukuro for 9 years or more are Early Adopters; because the service started
in 2004. They answered more frequently than most of the people joining later,
especially in the early period of the service. The amount of users were smaller atthat time, therefore they may retained communication by posting many answers
for rare questions.

0.7

35.00%

0.5

30.00%

0.4
0.3
0.2

25.00%
20.00%
15.00%
10.00%
5.00%

0.1

1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
106
111
116
121

0.00%

0
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97
101
105
109
113
117

A 100 %

11

40.00%

0.6% Best Answer% Asking

Q 100 %Who Stays Longer in community QA Media?

Staying Time (Months)

Months After Start Using< 1 year

2

3

4

5

6

7

8

9

Start as Answerer

10 yearsFig. 7. QA ratio vs n-th month

Start as Asker

Fig. 8. BA Rates on First Posts

We found that 67.76% of the users who posted five times or more, started byasking questions. As mentioned in section 3.2, many users stop using a cQA site
after they post a few questions. Others apparently started to like the site andbecome regular visitors, and some of the regular visitors become long-staying
users. There was only a small diﬀerence between the first action of short-stayusers (stay less than a year) and that of long-stay users (stay 5 or more years).
The percentage of short-stay users whose first post was a question was 62.48 %and that for long-stay users was 70.10%. That is, more long-stay users start byasking a question. The last posts of most users before they leave the site arealso asking, as 67.76% of users stop using the service after they post a question.
These facts suggest two factors: a great multitude of users post answer in theirmiddle period, and some particular users who are enthusiastic answerers post alarge amount of answers. It is possible that users who leave by asking a question
are losing their interest in the site and they just do not want to search a question
relevant to their interest. They may be tired to be commited to the cQA site.
16.00

Posts Per Month

14.00
12.00
10.00

Staying
Period of use
years starting 1 year 3 years 5 years

8.00
6.00
4.00
2.00
0.00

1

5

9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101105109113117

Months After Start Using

1

2

3

4

5

6

7

8

9

10 years

350

Number of Posts

300
250
200
150
100

1
2
3
4
5
6
7
8
9
10

12.53
10.98
10.21
9.23
9.63
12.39
12.56
10.47
7.55
14.50

3.25
2.36
2.31
2.75
2.19
3.92
3.82
4.98
1.09
0.89

1.15
1.31
1.27
1.74
1.80
2.19
2.40
2.50

0.68
1.10
1.70
1.60
0.71
0.39

50
0

1

5

9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101105109113117121

Months After Start Using

1

2

3

4

5

6

7

8

9

10 yearsFig. 9. Posts frequency vs n-th monthH2.2: Diﬀerence in Categories

Table 5. Frequency of Post and Time
PrsiodYoshiyuki Shoji, Sumio Fujita, Akira Tajima, Katsumi Tanaka
1.0

0.9

0.9

0.8

0.8

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0

Staying Time (Months)

1

2

3

4

5

Asking678

9

10 years

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
106
111
116

Category Diversity

1.0

1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
106
111
116

Category Diversity

12

Staying Time (Months)

1

2

3

4

5

6

Answering

78

9

10 yearsFig. 10. Category-diversity vs Time Period

Category / Staying Years

First Post
AskingAnswering<1
1–5 5–10 <1 1–5 5–10

Last Post
AskingAnswering<1
1–5 5–10 <1 1–5 5–10

Entertainment and Hobbies
12.6% 13.1% 8.3% 8.9% 7.7% 4.6% 10.4% 10.3% 7.7% 10.7% 7.8% 5.1%
Internet, PC, and Electronics
6.7% 9.3% 11.0% 2.0% 1.6% 2.0% 7.5% 11.4% 13.5% 2.4% 2.0% 2.2%
Life, Love, and Human Relationship 7.1% 4.9% 2.8% 5.7% 4.4% 3.1% 5.3% 4.4% 3.5% 7.0% 5.7% 5.7%
Living
5.0% 6.8% 8.4% 2.6% 2.3% 3.0% 4.8% 7.1% 9.3% 3.3% 2.6% 2.9%
Health, Beauty, and Fashion
6.2% 6.6% 6.5% 3.0% 2.4% 2.9% 5.7% 6.3% 5.5% 3.5% 2.3% 2.1%
4.0% 5.5% 6.2% 3.0% 2.8% 3.4% 3.5% 5.1% 6.0% 3.5% 3.0% 3.2%
Sports, Outdoor, and Cars
Knowledge, Education, and Science 3.9% 4.1% 4.0% 1.8% 1.5% 1.8% 3.5% 4.2% 4.0% 2.1% 1.5% 1.4%
Local, Travel, and Trip
2.4% 3.8% 4.1% 1.0% 1.0% 1.3% 2.5% 4.8% 6.4% 1.2% 1.2% 1.5%
Kids and Schools
3.8% 3.8% 3.6% 2.4% 2.3% 2.6% 3.0% 3.4% 2.6% 2.9% 2.5% 2.4%
Others
2.2% 1.2% 0.9% 1.8% 0.7% 0.6% 1.8% 1.0% 0.7% 2.2% 0.9% 0.6%
News, Politics, and Global
1.0% 0.7% 0.7% 1.3% 0.9% 0.9% 1.0% 0.8% 0.7% 1.6% 1.2% 1.2%
Jobs and Career
2.2% 2.6% 2.5% 0.9% 0.8% 0.9% 1.8% 2.6% 2.5% 1.0% 0.9% 0.9%
Yahoo! JAPAN
2.4% 2.6% 4.6% 1.6% 0.9% 1.6% 1.9% 1.0% 0.8% 1.8% 0.5% 0.3%
Business, Economics, and Money 1.7% 2.5% 3.4% 0.7% 0.6% 0.8% 1.6% 2.6% 3.6% 0.8% 0.6% 0.7%
Manner and Ceremony
0.9% 1.3% 1.9% 0.7% 0.6% 0.8% 0.8% 1.1% 1.5% 0.8% 0.6% 0.7%
Computer Technology
0.4% 0.6% 0.8% 0.1% 0.1% 0.1% 0.4% 0.6% 0.8% 0.1% 0.1% 0.1%

Table 6. first Post and Last Post

The users must change the category which they commit in response to current
time period. First, we focused on the category which the user commited at thefirst posting. Table 6 shows the category and the type of first posts. As explained
above, user tend to start using the cQA site as an asker. Regardless of users’
staying time, a lot of user start using cQA in entertainment and advice categories.
These categories are good appeal to get new users. They are originally popular
categories. Let us compare the last category they commit before leave. The ratioof answering increases especially in counseling categories and Entertainment
categories. This may reflects that users who stays longer in cQA tend to lovecommunication but not serious arguments.

5

Discussion

Category diversity which users ask or answer also change with their phase. Diversity generally increases when a user posts in more categories in a balancedmanner. Therefore, it has correlation to the length of using. Users post questions

Who Stays Longer in community QA Media?

13

and answers in new categories for a while after joining the service. On some level,
increasing of the diversity stops because they stop penetrating new categories orthey posting their own specific categories disproportionately. H2.3: Diﬀerencein satisfaction and reward Users’ bahavior on getting or giving BA also depends
on their phase and staying time. The simplest hypothesis that the asker who
got good answer must stay, and answerer who got BA must stay. To test it, we
focus on their first actions. Figure 8 shows the relation between first action andstaying time by the BA. Surprisingly, in the case of the user who post a question
first, the ratio of giving BA has inverse correlation to the staying time (R =-0.82). Through the whole data, probability of giving BA at first asking time is33.54%. One of long-stay users is lower, especially in the case of staying months
is longer than 90 months. One reason is that they are early adopters and staythe site since it was immaturity. In that time, the quarity of answer might belower, and the rule of giving BA was not enough spreaded.
In the case of users who join the cQA as answerers, user who got BA atfirst post tend to stay linger(R=0.45). To get BA on first post is not easy; theprobability if 10.88%. Such answerers might imbibe a taste for getting BA as anincentive.
We identified several tendencies of long-stay and short-stay users of cQA sites
from our analysis that should be useful in keeping users active on cQA sites for alonger period of time. First of all, many users use cQA only once as an asker andleave. Keeping such users is an important issue. Related to this, we have thatanswerers tend to stay longer than askers. It may be possible to recommend acatchy question for askers that would cause them to unconsciously switch frombeing an asker to being an answerer. Most users start using cQA as askers; then,as they stay longer, they diﬀerentiate into askers and answerers over time. Foruser who regularly uses cQA for a reasonable amount of time, navigation or
recommendation should be personalized to their stance.
Our findings related to categories may also be useful for keeping users. Askers
who post questions in a wide range of categories tend to stay longer, whileanswerers who post in a narrow range of categories tend to stay longer. For
askers, personalized navigation may help them to expand the range of categories
in which they ask questions. For answerers, it is better to let them focus on
their field of expertise. cQA sites are also used to get information about various
types of amusement. Users who mainly post in entertainment-related categories
tend to stay longer. The categories in which the posts are short tend to be more
popular and attract more long-stay users. Some desultory exchanges of words inshort sentence are not useful for improving the information quality of cQA sites,
but it is useful to sustain the community in active state.
Askers who tend to select the BA tend to stay longer. In other words, askers
who feel that they did not receive a good answer tend to leave sooner. It is safe
to assume that good answers make askers more satisfied, and this encourages
them to stay longer. In contrast, answerers who stay longer are apparently lessconcerned about their BA rate.

14

Yoshiyuki Shoji, Sumio Fujita, Akira Tajima, Katsumi Tanaka

Our phase analysis revealed that most users of a cQA site are not always
active. While users who stay longer are also not always active, they tend tocome back sooner to answer or to ask. One simple way to keep users active is tosend them reminders or advertisements.

6

Conclusion

We have conducted both macro and micro analysis to clarify why and whenusers stop asking or answering questions on cQA sites. We used a dataset obtained from the Yahoo! Chiebukuro site and analyzed it both statistically andsequentially. For the macro analysis, we set three simple hypothesis related tocontribution, category, and reward and satisfaction and analyzed how long users
participate in the QA community. For the micro analysis, we analyzed how cQA
users behave by comparing users’ actions in early-phase and late-phase. We
found that askers who stay longer in cQA sites tend to ask questions in a wider
range of categories while answerers who stay longer tend to answer questions ina narrower range of categories.

Acknowledgments
This work was supported in part by the following projects: Grants-in-Aid for
Scientific Research (Nos. 24240013) from MEXT of Japan.

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