Characterizing the Evolution of Social Computing Research
CO-WORD ANALYSIS
Characterizing the
Evolution of Social
Computing Research
Tao Wang, Zhong Liu, and Baoxin Xiu, National University of Defense Technology
Hong Mo, Changsha University of Science and Technology
Qingpeng Zhang, City University of Hong Kong
W
working sites and personalized recommender systems, have changed
An analysis of the
our daily life profoundly.1–3 New and stronger computational infrastructures
characteristics of
have boosted computational power and enabled the quantitative research that
social computing
wasn’t possible in the past,4,5 such as computational organization 6 and sentiment
analysis. Based on these improvements, social computing1 has garnered more and
more attention from researchers across multiple domains.
We can analyze social computing from
both qualitative and quantitative perspectives. Qualitative work mainly focuses on
the application areas such as virtual communities and social media dimensions.
Quantitative research focuses on collaboration networks or simple statistics dimensions. See the “Related Work” sidebar for a
discussion on past surveys.
However, the qualitative description
hardly shows the development accurately;
and the statistics studies fail to consider the
structural. Collaboration network analysis generally finds it difficult to show the
research content because it only focuses on
institutions or persons. Bibliometric analysis7 is an effective quantification method to
research looks
at both static
and dynamic
perspectives. The
article characterizes
the key features
and the evolution
of social computing
from a quantitative
perspective.
48
eb 2.0 technology and its versatile applications, such as social net-
examine the situation of a research field,
involving analysis from the statistical and
social network dimension of scientific publications, such as historiographical mapping,8 document9 or author cocitation,10
co-word analysis,11 and journal mapping.12
Here, we characterize the footprint of social
computing development from two quantitative, bibliometrical dimensions: statistics and
topology.
Data Collection
and Methodologies
In this section, we first introduced our data
collection in detail, and then present our
methods.
Data Collection
We identified a set of terms about social computing from two sources: the topics of the IEEE International Conference
on Social Computing (SocialCom) as of
2011 (see http://asesite.com/conferences/
1541-1672/14/$31.00 © 2014 IEEE
Published by the IEEE Computer Society
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Related Work
socialcom/2011) and the description of
social computing from Wikipedia (see
http://en.wikipedia.org/wiki/ Social_
computing). SocialCom provided 16
topics in three years. We extracted
the keywords from Wikipedia and the
most cited paper with “social computing”1 in the title for each topic. After excluding universal terms such as
“data mining,” “Web 2.0,” and so on,
and with the help of Fei-Yue Wang
(the author of a widely cited review of
social computing1), we identified 17
keywords to depict social computing
research. We then retrieved these terms
in two citation databases (the Science
Citation Index Expanded and the Conference Proceedings Citation Index—
Science) as of 2011, in which the term
“social computing” appeared first in
1995.13 Table 1 shows the results.
After excluding duplicate items, we
identified 2,079 items, including 1,518
proceedings papers. It should be noted
that we didn’t cover all the publications on social computing, even though
the Science Citation Index Expanded
database and Conference Proceedings
Citation Index—Science database indexed the majority of papers.
Methodologies
We adopted statistics and co-word
network analysis in this research. We
treated overlapping content as a static
characteristic to measure the proximity14 between terms as we computed
it in the whole dataset; while we used
ThemeRiver15 visualized and the cowords network to show the dynamic
characters.
We used content that overlapped between records to show the static state
of social computing. One paper often had more than one keyword. We
assume that if two papers share the
same keywords, they share the same
content to certain degree.14 If all the
keywords are the same between two
papers, the topic largely overlaps. For
SEPTEMBER/OCTOBER 2014
S
ome surveys about social computing have been conducted from both
qualitative and quantitative perspectives. Qualitative works mainly focus on the application areas such as virtual communities and social media dimensions.1 Quantitative research focus on collaboration networks2 or
simple statistics dimensions. 3–6 Corina Pascu presented a systematic empirical assessment of the creation, use and adoption of specific social computing
application areas.5 Yanxiang Xu and his colleagues analyzed the 187 papers
published at the 2009 IEEE International Conference on Social Computing and
presented one benchmark to measure the maturing level of a social computing research.6 Xiaochen Li and his colleagues summarized modeling methods
in social computing.4
References
1. M. Parameswaran and A.B. Whinston, “Research Issues in Social Computing,” J.
Assoc. Information Systems, vol. 8, no. 6, 2007, pp. 336–350.
2. T. Wang et al., “On Social Computing Research Collaboration Patterns: A Social Network Perspective,” Frontiers of Computer Science in China, vol. 6, no. 1, 2012,
pp. 122–130.
3. I. King, J. Li, and K.T. Chan, “A Brief Survey of Computational Approaches in Social
Computing,” Proc. Int’l Joint Conf. Neural Networks, 2009, pp. 2699–2706.
4. X.C. Li et al., “Agent-Based Social Simulation and Modeling in Social Computing,”
Proc. Intelligence and Security Informatics, C.C. Yang et al., eds., 2008,
pp. 401–412.
5. C. Pascu, An Empirical Analysis of the Creation, Use and Adoption of Social Computing Applications, tech. report, Inst. for Prospective Technological Studies, 2008,
pp. 1–92.
6. Y. Xu, T. Luo, and H. He, “Social Computing Research Map,” Proc. IEEE 2nd Symp.
Web Society (SWS), 2010.
example, the words in the records retrieved by “social computing” (169
records with 521 keywords) share 41
of the same words with the records retrieved by “computational social science” (19 records with 78 keywords).
Hence, “computational social science”
records overlapped “social computing” records by 53 percent (41 of 78
keywords); while the “social computing” records only overlapped “computational social science” records by
8 percent (41 of 521 keywords). This
is similar to friendships in some sense.
For example, Peter and Jack have
10 common friends. Peter has 100
friends, while Jack just has 20; therefore, the 10 common friends are more
important for Jack than Peter.
We make a ThemeRiver visualization for each term to show the trend
of social computing research. The
ThemeRiver visualization depicts thematic variations in the context of a
timeline.15 It uses a river metaphor to
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convey several key notions, for example, the width of river represents the
amount of content about the theme.
Compared with the original ThemeRiver,15 we considered the relative ratio clearer in each time slice. We use
a river metaphor to show keyword
changes year by year, in which the
length of the river is the timeline, and
the width of the river represents its relative amount. The combined width of
all rivers is 100 percent. This exhibits
the growth of each subfield relatively
in each period.
We use co-word analysis11 and network topology 16 for constructing
the content map, which represents by
the keywords of each paper. We first
analyzed the topology proprieties of
a co-word network at different time
points. Then, we identified the theme
transmission trend. Unlike common
co-word analysis,17 we tried to identify the evolution of social computing at different time snapshots. We
49
CO-WORD ANALYSIS
Table 1. Retrieved terms and the results.
Topics
2000
2001–2005
2006
2007
2008
2009
2010
2011
Total
Social intelligence (SI)
14
25
7
11
7
8
7
15
94
Social simulations (SSi)
13
39
7
16
19
24
27
16
161
Social engineering (SE)
8
16
8
10
8
17
7
6
80
Social informatics (SI2)
7
24
18
9
4
1
3
6
72
Social computing (SC)
4
10
2
13
34
41
38
27
169
Social software (SSo)
2
5
16
24
30
49
57
24
207
Computational social science (CSS)
2
3
4
1
2
3
1
3
19
Mobile social (MS)
2
1
4
8
9
29
23
22
98
Social media (SM)
0
3
1
13
19
80
107
165
388
Human computation (HC)
0
3
0
5
2
9
9
14
42
Social bookmarks (SB)
0
2
8
23
20
38
18
22
131
Folksonomy (F)
0
1
13
21
26
59
35
34
189
Wisdom of crowds (WC)
0
1
2
3
5
13
13
8
45
Social tag (ST)
0
1
0
10
37
62
67
57
234
Reality mining (RM)
0
0
1
0
6
7
7
5
26
User-generated content (UGC)
0
0
1
14
32
64
45
68
224
Crowdsourcing (CS)
Total
0
0
0
0
4
20
35
54
113
52
134
92
181
264
524
499
546
2292
* Note: Except the term “social computing” in this table, other uses of “social computing” that appear in this article without quotation marks mean the social computing field.
concisely analyzed the trends and
characters of each time snapshot.
Result 1: Static
Characteristics
Figure 1 shows the static aspect
of social computing research. We
visualized the contents overlapping
in the social-computing field based on
the methods proposed in the “Methodologies” subsection. The x-axis represents overlapping rate (OR). One
unit describes the relationship between one term and the other terms,
in which the different terms can be
identified by their color. For each
term, bars on the left represent that
they’re covered by other content,
while bars on the opposite side represent that they cover other content.
The lengths of these bars indicate
how critical the terms are. The terms
are tagged with the number on the
head of the bars (the length of left
bars are noted as a minus to make a
distinction between the two parts).
It’s evident from Figure 1 that a
term overlapping itself entirely, that
50
is, the OR is 100 percent, are represented as the longest bar. Taken as a
whole, the average overlapping rate is
33 percent, which means that there
is one-third overlapping content between each two results retrieved
by two different terms on average.
This phenomenon shows that all
the terms are members of one family in some sense. The different research items discuss the same theme
and share the same topics to some
extent. As the histogram shows, social media is crucial for almost every term (average OR reached 54
percent, minimum OR is 41 percent,
and maximum OR is 66 percent).
Compared with ThemeRiver (see Figure 2), we find that although the “social media” river is becoming wider
and wider, almost half of the studies
shared the same theme with all the
other keywords.
Result 2: Dynamic
Characteristics
Here, we further consider the elements of the ThemeRiver, along
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with the co-word network that we
constructed.
ThemeRiver
We constructed the ThemeRiver for
17 terms in Table 1, and Figure 2 used
the river metaphor to show keywords
change over the years. In these rivers,
we can easily find the relative increasing or decreasing trends of each term
during these years. “Social simulation,” “social engineering,” “social informatics,” and “social intelligence,”
emerged at the beginning—that is,
before 2000. Hereafter, “social simulation” received more attention.
After 2005, almost all of the terms
appeared.
The growth patterns were easier
to find on the shapes of the ThemeRiver. First, the rugby-shaped river
increased step by step and then decreased little by little. “Social informatics” is the typical case of this type,
appearing in 1996 (only one record),
then increasing from two (in 1998) to
18 (in 2006). After that, the term decreased to one in 2009. The second
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type is the steady river. Compared
with others, these terms mounted and
leveled off, such as “social software”
(9 percent on average), “social bookmark” (6 percent on average), and
“social tag” (10 percent on average).
These terms have no dramatic fluctuation from beginning to end. The
shrunk river is the third typical trend
in Figure 2. “Social simulation” (from
25 to 3 percent), “social intelligence”
(from 27 to 3 percent), and “social engineering” (from 15 to 1 percent) represent this term type, which grew in
the first period, then declined steadily.
Mountain climbing is the fourth type
of river, which increases in a stable
way. For example, “crowdsourcing”
first emerged in 2008 (with four records, which account for 1 percent),
then it increased to 54 records (accounting for 10 percent in 2011). The
fifth river type is a fluctuating one,
which includes “social computing”
(8 percent of papers before 2000,
few between 2000 and 2005, but
bounced back after 2007). The last
type of river is the soaring one. “Social media” is the best example—this
river boomed from 13 in 2007 to 80
in 2009, and increased markedly even
after 2009 (107 in 2010 and 165 in
2011).
Several research trends can be observed from the rivers. For example,
“social engineering” appeared before 2000. It flourished at an early
stage, while it declined later as the
focus shifted from research to applications, such as homeland security,
personal privacy, and so on.18 The
most obvious trend observed is that
social media played an increasingly
critical role in the social computing
field. The most important reason is
that social media connected individuals together,19 which is the core of social computing in some sense1 —that
is, the people connected could mirror the real society. The content the
SEPTEMBER/OCTOBER 2014
Figure 1. Histogram of overlapping content between different terms. The x-axis
represents the overlapping rate (OR). One unit describes the relationship between one
term and the other terms, in which the different terms can be identified by their color.
people created and shared provided
rich content for social computing
studies. In these studies, social media
is mainly treated as a social sensor to
detect the information, opinions, and
sentiments.20
Network Evolution
We constructed a co-word network21
with 3,542 distinctive keywords,
which we extracted from the 2,079
items, to analyze the dynamic aspects
of social computing research. The
number of keywords increased year
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by year (see Figure 3a), while isolated
keywords increased more slowly than
connected keywords (from 0 to 324
in 22 years). Few nodes connected
together at an early stage, while the
giant component of this network covered 90.2 percent of the keywords
(3,195/3,542), and they were connected by 12,936 links at last. We
tagged the node’s time by the year it
first appeared, while the links both
have the node pair’s appearance time.
We treated the same node pairs linked
at different time as different links.
51
CO-WORD ANALYSIS
100%
Crowdsourcing (CS)
User-generated content (UGC)
90%
Reality mining (RM)
80%
Social tag (ST)
Wisdom of crowds (WC)
70%
Folksonomy (F)
Social bookmark (SB)
60%
Human computation (HC)
50%
Social media (SM)
Mobile social (MS)
40%
Computational social science (CSS)
Social software (SSo)
30%
Social computing (SC)
20%
Social informatics (SI2)
Social engineering (SE)
10%
Social simulation (SSi)
0%
2000
Social intelligence (SI)
2005
2006
2007
2008
2009
2010
2011
Figure 2. The ThemeRiver. We constructed the ThemeRiver for the 17 terms shown in Table 1, using a river metaphor to show
keyword changes over the years.
4,000
3,500
600
Nodes count
Links count
Papers count
2005
λ = –2.65
R² = 0.9643
500
3,000
2007
2009
1,000
400
2,500
300
2,000
1,500
Papers count
Nodes, links count
1,000
10,000
100
200
1,000
100
2011
100
10
10
500
0
1
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
0
(a)
1
10
1
100
(b)
1
10
100
(c)
Figure 3. The co-word network. (a) Paper, node, and link counts, (b) keywords frequency distribution, and (c) keywords degree
distribution.
Besides the data shown in Figures 3a
and 3b, we analyzed the network topology information based on the giant
component, not the whole network.
52
Figure 3a shows the papers, nodes,
and links count year by year. We clearly
see that links are actually in proportion
to papers. Links can grow faster than
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nodes and can be more easily affected
than node count. The average links
nodes ratio is four (12,936/3,195),
which means that each new keyword
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8.5
3.44
8.0
7.5
3.39
7.0
3.34
6.5
6.0
3.29
5.5
5.0
2004
2005
2006
2007
2008
2009
2010
2011
2012
(a)
3.24
2004
2005
2006
2007
2008
2009
2010
2011
2012
2005
2006
2007
2008
2009
2010
2011
2012
(b)
0.20
350
0.19
300
0.18
250
0.17
0.16
200
0.15
150
0.14
0.13
100
0.12
50
0
2004
0.11
2005
2006
2007
2008
2009
(a)
2010
2011
2012
0.10
2004
(b)
Figure 4. The topology properties of the co-words network in different time snapshots: (a) diameter, (b) average shortest path
length, (c) number of multiedge node pairs, and (c) centralization.
appearance would bring four links to
the co-word network on average. Each
paper often provides three or more
keywords for indexing.
Figure 3b shows the log-log plot of
the frequency distribution of keywords.
The x-axis is the frequency and the yaxis is the keyword count, which appeared with the same frequency. This
distribution followed power-law approximately22 with a long tail. More
than 80 percent of keywords appeared
just once (2,933 in 3,542 keywords).
While the other 609 keywords appeared
SEPTEMBER/OCTOBER 2014
more than twice (3,220 times) appeared
more than half the time, they occupied
less than 20 percent, with the term “social media” (136 times) and “Web 2.0”
(124 times) being the most frequently
occurring units.
Figure 3c shows the log-log plot of
the keyword node degree distribution
at four time snapshots in 2005, 2007,
2009, and 2011, respectively. For the
giant component of the co-word network, the nodes with degree 4 played
a critical role in the network. We can
see that the amount of nodes with
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degree less than 4 climbed quickly,
while the amount of other nodes with
degree more than 4 descended with a
long tail. That means the keywords
often appeared with three other keywords. Few keywords just appeared
with one fixed keyword; while keywords appearing with more than 100
different keywords existed as well.
These keywords played the core roles.
We can use network topology proprieties to identify the trends about
the situation of research.16 Figures
4a through 4d shows four network
53
CO-WORD ANALYSIS
0.25
Before 2005
2006
2007
2008
2009
2010
2011
Forward average
Afterward average
0.20
0.15
0.10
0.05
0
Before 2005
2006
2007
2008
2009
2010
2011
Figure 5. The links between different time points. We treated keywords appearing
before 2006 as monolithic, while we treated each year after 2006 as one time point
based on the distribution of links.
topology properties from a node perspective in different time snapshots.
We can identify three characteristics about social computing research
based on the following four topology
propriety evolutions.
First, we observe a small world effect, which further confirmed the previous result of Xiaoguang Wang and
his colleagues.21 The average shortest
path length23 for all connected node
pairs in the network is 3.443, with a
diameter D of 8.23 Both numbers are
small compared to the total number of
nodes in the network (3,195). In addition, the average clustering coefficient
of the network is 0.89, indicating that
the keywords tend to form closed triplets. Almost every paper has more than
3 keywords, which could form one
cluster. These observations have shown
that the social computing co-words network possesses the small world property, even though the average shortest
path length increased little by little after
2007 from 3.27 to 3.44 (see Figure 4b).
Second, the turning point of the social computing field seems to have happened in 2008, which we can draw
from the diameter (see Figure 4a),
54
average shortest path length (see Figure 4b), and centralizations24 (see Figure 4d) of the co-word network.
Last, decentralization is another
trend of the social computing research,
even though the situation is on the opposite side between 2006 and 2008.
More topics appeared after 2008, and
the social computing field evolved more
and more subdomains, which could be
shown from the centralization changing trend in Figure 4d—ascended from
0.142 in 2006 to 0.179 in 2008 and
then descended to 0.134 in 2011—and
the number of multi-edge node pairs in
Figure 4c. The increasing rate of multiedge node pairs slowing down illustrates that the new keywords preferred
to connect to old keywords traditionally instead of connecting old keywords
together, especially after 2008. These
observations might indicate that the
topics of social computing have been
expanding after 2008, with the different topics providing different keywords
to form new research domains.
In addition to the topology from
a node perspective, link types could
reflect the changes as well. Two
link types could be identified in our
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co-word network. Some links connected the keywords with the same
time tags, which means that they first
appeared in the same year; while other
links connected the keywords with
different time tags, which indicates
that one of them appeared before and
they didn’t appear in the same paper
at an earlier time. We focused more on
the second type of links and treated
the co-word network as directed.
Figure 5 plots the network evolution
from a link perspective in different
time points. We treated keywords appearing before 2006 as a monolithic
group. While we treated each year after 2006 as one time point based on
the distribution of links. We measured the transmission by the links
amount normalized ratio between different time points, which we plotted
on the y-axis. The content at one time
point might share some keywords
that appeared previously (input links),
and be shared with the new keywords
appearing at a later time point (output
links). The red line in the figure shows
the approximate trend of the average
forward links transmission, while the
blue line represents the average afterward links transmission.
We can explain this from different
perspectives. One possible explanation is the memory effect of the papers, which is similar to a co-citation
network.22 The almost stable average forward links suggested that the
forward memory (the keywords remembered others keywords) is stable
in some sense, while the afterward
memory (the keywords remembered
by others keywords) were descending,
which was indicated by a trend of average afterward links. Another potential explanation could be made from
transmission insight. If the links from
one node to the other node in the
co-word network mean that the two
nodes transmitted something, then we
can treat this phenomenon as theme
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THE AUTHORS
transmission. The descending trend
of average afterward transmission indicated that the earlier theme transmitted more than the later period,
especially for the themes appearing
before 2005. This means that most
themes at later periods left less value
than the content at earlier periods on
average, even though the number of
papers published increased. Most papers might just follow some popular
topics and share the keywords, while
the main themes don’t reflect the issue.
I
n this article, we analyzed the
static and dynamic aspects of social computing research. Overlapping
content indicated that subfields of social computing often share the same
topics, even though they have many
different terms. There are several aspects of this research future study
could enhance. First, as our bibliographical data is retrieved by terms,
we might have missed some records
that don’t share terms which we used.
Retrieving the publications with cited
and citing patterns might be a better
approach to get more precise records.
Second, we took the same weight of
all papers, while the importance of
each paper is not the same in some
senses. Constructing weighted networks would be a promising direction to improve our research.
Acknowledgments
We thank Fei-Yue Wang and Xiaolong
Zheng for their guidance and advice in this
research. This work is supported in part
by Hunan Provincial Innovation Foundation for Postgraduate, and by the National
Natural Science Foundation of China under
grants 70771109 and 61074903.
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SEPTEMBER/OCTOBER 2014
Tao Wang is a PhD candidate in the College of Information Systems and Management at
the National University of Defense Technology, Changsha, China. He’s a visiting student in
the State Key Laboratory of Management and Control for Complex Systems at the Chinese
Academy of Sciences. His research interests include social computing, social network analysis, and cyber-enabled social movement organization. Wang has an MS in engineering from
the National University of Defense Technology. Contact him at wangtao@nudt.edu.cn.
Zhong Liu is a professor at the National University of Defense Technology. His research
interests include information management and decision-making support technology. Liu
has a PhD in engineering from the National University of Defense Technology. Contact
him at liuzhong@nudt.edu.cn.
Baoxin Xiu is an associate professor in Information Systems Engineering Laboratory at
the National University of Defense Technology. His research interests include granular
computing, and computational and mathematical organization theory. Xiu has a PhD in
management science and engineering from the National University of Defense Technology. Contact him at baoxinxiu@163.com.
Hong Mo is an associate professor at the School of Electric and Information Engineering,
Changsha University of Science and Technology. Her research interests include linguistic
dynamic systems and intelligence computing. Mo has a PhD in engineering from the Graduate University of China Academy of Sciences. Contact her at mohong72@gmail.com.
Qingpeng Zhang is an assistant professor at the City University of Hong Kong. His research interests include social computing, complex networks, Semantic Web, and Web
science. Zhang has a PhD in systems and industrial engineering from The University of
Arizona. Contact him at qingpeng.zhang@cityu.edu.hk.
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Acidification Research: A Test of the
Co-Word Method,” Scientometrics, vol.
23, no. 3, 1992, pp. 417–461.
15. S. Havre et al., “ThemeRiver: Visualizing Thematic Changes in Large
Document Collections,” IEEE Trans.
Visualization and Computer Graphics,
vol. 8, no. 1, 2002, pp. 9–20.
16. N. Shibata et al., “Detecting Emerging
Research Fronts Based on Topological Measures in Citation Networks of
Scientific Publications,” Technovation,
vol. 28, no. 11, 2008, pp. 758–775.
17. G.A. Ronda-Pupo and L.Á. GuerrasMartin, “Dynamics of the Evolution of
the Strategy Concept 1962–2008: A CoWord Analysis,” Strategic Management
J., vol. 33, no. 2, 2012, pp. 162–188.
18. L.A. Cutillo, R. Molva, and T. Strufe,
“Safebook: A Privacy-Preserving Online
Social Network Leveraging on Real-Life
Trust,” IEEE Comm., vol. 47, no. 12,
2009, pp. 94–101.
19. T. Ahlqvist, Social Media Roadmaps:
Exploring the Futures Triggered by
Social Media, VTT, 2008.
20. N. Savage, “Twitter as Medium and
Message,” Comm. ACM, vol. 54, no. 3,
2011, pp. 18–20.
21. X. Wang et al., “The ‘Small-World’
Characteristic of Author Co-Words Network,” Proc. Int’l Conf. Wireless Communications, Networking, and Mobile
Computing, 2007, pp. 3717–3720.
22. S. Redner, “How Popular is Your Paper? An Empirical Study of the Citation
Distribution,” The European Physical J.
B-Condensed Matter and Complex Systems, vol. 4, no. 2, 1998, pp. 131–134.
23. D.J. Watts and S.H. Strogatz, “Collective Dynamics of ‘Small-World’
Networks,” Nature, vol. 393, no. 6684,
1998, pp. 440–442.
24. J. Dong, and S. Horvath, “Understanding Network Concepts in Modules,”
BMC Systems Biology, vol. 1, no. 1,
2007; doi:10.1186/1752-0509-1-24.
Selected CS articles and columns
are also available for free at
http://ComputingNow.computer.org.
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56
www.computer.org/intelligent
IEEE INTELLIGENT SYSTEMS
Characterizing the
Evolution of Social
Computing Research
Tao Wang, Zhong Liu, and Baoxin Xiu, National University of Defense Technology
Hong Mo, Changsha University of Science and Technology
Qingpeng Zhang, City University of Hong Kong
W
working sites and personalized recommender systems, have changed
An analysis of the
our daily life profoundly.1–3 New and stronger computational infrastructures
characteristics of
have boosted computational power and enabled the quantitative research that
social computing
wasn’t possible in the past,4,5 such as computational organization 6 and sentiment
analysis. Based on these improvements, social computing1 has garnered more and
more attention from researchers across multiple domains.
We can analyze social computing from
both qualitative and quantitative perspectives. Qualitative work mainly focuses on
the application areas such as virtual communities and social media dimensions.
Quantitative research focuses on collaboration networks or simple statistics dimensions. See the “Related Work” sidebar for a
discussion on past surveys.
However, the qualitative description
hardly shows the development accurately;
and the statistics studies fail to consider the
structural. Collaboration network analysis generally finds it difficult to show the
research content because it only focuses on
institutions or persons. Bibliometric analysis7 is an effective quantification method to
research looks
at both static
and dynamic
perspectives. The
article characterizes
the key features
and the evolution
of social computing
from a quantitative
perspective.
48
eb 2.0 technology and its versatile applications, such as social net-
examine the situation of a research field,
involving analysis from the statistical and
social network dimension of scientific publications, such as historiographical mapping,8 document9 or author cocitation,10
co-word analysis,11 and journal mapping.12
Here, we characterize the footprint of social
computing development from two quantitative, bibliometrical dimensions: statistics and
topology.
Data Collection
and Methodologies
In this section, we first introduced our data
collection in detail, and then present our
methods.
Data Collection
We identified a set of terms about social computing from two sources: the topics of the IEEE International Conference
on Social Computing (SocialCom) as of
2011 (see http://asesite.com/conferences/
1541-1672/14/$31.00 © 2014 IEEE
Published by the IEEE Computer Society
IEEE INTELLIGENT SYSTEMS
Related Work
socialcom/2011) and the description of
social computing from Wikipedia (see
http://en.wikipedia.org/wiki/ Social_
computing). SocialCom provided 16
topics in three years. We extracted
the keywords from Wikipedia and the
most cited paper with “social computing”1 in the title for each topic. After excluding universal terms such as
“data mining,” “Web 2.0,” and so on,
and with the help of Fei-Yue Wang
(the author of a widely cited review of
social computing1), we identified 17
keywords to depict social computing
research. We then retrieved these terms
in two citation databases (the Science
Citation Index Expanded and the Conference Proceedings Citation Index—
Science) as of 2011, in which the term
“social computing” appeared first in
1995.13 Table 1 shows the results.
After excluding duplicate items, we
identified 2,079 items, including 1,518
proceedings papers. It should be noted
that we didn’t cover all the publications on social computing, even though
the Science Citation Index Expanded
database and Conference Proceedings
Citation Index—Science database indexed the majority of papers.
Methodologies
We adopted statistics and co-word
network analysis in this research. We
treated overlapping content as a static
characteristic to measure the proximity14 between terms as we computed
it in the whole dataset; while we used
ThemeRiver15 visualized and the cowords network to show the dynamic
characters.
We used content that overlapped between records to show the static state
of social computing. One paper often had more than one keyword. We
assume that if two papers share the
same keywords, they share the same
content to certain degree.14 If all the
keywords are the same between two
papers, the topic largely overlaps. For
SEPTEMBER/OCTOBER 2014
S
ome surveys about social computing have been conducted from both
qualitative and quantitative perspectives. Qualitative works mainly focus on the application areas such as virtual communities and social media dimensions.1 Quantitative research focus on collaboration networks2 or
simple statistics dimensions. 3–6 Corina Pascu presented a systematic empirical assessment of the creation, use and adoption of specific social computing
application areas.5 Yanxiang Xu and his colleagues analyzed the 187 papers
published at the 2009 IEEE International Conference on Social Computing and
presented one benchmark to measure the maturing level of a social computing research.6 Xiaochen Li and his colleagues summarized modeling methods
in social computing.4
References
1. M. Parameswaran and A.B. Whinston, “Research Issues in Social Computing,” J.
Assoc. Information Systems, vol. 8, no. 6, 2007, pp. 336–350.
2. T. Wang et al., “On Social Computing Research Collaboration Patterns: A Social Network Perspective,” Frontiers of Computer Science in China, vol. 6, no. 1, 2012,
pp. 122–130.
3. I. King, J. Li, and K.T. Chan, “A Brief Survey of Computational Approaches in Social
Computing,” Proc. Int’l Joint Conf. Neural Networks, 2009, pp. 2699–2706.
4. X.C. Li et al., “Agent-Based Social Simulation and Modeling in Social Computing,”
Proc. Intelligence and Security Informatics, C.C. Yang et al., eds., 2008,
pp. 401–412.
5. C. Pascu, An Empirical Analysis of the Creation, Use and Adoption of Social Computing Applications, tech. report, Inst. for Prospective Technological Studies, 2008,
pp. 1–92.
6. Y. Xu, T. Luo, and H. He, “Social Computing Research Map,” Proc. IEEE 2nd Symp.
Web Society (SWS), 2010.
example, the words in the records retrieved by “social computing” (169
records with 521 keywords) share 41
of the same words with the records retrieved by “computational social science” (19 records with 78 keywords).
Hence, “computational social science”
records overlapped “social computing” records by 53 percent (41 of 78
keywords); while the “social computing” records only overlapped “computational social science” records by
8 percent (41 of 521 keywords). This
is similar to friendships in some sense.
For example, Peter and Jack have
10 common friends. Peter has 100
friends, while Jack just has 20; therefore, the 10 common friends are more
important for Jack than Peter.
We make a ThemeRiver visualization for each term to show the trend
of social computing research. The
ThemeRiver visualization depicts thematic variations in the context of a
timeline.15 It uses a river metaphor to
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convey several key notions, for example, the width of river represents the
amount of content about the theme.
Compared with the original ThemeRiver,15 we considered the relative ratio clearer in each time slice. We use
a river metaphor to show keyword
changes year by year, in which the
length of the river is the timeline, and
the width of the river represents its relative amount. The combined width of
all rivers is 100 percent. This exhibits
the growth of each subfield relatively
in each period.
We use co-word analysis11 and network topology 16 for constructing
the content map, which represents by
the keywords of each paper. We first
analyzed the topology proprieties of
a co-word network at different time
points. Then, we identified the theme
transmission trend. Unlike common
co-word analysis,17 we tried to identify the evolution of social computing at different time snapshots. We
49
CO-WORD ANALYSIS
Table 1. Retrieved terms and the results.
Topics
2000
2001–2005
2006
2007
2008
2009
2010
2011
Total
Social intelligence (SI)
14
25
7
11
7
8
7
15
94
Social simulations (SSi)
13
39
7
16
19
24
27
16
161
Social engineering (SE)
8
16
8
10
8
17
7
6
80
Social informatics (SI2)
7
24
18
9
4
1
3
6
72
Social computing (SC)
4
10
2
13
34
41
38
27
169
Social software (SSo)
2
5
16
24
30
49
57
24
207
Computational social science (CSS)
2
3
4
1
2
3
1
3
19
Mobile social (MS)
2
1
4
8
9
29
23
22
98
Social media (SM)
0
3
1
13
19
80
107
165
388
Human computation (HC)
0
3
0
5
2
9
9
14
42
Social bookmarks (SB)
0
2
8
23
20
38
18
22
131
Folksonomy (F)
0
1
13
21
26
59
35
34
189
Wisdom of crowds (WC)
0
1
2
3
5
13
13
8
45
Social tag (ST)
0
1
0
10
37
62
67
57
234
Reality mining (RM)
0
0
1
0
6
7
7
5
26
User-generated content (UGC)
0
0
1
14
32
64
45
68
224
Crowdsourcing (CS)
Total
0
0
0
0
4
20
35
54
113
52
134
92
181
264
524
499
546
2292
* Note: Except the term “social computing” in this table, other uses of “social computing” that appear in this article without quotation marks mean the social computing field.
concisely analyzed the trends and
characters of each time snapshot.
Result 1: Static
Characteristics
Figure 1 shows the static aspect
of social computing research. We
visualized the contents overlapping
in the social-computing field based on
the methods proposed in the “Methodologies” subsection. The x-axis represents overlapping rate (OR). One
unit describes the relationship between one term and the other terms,
in which the different terms can be
identified by their color. For each
term, bars on the left represent that
they’re covered by other content,
while bars on the opposite side represent that they cover other content.
The lengths of these bars indicate
how critical the terms are. The terms
are tagged with the number on the
head of the bars (the length of left
bars are noted as a minus to make a
distinction between the two parts).
It’s evident from Figure 1 that a
term overlapping itself entirely, that
50
is, the OR is 100 percent, are represented as the longest bar. Taken as a
whole, the average overlapping rate is
33 percent, which means that there
is one-third overlapping content between each two results retrieved
by two different terms on average.
This phenomenon shows that all
the terms are members of one family in some sense. The different research items discuss the same theme
and share the same topics to some
extent. As the histogram shows, social media is crucial for almost every term (average OR reached 54
percent, minimum OR is 41 percent,
and maximum OR is 66 percent).
Compared with ThemeRiver (see Figure 2), we find that although the “social media” river is becoming wider
and wider, almost half of the studies
shared the same theme with all the
other keywords.
Result 2: Dynamic
Characteristics
Here, we further consider the elements of the ThemeRiver, along
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with the co-word network that we
constructed.
ThemeRiver
We constructed the ThemeRiver for
17 terms in Table 1, and Figure 2 used
the river metaphor to show keywords
change over the years. In these rivers,
we can easily find the relative increasing or decreasing trends of each term
during these years. “Social simulation,” “social engineering,” “social informatics,” and “social intelligence,”
emerged at the beginning—that is,
before 2000. Hereafter, “social simulation” received more attention.
After 2005, almost all of the terms
appeared.
The growth patterns were easier
to find on the shapes of the ThemeRiver. First, the rugby-shaped river
increased step by step and then decreased little by little. “Social informatics” is the typical case of this type,
appearing in 1996 (only one record),
then increasing from two (in 1998) to
18 (in 2006). After that, the term decreased to one in 2009. The second
IEEE INTELLIGENT SYSTEMS
type is the steady river. Compared
with others, these terms mounted and
leveled off, such as “social software”
(9 percent on average), “social bookmark” (6 percent on average), and
“social tag” (10 percent on average).
These terms have no dramatic fluctuation from beginning to end. The
shrunk river is the third typical trend
in Figure 2. “Social simulation” (from
25 to 3 percent), “social intelligence”
(from 27 to 3 percent), and “social engineering” (from 15 to 1 percent) represent this term type, which grew in
the first period, then declined steadily.
Mountain climbing is the fourth type
of river, which increases in a stable
way. For example, “crowdsourcing”
first emerged in 2008 (with four records, which account for 1 percent),
then it increased to 54 records (accounting for 10 percent in 2011). The
fifth river type is a fluctuating one,
which includes “social computing”
(8 percent of papers before 2000,
few between 2000 and 2005, but
bounced back after 2007). The last
type of river is the soaring one. “Social media” is the best example—this
river boomed from 13 in 2007 to 80
in 2009, and increased markedly even
after 2009 (107 in 2010 and 165 in
2011).
Several research trends can be observed from the rivers. For example,
“social engineering” appeared before 2000. It flourished at an early
stage, while it declined later as the
focus shifted from research to applications, such as homeland security,
personal privacy, and so on.18 The
most obvious trend observed is that
social media played an increasingly
critical role in the social computing
field. The most important reason is
that social media connected individuals together,19 which is the core of social computing in some sense1 —that
is, the people connected could mirror the real society. The content the
SEPTEMBER/OCTOBER 2014
Figure 1. Histogram of overlapping content between different terms. The x-axis
represents the overlapping rate (OR). One unit describes the relationship between one
term and the other terms, in which the different terms can be identified by their color.
people created and shared provided
rich content for social computing
studies. In these studies, social media
is mainly treated as a social sensor to
detect the information, opinions, and
sentiments.20
Network Evolution
We constructed a co-word network21
with 3,542 distinctive keywords,
which we extracted from the 2,079
items, to analyze the dynamic aspects
of social computing research. The
number of keywords increased year
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by year (see Figure 3a), while isolated
keywords increased more slowly than
connected keywords (from 0 to 324
in 22 years). Few nodes connected
together at an early stage, while the
giant component of this network covered 90.2 percent of the keywords
(3,195/3,542), and they were connected by 12,936 links at last. We
tagged the node’s time by the year it
first appeared, while the links both
have the node pair’s appearance time.
We treated the same node pairs linked
at different time as different links.
51
CO-WORD ANALYSIS
100%
Crowdsourcing (CS)
User-generated content (UGC)
90%
Reality mining (RM)
80%
Social tag (ST)
Wisdom of crowds (WC)
70%
Folksonomy (F)
Social bookmark (SB)
60%
Human computation (HC)
50%
Social media (SM)
Mobile social (MS)
40%
Computational social science (CSS)
Social software (SSo)
30%
Social computing (SC)
20%
Social informatics (SI2)
Social engineering (SE)
10%
Social simulation (SSi)
0%
2000
Social intelligence (SI)
2005
2006
2007
2008
2009
2010
2011
Figure 2. The ThemeRiver. We constructed the ThemeRiver for the 17 terms shown in Table 1, using a river metaphor to show
keyword changes over the years.
4,000
3,500
600
Nodes count
Links count
Papers count
2005
λ = –2.65
R² = 0.9643
500
3,000
2007
2009
1,000
400
2,500
300
2,000
1,500
Papers count
Nodes, links count
1,000
10,000
100
200
1,000
100
2011
100
10
10
500
0
1
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
0
(a)
1
10
1
100
(b)
1
10
100
(c)
Figure 3. The co-word network. (a) Paper, node, and link counts, (b) keywords frequency distribution, and (c) keywords degree
distribution.
Besides the data shown in Figures 3a
and 3b, we analyzed the network topology information based on the giant
component, not the whole network.
52
Figure 3a shows the papers, nodes,
and links count year by year. We clearly
see that links are actually in proportion
to papers. Links can grow faster than
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nodes and can be more easily affected
than node count. The average links
nodes ratio is four (12,936/3,195),
which means that each new keyword
IEEE INTELLIGENT SYSTEMS
8.5
3.44
8.0
7.5
3.39
7.0
3.34
6.5
6.0
3.29
5.5
5.0
2004
2005
2006
2007
2008
2009
2010
2011
2012
(a)
3.24
2004
2005
2006
2007
2008
2009
2010
2011
2012
2005
2006
2007
2008
2009
2010
2011
2012
(b)
0.20
350
0.19
300
0.18
250
0.17
0.16
200
0.15
150
0.14
0.13
100
0.12
50
0
2004
0.11
2005
2006
2007
2008
2009
(a)
2010
2011
2012
0.10
2004
(b)
Figure 4. The topology properties of the co-words network in different time snapshots: (a) diameter, (b) average shortest path
length, (c) number of multiedge node pairs, and (c) centralization.
appearance would bring four links to
the co-word network on average. Each
paper often provides three or more
keywords for indexing.
Figure 3b shows the log-log plot of
the frequency distribution of keywords.
The x-axis is the frequency and the yaxis is the keyword count, which appeared with the same frequency. This
distribution followed power-law approximately22 with a long tail. More
than 80 percent of keywords appeared
just once (2,933 in 3,542 keywords).
While the other 609 keywords appeared
SEPTEMBER/OCTOBER 2014
more than twice (3,220 times) appeared
more than half the time, they occupied
less than 20 percent, with the term “social media” (136 times) and “Web 2.0”
(124 times) being the most frequently
occurring units.
Figure 3c shows the log-log plot of
the keyword node degree distribution
at four time snapshots in 2005, 2007,
2009, and 2011, respectively. For the
giant component of the co-word network, the nodes with degree 4 played
a critical role in the network. We can
see that the amount of nodes with
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degree less than 4 climbed quickly,
while the amount of other nodes with
degree more than 4 descended with a
long tail. That means the keywords
often appeared with three other keywords. Few keywords just appeared
with one fixed keyword; while keywords appearing with more than 100
different keywords existed as well.
These keywords played the core roles.
We can use network topology proprieties to identify the trends about
the situation of research.16 Figures
4a through 4d shows four network
53
CO-WORD ANALYSIS
0.25
Before 2005
2006
2007
2008
2009
2010
2011
Forward average
Afterward average
0.20
0.15
0.10
0.05
0
Before 2005
2006
2007
2008
2009
2010
2011
Figure 5. The links between different time points. We treated keywords appearing
before 2006 as monolithic, while we treated each year after 2006 as one time point
based on the distribution of links.
topology properties from a node perspective in different time snapshots.
We can identify three characteristics about social computing research
based on the following four topology
propriety evolutions.
First, we observe a small world effect, which further confirmed the previous result of Xiaoguang Wang and
his colleagues.21 The average shortest
path length23 for all connected node
pairs in the network is 3.443, with a
diameter D of 8.23 Both numbers are
small compared to the total number of
nodes in the network (3,195). In addition, the average clustering coefficient
of the network is 0.89, indicating that
the keywords tend to form closed triplets. Almost every paper has more than
3 keywords, which could form one
cluster. These observations have shown
that the social computing co-words network possesses the small world property, even though the average shortest
path length increased little by little after
2007 from 3.27 to 3.44 (see Figure 4b).
Second, the turning point of the social computing field seems to have happened in 2008, which we can draw
from the diameter (see Figure 4a),
54
average shortest path length (see Figure 4b), and centralizations24 (see Figure 4d) of the co-word network.
Last, decentralization is another
trend of the social computing research,
even though the situation is on the opposite side between 2006 and 2008.
More topics appeared after 2008, and
the social computing field evolved more
and more subdomains, which could be
shown from the centralization changing trend in Figure 4d—ascended from
0.142 in 2006 to 0.179 in 2008 and
then descended to 0.134 in 2011—and
the number of multi-edge node pairs in
Figure 4c. The increasing rate of multiedge node pairs slowing down illustrates that the new keywords preferred
to connect to old keywords traditionally instead of connecting old keywords
together, especially after 2008. These
observations might indicate that the
topics of social computing have been
expanding after 2008, with the different topics providing different keywords
to form new research domains.
In addition to the topology from
a node perspective, link types could
reflect the changes as well. Two
link types could be identified in our
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co-word network. Some links connected the keywords with the same
time tags, which means that they first
appeared in the same year; while other
links connected the keywords with
different time tags, which indicates
that one of them appeared before and
they didn’t appear in the same paper
at an earlier time. We focused more on
the second type of links and treated
the co-word network as directed.
Figure 5 plots the network evolution
from a link perspective in different
time points. We treated keywords appearing before 2006 as a monolithic
group. While we treated each year after 2006 as one time point based on
the distribution of links. We measured the transmission by the links
amount normalized ratio between different time points, which we plotted
on the y-axis. The content at one time
point might share some keywords
that appeared previously (input links),
and be shared with the new keywords
appearing at a later time point (output
links). The red line in the figure shows
the approximate trend of the average
forward links transmission, while the
blue line represents the average afterward links transmission.
We can explain this from different
perspectives. One possible explanation is the memory effect of the papers, which is similar to a co-citation
network.22 The almost stable average forward links suggested that the
forward memory (the keywords remembered others keywords) is stable
in some sense, while the afterward
memory (the keywords remembered
by others keywords) were descending,
which was indicated by a trend of average afterward links. Another potential explanation could be made from
transmission insight. If the links from
one node to the other node in the
co-word network mean that the two
nodes transmitted something, then we
can treat this phenomenon as theme
IEEE INTELLIGENT SYSTEMS
THE AUTHORS
transmission. The descending trend
of average afterward transmission indicated that the earlier theme transmitted more than the later period,
especially for the themes appearing
before 2005. This means that most
themes at later periods left less value
than the content at earlier periods on
average, even though the number of
papers published increased. Most papers might just follow some popular
topics and share the keywords, while
the main themes don’t reflect the issue.
I
n this article, we analyzed the
static and dynamic aspects of social computing research. Overlapping
content indicated that subfields of social computing often share the same
topics, even though they have many
different terms. There are several aspects of this research future study
could enhance. First, as our bibliographical data is retrieved by terms,
we might have missed some records
that don’t share terms which we used.
Retrieving the publications with cited
and citing patterns might be a better
approach to get more precise records.
Second, we took the same weight of
all papers, while the importance of
each paper is not the same in some
senses. Constructing weighted networks would be a promising direction to improve our research.
Acknowledgments
We thank Fei-Yue Wang and Xiaolong
Zheng for their guidance and advice in this
research. This work is supported in part
by Hunan Provincial Innovation Foundation for Postgraduate, and by the National
Natural Science Foundation of China under
grants 70771109 and 61074903.
References
1. F.Y. Wang et al., “Social Computing:
From Social Informatics to Social Intelligence,” IEEE Intelligent Systems, vol.
22, no. 2, 2007, pp. 79–83.
SEPTEMBER/OCTOBER 2014
Tao Wang is a PhD candidate in the College of Information Systems and Management at
the National University of Defense Technology, Changsha, China. He’s a visiting student in
the State Key Laboratory of Management and Control for Complex Systems at the Chinese
Academy of Sciences. His research interests include social computing, social network analysis, and cyber-enabled social movement organization. Wang has an MS in engineering from
the National University of Defense Technology. Contact him at wangtao@nudt.edu.cn.
Zhong Liu is a professor at the National University of Defense Technology. His research
interests include information management and decision-making support technology. Liu
has a PhD in engineering from the National University of Defense Technology. Contact
him at liuzhong@nudt.edu.cn.
Baoxin Xiu is an associate professor in Information Systems Engineering Laboratory at
the National University of Defense Technology. His research interests include granular
computing, and computational and mathematical organization theory. Xiu has a PhD in
management science and engineering from the National University of Defense Technology. Contact him at baoxinxiu@163.com.
Hong Mo is an associate professor at the School of Electric and Information Engineering,
Changsha University of Science and Technology. Her research interests include linguistic
dynamic systems and intelligence computing. Mo has a PhD in engineering from the Graduate University of China Academy of Sciences. Contact her at mohong72@gmail.com.
Qingpeng Zhang is an assistant professor at the City University of Hong Kong. His research interests include social computing, complex networks, Semantic Web, and Web
science. Zhang has a PhD in systems and industrial engineering from The University of
Arizona. Contact him at qingpeng.zhang@cityu.edu.hk.
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Selected CS articles and columns
are also available for free at
http://ComputingNow.computer.org.
CALL FOR STANDARDS AWARD NOMINATIONS
IEEE COMPUTER SOCIETY HANS K ARLSSON
STANDARDS AWARD
A plaque and $2,000 honorarium is presented in recognition of
outstanding skills and dedication to diplomacy, team facilitation, and
joint achievement in the development or promotion of standards in the
computer industry where individual aspirations, corporate competition,
and organizational rivalry could otherwise be counter to the benefit
of society.
NOMINATE A COLLEAGUE FOR THIS AWARD!
DUE: 15 OCTOBER 2014
• Requires 3 endorsements.
• Self-nominations are not accepted.
• Do not need IEEE or IEEE Computer
Society membership to apply.
PAST RECIPIENT: ANNETTE D. REILLY
“For harmonization and development
of novel approaches to the system and
software engineering standards for
vocabulary, life-cycle information, and
user documentation.”
Submit your nomination electronically: awards.computer.org | Questions: awards@computer.org
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