Content uploaded by B. T. Sayed
Author content
All content in this area was uploaded by B. T. Sayed on Sep 21, 2018
Content may be subject to copyright.
Social Information System – a paradigm for characterizing social values
in an organization
S.M.F.D Syed Mustapha
Department of Computer Science
Dhofar University
Sulatanate of Oman
smfdsm@yahoo.com
B. T. Sayed
Department of Computer Science
Dhofar University
Sulatanate of Oman
btsm999@yahoo.com
Abstract
Formal structure in an organization describes the information about the organizational
hierarchies, responsibilities, functions and workflows which are stated explicit in printed
form such as reports and manuals. The information is important for formality purposes
and professional activities in getting the right job done by the right people. Informal
structure in an organization is rather implicit and untraceable. It is more dynamic than
the formal structure since its formation is determined by the dynamism of the people and
social factors. Social values such as politics, cronyisms, special interest group, human
behaviors, group communications and virtual organizations are informal information that
exists in mostly large organization. This information is important as an additional
limelight for the top management to understand better about the dynamisms in the
organization. Social information system in this paper describes the capturing and
characterizing of the social values in the organizations. It captures social dialogue as the
main data source to be processed into several types of extractions such as the politics in
the organization, the social network of the individuals and groups, the communication
style and the participatory profiles. We illustrate the life cycle of the social information
system from the beginning of the social dialogue to the final output of the extractions.
The benefits of the system are described as providing the extra source of information
about the informal structure of the organization in decision making.
Introduction
The classical understanding about an information system is about a system that
generates data summarization from a data crunching systems. The efforts that are
required to build such system are now made easier with the object-oriented technology
and the approach of building such systems are now well-documented and well-known
such that building them is just a matter of putting the pieces together. Accounting
information system, human resource information system, financial information system, to
name the few, are ready as packages that need few customizations before use.
Information system is the most preferable tool for the top management to view scattered
data in the visual format.
Social information system (thereafter is called SIS) is a one-step-ahead of its
kind that it processes stochastic data, unstructured text, unpredictable forms of inputs
that the technologies required to build such system are not available as ready-made. S IS
deals with social knowledge that is extracted mainly from the community dialogues and
actions [9]. Nevertheless, other learning artifacts such as text documents, videos,
pictures are also part of the media that has social knowledge embedded in them. This
makes SIS requires an outreach technologies to build it when compare to the
conventional information system.
Social knowledge has been defined in many ways in other literature [7]. In the
previous related papers social knowledge are defined by comparing its differences with
content knowledge [1]. In this paper, the idea of social knowledge is extended that it
characterizes the social values of the people in an organization. Social values contain
the political information, social structure, corporate memory and knowledge dynamics of
an organization. This information is not directly available from a single source as they
are ubiquitously resided. In other words, the social communication can take place in cars,
between the office corridors or through telephone conversation [10]. Besides, the social
communication can occur at any time and the contents may not always be important at
all times. The process of identifying important conversation is a delicate task.
Nevertheless, top management is interested to know the information related to such as
“who spearheads the idea of revising staff benefits”, “what are the main disgruntled
issues that are discussed by the employees” and “is there any special interest groups
within the formal organizational structure” [6]. Very commonly, this information is only
made available through informants who are the trusted parties appointed by the top
management.
In the subsequent sections we discuss the features of the SIS, the techniques in
implementing SIS in Intelligent Conversational Channel, the methodologies in extracting
the social values, the simulated environment of SIS in a real practice and conclude with
the limitations and future work of the system.
Features of Social Information System
Similarly to any other information system, SIS system captures data from various
input devices. The challenging part for the SIS is that the input data appears to be in a
form of verbal and written communication format. The verbal communication can be
captured through a simple device embedded in someone’s personal electronic gadgets
such as mobile phone, PDAs or portable electronic organizers that can record verbal
communication. The written text is less complex to be attained as it can be directly
imported from current communication channels such as e-mails, SMS to personal
computer, online chat room, electronic bulletin board and electronic documents.
There are three important features of the SIS that it requires mutuality in
participation, it is a basis of all communication that takes place in the organization and it
must have natural expression of the community.
Mutuality in participation requires an equivalence balance in involvement of the
individuals in the community of SIS. The balance is measured by examining the
response network of each individual to other individuals in the community as well as the
posted messages by the community. Community participates in posting messages of
their opinions, ideas, and arguments; and contributes in building community learning
artifacts. Learning artifacts substantiate the posted messages with more detail
description in the form of visual representation such as video clips or graphical pictures
and textual representation such as scientific documents.
Another important feature in SIS is that most communication that takes place in
the organization must be in the SIS platform. Meetings, discussion, briefings, seminars
and dialogues can be captured in a computable format and stored in the SIS’s repository.
Detail information about when and where it takes, who are the participants as well as the
active members in the events are part of the useful details in SIS. The quality in
characterizing the social values of an organization is highly dependable to the mass of
the community network and the rich content of community actions captured by the
system. Therefore, the system should be the basis for all communication that takes
place in the organization. The community in the organization is a composition of several
sets of people from different functional roles, designation categories and social interests.
Workers in the marketing department develop a higher mass of the community network
with the customers than the workers in the computer department. So, there are several
masses of community network that form a global mass of community network in the
entire organization. The mass of the community is measured by the ratio of the number
of participating community member against the number of community members. T he
community performs some actions in the SIS system such as uploading articles, web
pages, pictures, video clips and consequently discusses the contents of these
multimedia objects. These objects are analyzed and classified according to their
subjects and domains such that they reflect the interests and background of the
community member who uploads them. Community members that are active with these
activities enrich the content of the community actions. The richness of the activities is
measured by the average number of activities performed by the mass of the community
network.
The final feature of the SIS is the natural expression of the community. The
community members that participate in the social dialogue in SIS are aware of the fact
that their discourse and actions are captured and recorded for the analysis. Due to this
fact, their participation may be driven by the enthusiasm of popularizing their identity or
on the other hand, their interests may be suppressed when a feeling of inferiority
complex emerges. The community members can only express the true feeling and
sincere opinion when they are sure about their well-being and safe from any uncalled for
consequences. Natural expression can be obtained by hiding the true identity of the
community members such that flaming-talk based on someone’s personality can be
avoided. SIS preserves the dialogues purely based on the discussion of the subject
matter and avoids the attempts on personal attacks by not displaying members’ personal
identities.
Techniques in implementing SIS through Intelligent
Conversational Channel
The concept of SIS is built on Intelligent Conversational Channel (thereafter, ICC)
which is primarily a community channel for knowledge sharing. There are three main
components of ICC that supports the functionalities in SIS. There are three of them
which are the social network analyzer, learning object classifier and community memory
[2, 8].
Community channel which is the kernel to the entire ICC system interfaces the
community by providing facilities for discussion and building the community knowledge.
The community members posted messages in the form of short story that contains ideas,
arguments, supports, suggestions, general statements and disagreement in this channel.
In order to induce naturalness in expression (refer to the third feature of SIS), the true
identities are not identifiable by other members in the channel [3]. On the other hand,
without this information about the members the system can easily abused with obtrusive
messages. In order to prevent this, ICC requires earlier registration made by every
member in the community. Therefore, only the system administrator has the authority to
investigate the owner of each posted message. However, for the purpose of referencing
between participants in the online discussion, nicknames are used. The community
members share their knowledge not only through text conversation but also multimedia
objects, so-called learning artifacts, such as video clips, graphical pictures, audio files,
web pages and articles or electronic documents that can be uploaded to the community
memory. In the community channel, a story object is created when a member initiate an
issue by posting a message that contains a general idea or claim. Other members who
are interested to participate with that issue can respond using provided response buttons.
A series of responses form a coherent body of a story object. Similarly, other member
can also create another story object by initiating different issue in which the community
channel is made up a series of story objects [4].
Social network analyzer (SNA) determines statistically the respond network in the
community. This is important to ensure the mutuality in participation (refer to the first
feature of SIS) such that every member must not only participate in the process but also
there is a balance in the responses. For example, a member should respond in balance
to every other member so that there is evidence that each posted knowledge receives
equal attention by all members. Consequently, SNA is also able to trace the formation of
virtual subgroups within the larger group mass when there is inequality distribution of
respond network. This happens when a number of participants only communicate
restrictively within a smaller set of participants. SNA analyzes the type of response in
order to classify the type of social participation of a member. The actual identity of the
participants is used for this purpose since nicknames used by a member can vary every
time the person posted a message. The classification of the social participation
describes whether a person is an initiator to a new issue, an opposition to many ideas, a
follower to many ideas, a rich knowledge contributor or a neutral in discussion. A
frequent initiator creates issue in most of the story objects but less likely to appear in the
middle of the discussion. An opposition and follower can be determined when most
frequently the response button chosen are “Disagree” or “Support”, respectively. A rich
knowledge contributor posted a sizable amount of text in his/her response (ideally the
text is genuinely typed from his/her own thinking). A person is characterized as a neutral
when he/she chooses to respond using “General” button and most of the time avoids
disagreement or support. Another classification of social participation is the
measurement of participatory profile of the member. Participatory profile describes
whether a person is an active, passive or average. The benchmark used is by making
comparison with other members who are more active i.e. contributes the most in terms
of responses.
Generally, learning object classifier processes the contents of the learning
objects that are uploaded by the communities. In the current technology available, some
of the learning objects such as video clips and graphical picture are not possible to
determine the contents without having the knowledge domain about the objects and the
task for doing this is complex. ICC is a community collaborated system in which the
community plays important role in the process of learning object classification. For
example, a member after choosing and reading the article of interest uploads it into the
community channel and a general description about the article is posted as an
attachment. Other members read the same article and contribute their understanding by
responding to the general description posted earlier or to the responses made by other
members [2]. This process naturally produces summarization about the article by
broadening the scope and aspects about the topic originally described in the paper. This
is possible as the members may not only describe about the article but also associates
the article with their background knowledge. This process can be applied to other types
of learning objects such that the ICC uses these descriptions to determine the gist of the
learning objects. The learning objects are then classified according to the topics they
belong to.
Community memory in ICC is an association of relevant keywords that are
connected as a network [5]. The keywords are selection of important words based on
their relevancy to the domain subject. The keywords are extracted from the messages
posted by the members in a story object. These keywords are connected dynamically
and randomly as a network of keywords in a similar category. Similar network is built for
each story objects that contain different categorical discussion. These networks are
interconnected based on the keywords that found to be similar. An extensive description
about the community memory is discussed in the previous paper [ ].
The methodologies in extracting the social values
The ICC system extracts social information using the technologies discussed in the
previous section. ICC in practice can be used to discuss a formal and informal issue.
Formal issues are topics that are relevant to the organizational matters and workplace
while informal issues are those beyond the scope of the former. Sports, hobbies,
entertainment and latest home appliance technology are examples of informal issues.
Three examples of social information system that will be discussed in this section are the
identification of special interest group, identification of prevalent issues and identification
of political group.
Special interest group consists of community members who share a specific topic
of interest in their discussion and learning objects. There are three criteria that are used
in determining the existence of special interest group:
1. Over a period of time – the discussion that takes place must recur over a
given period of time. In the case of ICC, the discussions appear repeatedly in
several story objects for a given time. The time is relative to the number of
occurrences, for example, three story objects created every month for a
period of four months are more preferable than twenty story objects that are
created over a year.
2. Regular members – members participating in the discussion have to be
regular with some exceptional of inclusion or exclusion of very few members
at certain intervals of discussion sessions. Practically this has to be tolerated
as members may be away when the discussion took place.
3. Specific and similar topic – the topic is specific with varieties of sub-issues.
For example, a group may discuss mainly about golf with issues on the
places, clubs, tournament and coaching.
A virtual subgroup can also be determined using the similar criteria mentioned above
except for the third. This group consists of members who are bonded together for their
friendship rather than the topic of interest. Therefore, the topics may vary at every
session of the discussions.
Prevalent issues are formal matters related to the organization such as the newly
introduced policy, merging or restructuring of departments, reshuffling of organizational
structure and adoption of new workflow. The discussions contain the comments from the
employees in the form of constructive suggestion, disgruntlement, frustration or support.
Prevalent issues are most commonly discussed in most discussion groups as defined
below:
1. Majority of individuals – most individual in the organization participates and
expresses concern about the same issue. The majority is the number of
members that are acceptable to represent the entire organization.
2. Active discussion – the issues are widely discussed and received a chain of
responses from different groups of members.
Political group is formed naturally among members who have common line of
thinking about an ideology. The existence is detected by analyzing the response type in
the discussion network. Figure 1 shows Group A consisting of similar members
discussing on different topics (Topics A and B). KI is the knowledge initiator who triggers
certain issue by posting a message. R1 – R5 are respondents who contribute their
Agreement, Disagreement or Support. The response networks for both discussion topics
are different in that the individuals are corresponding to different persons in different
topics. Nevertheless, the pattern of the political inclination can be considered to be the
same. Table 1 determines the political inclination by analyzing the argument links.
Table 1: Argument link analysis
Topic A
Topic B
Response to
Type of response
Response to
Type of response
R1
KI
Support: Support
link from R1 KI
R1
KI
Support: Support
link from R1 to KI
R2
R1
Disagree: Disagree
link from R2 to R1
R2
R1
Disagree: Disagree
link with both R1
and KI.
R4
R2
Agree: Agree link
R4
R2
Agree: D isagree
Group A
KI
T op ic A
R1
R4
R2
R5
R3
A gr ee
Disagr e e
Support
Group A
KI
To pic B
R1
R4
R2
R5
R3
Figure 1.Formation of political group
Note: Agree and Support links have the same effect of mutuality in argument
Life cycle of Social Information System
from R4 to R2
link from R4 to K I
who has
disagreement with
R2.
R5
R4
Disagree: Disagree
link from R5 to R4
R5
R4
Disagree: Disagree
link from R5 to R2
who agrees with R4.
Thus, R5 disagrees
with R4.
Produce meeting
minutes
Meeting minute is
uploaded to ICC
and communities
discuss the matters
infor mally
Formal physical
meetings
Organizational
Events
Technical
committee meeting
Management
m e et i n g
Sports day
Ann ual
Dinner
VI P v i s it
EXTRACTING SOCIAL VALUES FROM THE ICC
Communities discuss
random topics on
recent events
Top management
views the social
information
through ICC
Figure 2. Life cycle of Social Information System
The idea of social information system is novel and therefore it is imperative to
demonstrate a simulated life cycle of the system as an illustration. The current
implementation of SIS using ICC acquires social information from the community
channel as a single source of input. Figure 2 illustrates the life cycle of the SIS that
involves the community members, their social interactions and the learning artifacts used
as the communication media. The community in this example is represented by the
workers in the organization and the top management who is extracting and monitoring
the social values. The lifecycle of SIS begins with any point of organization’s community
gathering which could be the formal meetings (e.g. technical committee meeting or
management meeting) or organizational events (e.g. sports day, annual dinner or VIP
visits) that lead to further gossips or discussions within the committee. The results from
such c ommunity gatherings are the topics entail to several social talks among the
communities. The social talk in ICC allows other members who were not originally
present in that particular event to participate and get acquainted with the issues. The
social discussion in ICC can be supported by learning artifacts that mediates the
discussions; examples in Figure 2 are such as the meeting minutes, annual dinner
photos, sports day pictures and videos taken from VIP visit. The social talks evolve into
organizational massive social network and social knowledge which can be processed,
analyzed and transform into useful social information to the top management.
Conclusion and future work
This paper highlights the importance of new dimensional information about the
organization that top management frequently sniffs out from trustworthy party. Social
information helps the top management to identify the ball game appears at the
background of the actual organizational set up. Despite the usefulness of the information,
there are basic features related to the community involvement and working environment
structure that SIS must fulfill. The community has to place the social information system
as part of their social media for interactions in which the current technology such as e-
mails and electronic discussion board have successfully benefited. The community’s
intellectual participation will increase the momentum of social knowledge in the
organization at the same time builds social capital to the organization. The structure of
the working environment is dynamic that there are lots of interactions, sharing and
exchanging information and communication media and the job functions are not rigidly
specified. In brief, social information system works better in service-oriented organization
such as insurance company rather than production-oriented organization that majority of
the workers are at the assembly line.
Social information system is new of its kind and therefore there is wide room for
research opportunity and advancement. One the immediate areas to be looked into is
the automated discourse classifier which determines the type of arguments. Currently,
ICC requires members to tag the type of respond in the system. Another area is
visualization of the social network analysis. Visual representation is the common feature
in any information system that ICC should be able to show the dynamism of the social
interaction, special interest group and virtual political formation visually. The challenging
issue in this research work is finding the right transformation formula for converting
social data to visual information.
References
1. S.M.F.D S yed Mustapha. Intelligent Conversational Channel for
Learning Social Knowledge among Communities. 8th International
Conference on Knowledge-based Intelligent Information & Engineering
System, KES 04, New Zealand, Vol I, pp343-349, 2004.
2. S.M.F.D S yed Mustapha. Agent-mediated Community Channel for
Social Knowledge Building in Educational Environment. 5th Int. Conf.
on Information Technology Based Higher Education and Training:
ITHET ’04, 31st CD proceeding, May – 2 June, 2004.
3. S.M.F.D Syed Mustapha. Theoretical optic on enabling PBL activities
for large group through intelligent conversational channel. 5th Asia-
Pacific Conference on Problem-based Learning, CD publication, 16 & 17
March, 2004.
4. S.M.F.D S yed Mustapha. An Algorithm for Avoiding Paradoxical
Argument among the Multi-Agent in the Discourse Communicator.
8th International Conference on Knowledge-based Intelligent Information
& Engineering System, KES 04, New Zealand, Vol I, pp350 – 356, 2004.
5. S.M.F.D Syed Mustapha. Towards Building Socially Intelligent
Knowledge-Building System – in probe to missing component. A
report submitted to Department of Information and Communication
Engineering, School of Information Science and Technology, The
University of Tokyo, EMEDIA 2004, pp 169-179, 2004.
6. S.M.F.D Syed Mustapha, T. Nishida Communicative Social Intelligence:
An analysis in the Community of Practice, Knowledge Management
International Conference and Exhibition, 14 - 15th February, Pe nang, pp
42-52, 2004
7. Cooles, C. H. The roots of social knowledge. American Journal of
Sociology, 32 (1926- 27): 59-79.
8. S.M.F.D Syed Mustapha. Knowledge construction technology through
hyper-based media community channel. 2nd International Conference
of Artificial Intelligence in Engineering and Technology, 3 – 5 August, 2004.
9. S.M.F.D Syed Mustapha Extracting Social Knowledge in the Intelligent
Conversational Channel for Agent Communication, 2005 International
Workshop on Web and Mobile Information Systems (WAMIS’05), Taiwan,
Vol 1,pp 273-279, 2005.
10. S.M.F.D Syed Mustapha a nd T. Nis hida. Communicative Social
Intelligence: Technological Concepts for Breaking Communication
Impediments. International Journal of Web-based Communities,
Inderscience Publishers, UK, Vol 1 No4, 2005.
