The document summarizes a proposed online deliberation system that uses mutual evaluation and collaborative rewriting of proposals. Key points:
1. The system invites participants to rewrite proposals that are supported by many but not a given person, to make the proposal more acceptable.
2. It also identifies poorly presented but potentially interesting proposals that may benefit from rewriting for clarity.
3. The first use case of inviting rewrites from those who disagree has been implemented in an online tool called "Vilfredo goes to Athens".
In this paper, a conceptual framework is proposed, supported in
the literature review, derived by identifying the main concepts
related to crowdsourcing, as well as ways of improving group
participation. We also propose a software solution that may be
used to support the crowdsourcing process. This software solution is inspired by the conceptual framework.
Presented by Sachin sharma.
Crowdsourcing systems enlist a multitude of humans to help solve a wide variety of problems. Over the past decade, numerous such systems have appeared on the World-Wide Web. Prime examples include Wikipedia, Linux, Yahoo! Answers, Mechanical Turk-based systems, and much effort is being directed toward developing many more.
H. Purohit, Y. Ruan, A. Joshi, S. Parthasarathy, A. Sheth. Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter. in SoME 2011 (Workshop on Social Media Engagement, in conjunction with WWW 2011), March 29, 2011.
Paper: http://knoesis.org/library/resource.php?id=1095
More on Social Media @ Kno.e.sis at http://knoesis.org/research/semweb/projects/socialmedia/
An Sna-Bi Based System for Evaluating Virtual Teams: A Software Development P...ijcsit
The dependence of today's collaborative projects on knowledge acquisition and information dissemination
emphasizes the importance of minimizing communication breakdowns. However, as organizations are
increasingly relying on virtual teams to deliver better and faster results, communication issues come to the
forefront of project managers' concerns. This is particularly palpable in software development projects
which are increasingly virtual and knowledge-consuming as they require continuous generation and
upgrade of shared information and knowledge. In a previous work, we proposed an SNA-BI based system
(Covirtsys) that supplements the Analytics modules of the collaborative platform in order to offer a
complementary analysis of communication flows through a network perspective. This paper concerns the
application of this system on a software development project virtual team and shows how it can bring new
insights that could help overcome communication issues among team members.
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Social network analysis for modeling & tuning social media websiteEdward B. Rockower
Social Network Analysis of a Professional Online Social Media Collaboration Community. Tuning and optimizing based on observed social network dynamics and user behavior.
In this paper, a conceptual framework is proposed, supported in
the literature review, derived by identifying the main concepts
related to crowdsourcing, as well as ways of improving group
participation. We also propose a software solution that may be
used to support the crowdsourcing process. This software solution is inspired by the conceptual framework.
Presented by Sachin sharma.
Crowdsourcing systems enlist a multitude of humans to help solve a wide variety of problems. Over the past decade, numerous such systems have appeared on the World-Wide Web. Prime examples include Wikipedia, Linux, Yahoo! Answers, Mechanical Turk-based systems, and much effort is being directed toward developing many more.
H. Purohit, Y. Ruan, A. Joshi, S. Parthasarathy, A. Sheth. Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter. in SoME 2011 (Workshop on Social Media Engagement, in conjunction with WWW 2011), March 29, 2011.
Paper: http://knoesis.org/library/resource.php?id=1095
More on Social Media @ Kno.e.sis at http://knoesis.org/research/semweb/projects/socialmedia/
An Sna-Bi Based System for Evaluating Virtual Teams: A Software Development P...ijcsit
The dependence of today's collaborative projects on knowledge acquisition and information dissemination
emphasizes the importance of minimizing communication breakdowns. However, as organizations are
increasingly relying on virtual teams to deliver better and faster results, communication issues come to the
forefront of project managers' concerns. This is particularly palpable in software development projects
which are increasingly virtual and knowledge-consuming as they require continuous generation and
upgrade of shared information and knowledge. In a previous work, we proposed an SNA-BI based system
(Covirtsys) that supplements the Analytics modules of the collaborative platform in order to offer a
complementary analysis of communication flows through a network perspective. This paper concerns the
application of this system on a software development project virtual team and shows how it can bring new
insights that could help overcome communication issues among team members.
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Social network analysis for modeling & tuning social media websiteEdward B. Rockower
Social Network Analysis of a Professional Online Social Media Collaboration Community. Tuning and optimizing based on observed social network dynamics and user behavior.
Power no longer resides exclusively (if at all) in states, institutions, or large corporations. It is located in the networks that structure society. Social network analysis seeks to understand networks and their participants and has two main focuses: the actors and the relationships between them in a specific social context.
A Novel Frame Work System Used In Mobile with Cloud Based Environmentpaperpublications3
Abstract: Recent era efforts have been taken in the field of social based Question and Answer (Q&A) which is used to search the answers for the non – factorial questions. But traditional search engines like Google, Bing is used to answer only for the factorial questions where we can get direct answer from the data base servers. The web search engine for the (Q&A) system does not dependent on the broadcasting methods and centralized server for identifying friends on the social network. The problem is recovered by using mobile Q&A system in that mobile nodes are help full for accessing internet because these techniques are used to generate low node overload, higher server bandwidth cost and highest cost of mobile internet access. Lately technical experts proposed a new method called Distributed Social – Based Mobile Q&A system (SOS) which makes very faster and quicker responses to the asker. SOS enables the mobile user’s to forward the question in the decentralized manner in order get effective, capable, and potential answers from the users. SOS is the light weighted knowledge engineering technique which is used find correct person who ready and willing to answer questions hence this type of search are used reduce searching time and computational cost of the mobile nodes. In this paper we proposed a new method called mobile Q&A system in the cloud based environment through which the data has been as been transmitted form cloud server to the centralized server at any time.
This short set of slides summarizes the characteristics of people who play specific roles in networks. In a social network analysis, people in these roles can be discovered by running mathematical algorithms through the social graphs. But you don't need to be an algorithm to spot some of these people in your networks!
How to conduct a social network analysis: A tool for empowering teams and wor...Jeromy Anglim
Slides and details available at: http://jeromyanglim.blogspot.com/2009/10/how-to-conduct-social-network-analysis.html
A talk on using social network analysis as a team development tool.
Power no longer resides exclusively (if at all) in states, institutions, or large corporations. It is located in the networks that structure society. Social network analysis seeks to understand networks and their participants and has two main focuses: the actors and the relationships between them in a specific social context.
A Novel Frame Work System Used In Mobile with Cloud Based Environmentpaperpublications3
Abstract: Recent era efforts have been taken in the field of social based Question and Answer (Q&A) which is used to search the answers for the non – factorial questions. But traditional search engines like Google, Bing is used to answer only for the factorial questions where we can get direct answer from the data base servers. The web search engine for the (Q&A) system does not dependent on the broadcasting methods and centralized server for identifying friends on the social network. The problem is recovered by using mobile Q&A system in that mobile nodes are help full for accessing internet because these techniques are used to generate low node overload, higher server bandwidth cost and highest cost of mobile internet access. Lately technical experts proposed a new method called Distributed Social – Based Mobile Q&A system (SOS) which makes very faster and quicker responses to the asker. SOS enables the mobile user’s to forward the question in the decentralized manner in order get effective, capable, and potential answers from the users. SOS is the light weighted knowledge engineering technique which is used find correct person who ready and willing to answer questions hence this type of search are used reduce searching time and computational cost of the mobile nodes. In this paper we proposed a new method called mobile Q&A system in the cloud based environment through which the data has been as been transmitted form cloud server to the centralized server at any time.
This short set of slides summarizes the characteristics of people who play specific roles in networks. In a social network analysis, people in these roles can be discovered by running mathematical algorithms through the social graphs. But you don't need to be an algorithm to spot some of these people in your networks!
How to conduct a social network analysis: A tool for empowering teams and wor...Jeromy Anglim
Slides and details available at: http://jeromyanglim.blogspot.com/2009/10/how-to-conduct-social-network-analysis.html
A talk on using social network analysis as a team development tool.
This is a talk I gave at the ePart conference 2010.
The talk is actually available on Youtube.
Abstract: We present an alternative form of decision making designed using a Human Based Genetic Algorithms. The algorithm permits the participants to tackle open questions, by letting all of them propose answers and evaluate each others answers. A successful example is described and some theoretical results are presented showing how the system scales up.
The system I am presenting is available at: http://vilfredo.org
My homepage is at: http://pietrosperoni.it
Expectations for Electronic Debate Platforms as a Function of Application DomainIJERA Editor
Electronic debate (or commenting) platforms are used with many types of online applications, as a way to
engage the users or to provide enhancements, e.g., based on some type of collaborative filtering [1], [2]. The
applications enhanced with such debate platforms range widely : news, products, sport, religion, politics, etc.
Therefore, the emerging question is whether it is possible to make one electronic debate mechanism good for all
applications, and whether the studies on the success of a debate mechanism in one domain do automatically
apply to other application domains. Here we compare two traditional application domains of electronic debate
platforms: product evaluation and commented news. We exploit the fact that most users are very familiar with
both types of such applications, and therefore surveys can be designed to gauge reliably subtle differences
between expectations and properties of these domains. Based on over 1000 responses to surveys described here,
we are able to report statistically significant differences between the user behavior and expectations in the
studied domains.
Expectations for Electronic Debate Platforms as a Function of Application DomainIJERA Editor
Electronic debate (or commenting) platforms are used with many types of online applications, as a way to engage the users or to provide enhancements, e.g., based on some type of collaborative filtering [1], [2]. The applications enhanced with such debate platforms range widely : news, products, sport, religion, politics, etc. Therefore, the emerging question is whether it is possible to make one electronic debate mechanism good for all applications, and whether the studies on the success of a debate mechanism in one domain do automatically apply to other application domains. Here we compare two traditional application domains of electronic debate platforms: product evaluation and commented news. We exploit the fact that most users are very familiar with both types of such applications, and therefore surveys can be designed to gauge reliably subtle differences between expectations and properties of these domains. Based on over 1000 responses to surveys described here, we are able to report statistically significant differences between the user behavior and expectations in the studied domains.
Evaluation the Impact of Human Interaction/Debate on Online News to Improve U...IJERA Editor
The average of many people trust online comments for any news as much as personal recommendations [1], [2].
In this paper, we analyzed the impact of the online news’s comments to evaluating the threading models of
electronic debates by using online surveys. In this paper, based on the results of our online survey of 500
participants, we evaluated whether forums with comments concerning online news are appropriate for the study
of debates. In particular, we have to verify whether the nature of discussions around news is argumentative and
whether the participating people expect to engage in multiple rounds of arguments. We presented
DirectDemocracyP2P application as a user interface for decentralized debates. In this paper, we evaluated and
analyzed the comments that were collected from online surveys to improve the DirectDemocracyP2P
applications. Also we have to verify whether the actual comments commonly submitted around news do go
beyond the simple advertisement of one own’s merchandise and attacks of competitors, into fair reviews of
news features and quality.
Evaluation the Impact of Human Interaction/Debate on Online News to Improve U...IJERA Editor
The average of many people trust online comments for any news as much as personal recommendations [1], [2]. In this paper, we analyzed the impact of the online news’s comments to evaluating the threading models of electronic debates by using online surveys. In this paper, based on the results of our online survey of 500 participants, we evaluated whether forums with comments concerning online news are appropriate for the study of debates. In particular, we have to verify whether the nature of discussions around news is argumentative and whether the participating people expect to engage in multiple rounds of arguments. We presented DirectDemocracyP2P application as a user interface for decentralized debates. In this paper, we evaluated and analyzed the comments that were collected from online surveys to improve the DirectDemocracyP2P applications. Also we have to verify whether the actual comments commonly submitted around news do go beyond the simple advertisement of one own’s merchandise and attacks of competitors, into fair reviews of news features and quality.
AN EXTENDED HYBRID RECOMMENDER SYSTEM BASED ON ASSOCIATION RULES MINING IN DI...csandit
Social groups in the form of different discussion forums are proliferating rapidly. Most of these
forums have been created to exchange and share members’ knowledge in various domains.
Members in these groups may need to use and retrieve other members’ knowledge. Therefore,
recommender systems are one of the techniques which can be employed in order to extract
knowledge based on the members’ needs and favorites. It is noteworthy that not only the users’
comments and posts can have valuable information, but also there are some other valuable
information which can be obtained from social data; moreover, it could be extracted from
relations and interactions among users. Hence, association rules mining techniques are one of
the techniques which can be applied in order to extract more implicit data as input to the
recommender system. Our objective in this study is to improve the performance of a hybrid
recommender system by defining new hybrid rules. In this regard, for the first time, we have
defined new hybrid rules by considering both users and posts’ content data. Each of the defined
rules has been examined on an asynchronous discussion group in this study. In addition, the
impact of the defined rules on the precision and recall values of the recommender system has
been examined. We found that according to this impact, a classification of the defined rules can
be considered and a number of weights can be assigned to each rule based on their impact and
usability in the specific domain or application. It is noteworthy that the results of the
experiments have been promising.
AN EXTENDED HYBRID RECOMMENDER SYSTEM BASED ON ASSOCIATION RULES MINING IN DI...cscpconf
Social groups in the form of different discussion forums are proliferating rapidly. Most of these forums have been created to exchange and share members’ knowledge in various domains.
Members in these groups may need to use and retrieve other members’ knowledge. Therefore, recommender systems are one of the techniques which can be employed in order to extract
knowledge based on the members needs and favorites. It is noteworthy that not only the users comments and posts can have valuable information, but also there are some other valuable information which can be obtained from social data; moreover, it could be extracted from relations and interactions among users. Hence, association rules mining techniques are one of the techniques which can be applied in order to extract more implicit data as input to the recommender system. Our objective in this study is to improve the performance of a hybrid
recommender system by defining new hybrid rules. In this regard, for the first time, we have defined new hybrid rules by considering both users and posts’ content data. Each of the defined rules has been examined on an asynchronous discussion group in this study. In addition, the impact of the defined rules on the precision and recall values of the recommender system has been examined. We found that according to this impact, a classification of the defined rules can
be considered and a number of weights can be assigned to each rule based on their impact and usability in the specific domain or application. It is noteworthy that the results of the
experiments have been promising
Finding The Voice of A Virtual Community of PracticeConnie White
Critical components for a successful Community of Practice (CoP) are that: 1) the community members have a space where their voice can be heard and that, (2) the proper technology is given to them to aid in this effort. We describe a Dynamic Delphi system under development which interprets the group’s voice in the creation of information during the initial start up phases when cultivating a CoP. Community members’ alternatives are explored, justified and debated over periods of time, and best reflect the group’s opinion at any moment in time where collective intelligence will be created from the interactions amongst group members. The system could handle a wide variety of types of decisions reflecting the diversity of goals given a CoP including emergency response actions, prediction markets, lobbying efforts, any sort of problem solving, making investment suggestions, etc. Pilot studies indicate that the group creates a greater number of better ideas. Ongoing studies are described, including applications to emergency management planning and response. They demonstrate that implementing a Dynamic Delphi system will prove conducive for building the initial repertoire of ideas, rules, policies or any other aspect of the community’s ‘voice’ that should be heard, in such a way that the individual voices are juxtaposed in harmony to create a single song.
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Activating Research Collaboratories with Collaboration PatternsCommunitySense
This presentation explains how collaborative communities require evolving socio-technical systems. Collaboration patterns are important to design these systems and capture lessons learnt. The role of librarians as collaboration pattern stewards and collaborative working system architects is outlined.
Global Redirective Practices: an online workshop for a clientSean Connolly
This slidedeck is an exhaustive report consisting of research in sociological literature, user research in focus groups, competitive analysis of similar tools, and, designing for a client with no money and no technical ability.
[Because this was a presentation, much of the information is supplied by the presenter. Critical information of the presentation has been added to the slide deck as 'Notes:']
Similar to Paper at ePart 2011: System Generated Requests for Rewriting Proposals (20)
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At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
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Sectoral targets and attacks as well as the cost of ransom
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Why are attacks on smart factories rising?
Cyber risk predictions
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Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
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Paper at ePart 2011: System Generated Requests for Rewriting Proposals
1. Page 1
SYSTEM-GENERATED REQUESTS FOR REWRITING PROPOSALS
Pietro Speroni di Fenizio1, Cyril Velikanov2
We present an online deliberation system using mutual evaluation in order to col-
laboratively develop solutions. Participants submit their proposals and evaluate
each other’s proposals; some of them may then be invited by the system to rewrite
“problematic” proposals. Two cases are discussed: a proposal supported by
many, but not by a given person, who is then invited to rewrite it for making yet
more acceptable; and a poorly presented but presumably interesting proposal.
The first of these cases has been successfully implemented. Proposals are evalu-
ated along two axes—understandability (or clarity, or, more generally, quality),
and agreement. The latter is used by the system to cluster proposals according to
their ideas, while the former is used both to present the best proposals on top of
their clusters, and to find poorly written proposals candidates for rewriting. These
functionalities may be considered as important components of a large scale online
deliberation system.
1. Introduction
Our study presented in this paper relates to open online deliberation and open online collabo-
ration. Within the context of eParticipation, online deliberation is indeed an essential aspect;
while online collaboration should also be considered as essential if we want eParticipation to
be purposeful, that is, looking for a solution to a problem stated, and productive, that is, yield-
ing result(s) commonly agreed upon.
As eParticipation still remains a rather vague concept, we start with better delimiting the kind
of activities we are considering in this paper. Namely, we will consider that a given ePartici-
pation activity (project, campaign) involves a number of active participants, i.e. participants
who may write their own contributions, but who at least are actively reading and appraising
(aka evaluating) each other’s contributions. A contribution basically is a proposal (of how to
solve the problem being discussed) or a comment on one or more other contributions (propos-
als or comments). Other types of contributions may indeed be considered as well, but we are
mostly interested in proposals, and then, to a lesser extent, in comments.
Hereafter active participants will be simply called participants; while those other people who,
even after registering, do not manifested their presence, simply won’t be considered at all.
1.1 Clustering of Contributions
One of our basic assumptions is that an open online deliberation on an important societal is-
sue, when it is expected to be purposeful and productive, may attract a really large number of
1Pietro Speroni di Fenizio (speroni@dei.uc.pt ), CISUC, Department of Informatics Engineering, University of
Coimbra, Pólo II, 3030-290 Coimbra, Portugal
2 Cyril Velikanov (cvelikanov@gmail.com), “Memorial”, Malyi Karetnyi per. 12, Moscow 127051, Russia, and
PoliTech Institute, 67 Saint Bernard St., Brussels 1060, Belgium
2. Page 2 Procedures and Methods for Cross-community Online Deliberation
active participants (in the order of thousands, or tens of thousands or more). In this case, it
will be termed a mass online deliberation (MOD). Within a MOD, a large number of contri-
butions (proposals and comments), all related directly or indirectly to the same problem stated
in advance, will be “put on the table” by participants, requiring their reading, appraisal and
further commenting. Indeed, an average participant, who devotes for this activity just some
part of his/her leisure time, wouldn’t be able to read, understand and meaningfully classify
such a mass of competing proposals and of various comments on them. Hence the need of a
programmed MOD support system to facilitate this task for participants.
However, while considering the task of semantic sorting (clustering) of a large number of
proposals/comments on a given issue, we should state that this task cannot be performed
automatically by any existing algorithm of automated text analysis—simply because it must
analyse not only the content of every proposal (what it is about), but also its intent (what the
author suggests, prefers, wants, or rejects...); for the research into algorithms of the intent
analysis of texts is still at its very beginning.
On the other hand, in an open mass deliberation, semantic clustering of proposals cannot be
commissioned to a staff of “facilitators” external to the deliberating community—because this
would often bias the course of deliberation; and yet more often, if not always, it would arouse
suspicion that such a bias is indeed present. We refer to [1] for a more detailed rationale.
Hence, the task of clustering proposals in a deliberation can only be performed collectively by
the whole deliberating community, and the total number of items to be considered implies that
performance of this task should be distributed among a great number of participants, if not on
all of them, acting in a concerted manner. Such a distributed collective action can only be per-
formed under the guidance of a MOD support system with a suitable clustering algorithm.
1.2 Ranking of Contributions Within a Cluster
The task of clustering proposals should result in grouping together proposals that are similar
or compatible in the view of those participants who have read (and appraised) them. Some of
those clusters may become rather large, thus necessitating a finer analysis. On the other hand,
a given proposal containing some idea may present the idea in a more or less clear, concise
and argumented way. On the opposite end of this “quality scale” we may have many poorly
formulated proposals, down to barely understandable or completely obscure texts. The latter
case taken apart, such a quality ranking, if performed by participants and aggregated by the
support system, can further help participants to navigate across a large number of proposals.
As for texts that have appeared obscure to most of their readers, such a text nevertheless may
have been sufficiently well understood by some of them, who therefore may have been able to
emit a further judgement on the ideas it contains—in a form of a simple (dis)agreement or of
a comment. Indeed, it would be very desirable to spread this understanding onto the rest of
the deliberating community, for a badly formulated idea may nevertheless be valuable and
original. This may need partial or full rewriting of a text that appears obscure or is misunder-
stood.
3. Procedures and Methods for Cross-community Online Deliberation Page 3
1.3 Collaboration by Rewriting Proposals
Now, let us turn to online collaboration—which may be considered either as an integral part
of a purposeful online deliberation, or as a separate task. In the former case, it is however
hardly imaginable that a very large number of participants may be able to productively col-
laborate, e.g. in preparing a common “final proposal” from a set of “initial” ones.
A more practical solution would consist in creating within a large deliberating community one
or more online workgroups of a limited size (say, typically up to 30 people) charged with edit-
ing a common proposal. In such a case, as well as in that of a standalone collaborative work-
group, there should be an established mechanism that supports the workgroup’s efforts to
reach an agreement on a commonly accepted final text, by rewriting the input texts, maybe
even several times.
Rewriting may be done collectively, or by individual workgroup members; in the latter case,
the support system may be helpful in finding out the best candidates for rewriting.
1.4 System-Requested User Actions
In each of those above-presented cases, there appears a need for the support system to assist
the deliberation and/or collaboration process by requesting specific actions to be performed at
specific times by designated participants. These actions may include, in particular: (1) addi-
tional appraisal (evaluation) of a given contribution (proposal or comment) that hasn’t been
sufficiently seen yet, to reliably assign it to some cluster and to rank it within that cluster; (2)
rewriting a poorly written proposal that may nevertheless present some interest for the com-
munity; (3) rewriting a controversial proposal in order to make it more acceptable to a larger
part of the community or of a workgroup.
The idea of system-requested user actions has several aspects, that will be discussed in the
following sections. First, we need an algorithm of selecting the most necessary action(s)
whose performance is to be system-requested at a given time. Then, we need a principle for
selecting the participant(s) the best positioned for successfully performing the requested ac-
tion(s).
Then, we should consider the motives that would prompt participants to perform the re-
quested action rather than to decline, and whether any incentives should be established to cre-
ate such motives.
We will discuss these aspects for each of the above contexts (1) – (3), starting from the third.
2. Rewriting Proposals by Those Who Disagree With Them
The idea of charging the system with the task of requesting from specific participants to re-
write specific proposals with which they disagree has been implemented by one of the authors
in a Web-based collaboration tool “Vilfredo goes to Athens” (http://vilfredo.org). The website
is actually open for participation; its description can be found in [2]. In this system, partici-
pants can state a problem, and then move their own proposals (on how to solve the problem
stated) and evaluate each other’s proposal. The system uses some human based genetic algo-
rithm (HBGA) [6], where the whole process may have several cycles each consisting of a
4. Page 4 Procedures and Methods for Cross-community Online Deliberation
proposal phase and evaluation phase. In every consecutive cycle, a new generation of pro-
posals is written, based on, or inspired by, a filtered selection of the proposals of the previous
generation. The selection is done by extracting a Pareto front [7] of the proposals.
The concept of Pareto front can be explained as follows. Let us say that a proposal A domi-
nates a proposal B if everybody who votes for B also votes for A, though there exist some
participants (at least one) who vote for A but not for B. So we can exclude the proposal B
from the list of proposals without making anyone really unhappy because they are still satis-
fied with one of the available proposals, namely A. The Pareto front then will be the list of
proposals that are not dominated and hence pass the filter.
This particular way in which proposals reach the next generation may lead to some singular
effects. One of such effects is, that sometimes a certain person has a stronger than average
effect on the Pareto front. For example, let us suppose that there are two proposals A and B,
and everybody (except John) who voted for B also voted for A, while at the same time there
are other people who voted for A but not for B. So A “nearly dominates” B. Everybody except
John either prefers A to B, or finds them equally acceptable.
In such a case, easily discoverable by the support system, John can be invited to rewrite A, in
a way he thinks would be acceptable for him while (presumably) still acceptable for those
who currently accept the original proposal A. If John’s version of A, say A’, appears to be
really acceptable for everybody who voted for A, and if John also accepts it (something we
can assume since he wrote it), then A’ will dominate both A and B, and we have shrunk the
Pareto front at least from this one proposal. In a typical generation we may have multiple
people who are invited to rewrite some proposals; moreover, the same proposal can be rewrit-
ten by multiple people (each one trying to make the proposal dominate a different one). Also,
the same person can also be invited to rewrite multiple proposals. In general, the more a per-
son will vote in a different way from the other people (and thus will represent a unique point
of view) the more the system will ask him or her to rewrite other’s proposals.
Field experience has shown that participants tend to welcome the invitation to work on a par-
ticular proposal. In http://vilfredo.org, at every generation each user has many possibilities.
He/she can rewrite another person’s proposal, or try to work out a compromise between two
different positions, or recover a proposal that was lost, trying to reframe it in a different way.
Among all those possibilities it is common for users to feel lost. As time is always limited, a
participant will want to make the most effective action. An invitation presented by the support
system will represent in this sense one of the most cost-effective possible actions, and is thus
generally well received.
3. Clustering, Ranking, and System-Requested Appraisal Actions
Our next step concerns a support system for mass online deliberation (MOD), the need for
which has been discussed above in the introductory chapter of this paper. This system will
include an algorithm of clustering and ranking participants’ contributions—proposals, com-
ments, and more—based on individual appraisals (evaluations) of every contribution by a lim-
ited number of other participants.
5. Procedures and Methods for Cross-community Online Deliberation Page 5
To assure a more objective appraisal of every contribution, esp. immediately after it is written
and uploaded into the system, participants who come online will be invited to evaluate pro-
posals that the system presents to them, at random. This should be done in such a way that
each proposal is evaluated on average the same number of times, and all are evaluated at least
a predefined minimum number of times. Also, at this stage it should not be possible for any
participant to see the evaluation results of other ones, to avoid any unconscious bias. If at the
end of this “blind evaluation” period some proposals have not yet been evaluated enough,
they can be sent for additional evaluation to randomly selected participants.
This may be seen as a simplified blind peer review of contributions that makes use of volun-
tary participation as much as possible. Though, random selection of proposals for evaluation
by a given person is functionally equivalent to random selection of participants (among cur-
rently active ones) for evaluation of a given proposal. Each reviewer thus may be considered
as performing a system-requested action, the one of appraising a new contribution that other-
wise he/she would probably never have any impulse to read.
3.1 Managing Public Responsiveness to System Requests
Experience from existing projects that include users’ actions of appraisal (evaluation) of texts
and other items and/or of their tagging shows that people are typically active enough in per-
forming such actions on a discretionary (at will) basis. It is yet to be discovered how much
the people involved in future large-scale eParticipation activities would be responsive to the
same type of actions when they are not voluntary but system-requested. We can expect suffi-
cient level of responsiveness, considering that possibility to participate and collaborate is the
prise for itself.
If however the public will not be responsive enough, specific incentives could optionally be
installed and managed by the support system. For example, the system could maintain for
every participant two activity counters, one for his/her voluntary actions, esp. for writing his/
her own contributions, another for system-requested actions performed. If the second counter
is too low, new contributions from that participant will be temporarily blocked, pending a suf-
ficient number of system-requested actions performed.
A system that randomly requires participants to do this or that in a moment that may be inop-
portune for a given person, may be considered by participants as too “oppressive”. To avoid
such an undesirable effect, the system may send its requests at any given moment to those
participants only, who manifest by themselves their readiness at this moment to perform
system-requested actions. In this way, the system randomly selects a contribution to be sent to
a given participant, rather than a random participant to read this contribution. The result glob-
ally will be the same.
3.2 Two-Parameter Appraisal: How Well Presented, How Much Do I Agree
Our algorithm of clustering and ranking a large set of participants’ contributions is based on
their double appraisal, first by a few randomly selected participants (peer reviewers), then by
whoever wishes to read and appraise any given contribution. Presumably, the latter ones (dis-
cretionary appraisers) would be helped by the support system when they are looking for con-
tributions that are for them potentially the most interesting ones, and this system-made selec-
6. Page 6 Procedures and Methods for Cross-community Online Deliberation
tion will be made by using said algorithm, as briefly explained below; see [3] for more de-
tails.
So, every participant is expected, when reading a contribution, to appraise it on two different
scales, one for the contribution’s quality, another for the reader’s agreement with the ideas it
contains. The quality of a given text is considered to be its intrinsic characteristic, thus ap-
pealing for an objective evaluation by the reader; while the agreement is indeed a fully sub-
jective one, as it depends on the reader’s convictions, beliefs etc. Hence the two measures can
be theoretically seen as independent (though, as we will see in the next section, they indeed
depend on each other).
The two parameters are then exploited by the system in quite different ways. The quality
grades assigned by individual appraisers are “aggregated” into the quality rank of every con-
tribution; while the individually assigned agreement levels are used by the system to find con-
tributions expressing presumably similar or compatible ideas or opinions. Mathematically,
some metric is defined within the space of all contributions by pairwise comparison of
agreement levels assigned by the same readers (if X agrees with both A and B, this decreases
the distance between A and B; if Y agrees with A but disagrees with B, this increases the dis-
tance). Based on this metric, the system can discover clusters of presumably similar or com-
patible contributions.
Quality ranking is then performed within every cluster. Namely, good quality grades assigned
by appraisers to a contribution will promote it towards the top of a ranked list of all contribu-
tions among those contained in a given cluster. This is indeed much more meaningful than the
ordinary ranking of all contributions in a single list; namely, our method gives voice to mi-
norities, whose opinions are not supported by many.
When a participant wants to read the “most interesting” contributions (or “the most represen-
tative”, etc), the system will suggest him/her few highly ranked contributions within every
cluster, including the smallest clusters. In this way, the participant can easily get acquainted
with the whole spectrum of the currently expressed ideas.
3.3 System Requests for Additional Appraisals
At some point our algorithm may discover that there is not enough data to decide on a given
proposal to which cluster it should go (or maybe it should start a new cluster); or to better dis-
cern the difference between two proposals or two clusters. In such cases the system may re-
quest from participants to make additional appraisals.
At this stage, however, all participants are not equal in their capacity to make an appraisal that
will be decisive for the system. For example, if participant U hasn’t yet read and appraised
neither A nor B, he should do it for both, in order for the system to increase its knowledge
about (dis)similarity between A and B. In contrast, if V has appraised A but not B or vice
versa, then the system can ask her to appraise the other one, thus receiving a valuable infor-
mation by requesting from just one requested action, rather than two in the case of U.
7. Procedures and Methods for Cross-community Online Deliberation Page 7
4. Rewriting Poorly Written but Agreeable Proposals
Let us now discuss in more details a very simple case of two-parameter appraisal or evalua-
tion, where each participant is invited to characterise a proposal with just one out of three dis-
crete values: “agree”, “disagree”, and “don’t understand”. Despite the simplicity and straight-
forwardness of this method, it is still a “two-parameter” one, and it can provide the system
with a fairly rich information on both the set of proposals on the table, and the community of
participants.
4.1 Agreement Depends Upon Understanding.
Although the quality in which a proposal is made, and how much does a person agree with it,
are seen as independent, they are not fully so. For only inasmuch as we understand a proposal
can we agree or disagree with it. We cannot agree or disagree on something we do not under-
stand. Those two measures are a little bit like light and colour. If there is no light we cannot
perceive any colour, nor does it make any sense to speak about the bandwidth of a light
source when no light source is there. Hence, the brighter a source of light the better we can
distinguish different colours. Here we consider only two "colours" (agree, disagree), and the
brightness would represent how clearly do we understand it (see Figure 1 below; in the
printed version however colours are not represented).
Fig. 1. Space of possible evaluations for a single proposal. Participants will be invited to evaluate every pro-
posal with respect to how clearly do they understand it, and how much do they agree with it. The more a partici-
pant understands a proposal, the more can he/she agree or disagree with it. When a proposal is completely in-
comprehensible, it cannot be rated on the agreement axis.
So the simplest form of evaluation that can still work in our system would have 3 discrete op-
tions: “agree”, “disagree”, and “don’t understand”. More continuous evaluations are also pos-
sible, always keeping in mind that if a person does not understand at all a proposal, he/she
should not be allowed to evaluate how much does he/she agree with it. Also, in a continuous
evaluation (or multi-grade, say with integers from -5 to +5) the span of possible agreement
grades should depend on a level of understanding, so that for a medium-level understanding
the uttermost agreement values, e.g. -5 or +5, should not be allowed. This quite natural re-
striction is graphically represented by our inverted triangle on Figure 1.
8. Page 8 Procedures and Methods for Cross-community Online Deliberation
4.2 Using the Above Two Measures.
Once the measures have been taken and users start to evaluate the existing proposals, the sys-
tem recovers a lot of useful information, much more than what can be deduced from an ordi-
nary “linear” evaluation, even a multi-grade one.
The most obvious information that can be gathered is, first, to what extent a proposal is un-
derstandable, and second, to what extent do people agree with it. But there are also less obvi-
ous data that can be gathered, as, for example : which users are able to write proposals that are
widely understandable; which users understand and agree on a specific proposal; which pro-
posals share the same user-base (in other words, which proposals are agreed upon by the same
users). Each of those characteristics can then be used in a different way.
4.3 Using Agreement Data for Clustering
Here we present the simplest form of our agreement-based clustering algorithm, for the above
3-value appraisal scheme. Suppose we have n proposals, and m users. Let Kn be the complete
weighted undirected graph with n nodes and n*(n-1)/2 edges. Let EAB be the edge from node
A to node B; we can assign to EAB a weight that represents how many people that have voted
for A have also voted for B, and how many people that have voted for B have voted for A.
Thus the weight can be defined as W=|A∩B|/|A∪B|, i.e. the cardinality of (A intersection B)
divided by the cardinality of (A union B); it is a value between 0 and 1, with W=0 if no one
voted for both A and B, and W=1 if everybody who voted for A voted for B and vice versa.
There are many straightforward ways to cluster the nodes in such a weighted graph; the sim-
plest one is to delete every edge that has a weight less than x (see [5]). Clustering the nodes of
a graph is a well researched area, so we can just use one of those existing methods to perform
proposal clustering in a mass online deliberation (see e.g. [4]).
4.4 Using Quality Evaluation
While agreement evaluation can and should be used to cluster proposals, quality evaluation
(i.e. in our case clarity, that is, how understandable they are) can be used for a different pur-
pose. Namely, by counting the proportion of participants who didn’t understood any given
proposal, the system can discover which ones are not clearly written, and then invite some-
body among the participants to rewrite those too obscure proposals.
We do not think it practical to suggest for an author to rewrite his/her own proposal, for we
assume that each author has already done his/her best. But we also do not want to just ask a
random user to rewrite a proposal. Instead, the person to rewrite a proposal that needs to be
clarified should (a) understand the proposal, (b) be able to write (proposals) well, and possi-
bly better than the original author, and (c) agree with the proposal. Although the point (c) is
not strictly necessary, a person that does not agree with a proposal might not wish to spend
time and effort in trying to rewrite it, and might be biased against it.
On the other side, a proposal that needs to be rewritten should (a) be widely considered in-
comprehensible, (b) be such that those people who do understand it would generally support it
9. Procedures and Methods for Cross-community Online Deliberation Page 9
(otherwise there is no real need to clarify it), and (c) possibly be part of a relatively small
cluster, so that the same ideas are not already present in some similar proposal(s).
If all that is true, then the proposal possibly has some interesting, but not clearly expressed
ideas; ideas that would be lost if they are not explained better. Thanks to the above-described
appraisal method, our support system will know all that information. Hence, for each proposal
that needs to be rewritten, the system can find who should rewrite the proposal, and ask that
person to rewrite it.
Once the new proposal (or a new version of the old proposal) has been written, the initial
author can be contacted and asked if he/she agrees that the new proposal still expresses his/
her ideas. If he/she agrees on that, then the new proposal can be advertised to all those people
who supported the original proposal, and also to those who had declared that they did not un-
derstood it. The new proposal can be presented as "written by 1st author and 2nd author on an
original idea of 1st author". Indeed the process can have several iterations and more co-
authors, though probably few ideas are so complex as to require more than one rewriting.
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