The document describes an automated facilitation agent for crowd-scale online discussions. It summarizes:
1) The agent was used to facilitate large-scale discussions in Nagoya, Japan with over 1,000 opinions posted simultaneously across discussion themes.
2) The agent aims to address the challenges of human facilitators not scaling to these large discussions and discussions becoming tangled. It extracts discussion structures and provides automated facilitation like questioning and mediating.
3) A social experiment was conducted with Nagoya city where the automated agent facilitated themes and extracted more posts than human facilitators, and user satisfaction was the same between human and automated facilitation.
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The Civic Tech timeline: a recent history (Matt Stempeck and Micah L. Sifry, ...mysociety
This was presented by Matt Stempeck and Micah L. Sifry (Civic Hall) at the Impacts of Civic Technology Conference (TICTeC 2019) in Paris on 19th March 2019. You can find out more information about the conference here: http://tictec.mysociety.org/2019
Slides for Personal Democracy Forum in 2018 about experiments in Taiwan on prototyping future democracy, focusing on three projects: PO network, vTaiwan and Holopolis.
Overview presentation of the CPaaS.io project given at the first year review meeting in Tokyo on October 5, 2017.
Disclaimer:
This document has been produced in the context of the CPaaS.io project which is jointly funded by the European Commission (grant agreement n° 723076) and NICT from Japan (management number 18302). All information provided in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission and NICT have no liability in respect of this document, which is merely representing the view of the project consortium. This document is subject to change without notice.
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Need Response 1:
The subcomponent of crowdsourcing ICT platform technological architecture I would like to discuss is that gives additionally created an examination concerning public text information (for instance blog postings, comments, appraisals, etc.) and setting up this information sources using sentiment mining tools. The Web has changed the way wherein people express their emotions, offering them the capacity to post comments and reviews on business things and express their points of view on a a huge amount of issues in parties, talk get-togethers, visit rooms, long-broaden agreeable correspondence get-togethers and web diaries. This customer passed on the substance has been seen as a tremendous wellspring of business and political information. Notwithstanding, the tremendous the degree of this information and its normal language structure makes it difficult to remove the consistent areas, for instance, the general inclination/assessment (for instance positive, negative or sensible) on the particular subject (for instance a thing/affiliation or another methodology proposition) and the specific issues raised about it by the customers/visitors of these objectives. It is hidden motivation has been to enable firms to research online overviews and comments entered by customers of their things in various review districts, web diaries, social affairs, etc., in order to arrive at general judgments as for whether customers adored the thing or not (supposition assessment), and moreover continuously express finishes concerning features (traits) of the thing that has been commented on insistently or conflictingly (features extraction and examination).
This subcomponent performs three tasks, firstly it classifies the opinion text, a document which includes various declarations like a dialogue or a blog spot conveying a positive, negative or unprejudiced end. This is suggested as the record level evaluation examination. Secondly further focusing on sentence-level which deals with the gathering of a sentence as objective or passionate, it organizes each sentence in such a structure, that atmosphere it is a unique or targets (demonstrating whether it can express the inclination or not). For each sentence that is a conceptual (infers conveying an inclination) further, the portrayal is done as imparting an appositive, negative or unprejudiced supposition. Lastly extracting the most commented features of the commented articles, and for each commented feature further classification of relevant opinion is executed as positive, negative or unprejudiced.
References
Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.). (2015). Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research (Vol. 10). Springer
Need response 2:
nformation and communication technology platform has an important role to play in active crowdsourcing. A policy maker of a government agency initiates ...
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Overview presentation of the CPaaS.io project given at the first year review meeting in Tokyo on October 5, 2017.
Disclaimer:
This document has been produced in the context of the CPaaS.io project which is jointly funded by the European Commission (grant agreement n° 723076) and NICT from Japan (management number 18302). All information provided in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission and NICT have no liability in respect of this document, which is merely representing the view of the project consortium. This document is subject to change without notice.
AGIMO Web Publishing Guide Review - Research Reportwebpublishing
TNS Social Research was commissioned by the Australian Government Information Management Office (AGIMO) to inform further development of the online Web-Publishing Guide produced by the Department of Finance and Deregulation.
Introducing Public Square, Michelle Brook (Democratic Society) mysociety
Citizen participation is often low, but the desire to get things changed is rising. Public Square will explore how to increase democratic engagement beyond elections.
Alexa, Tell Me About Global Chatbot Design and Localization!Ultan O'Broin
Personal capacity, community giveback, non-oracle emp, pesentation given to SF Globalization Meetup iin San Francisco on February 2018. Opinions and views expressed there and in this presentation format are those of Ultan O'Broin only.
Need Response 1The subcomponent of crowdsourcing ICT platform.docxvannagoforth
Need Response 1:
The subcomponent of crowdsourcing ICT platform technological architecture I would like to discuss is that gives additionally created an examination concerning public text information (for instance blog postings, comments, appraisals, etc.) and setting up this information sources using sentiment mining tools. The Web has changed the way wherein people express their emotions, offering them the capacity to post comments and reviews on business things and express their points of view on a a huge amount of issues in parties, talk get-togethers, visit rooms, long-broaden agreeable correspondence get-togethers and web diaries. This customer passed on the substance has been seen as a tremendous wellspring of business and political information. Notwithstanding, the tremendous the degree of this information and its normal language structure makes it difficult to remove the consistent areas, for instance, the general inclination/assessment (for instance positive, negative or sensible) on the particular subject (for instance a thing/affiliation or another methodology proposition) and the specific issues raised about it by the customers/visitors of these objectives. It is hidden motivation has been to enable firms to research online overviews and comments entered by customers of their things in various review districts, web diaries, social affairs, etc., in order to arrive at general judgments as for whether customers adored the thing or not (supposition assessment), and moreover continuously express finishes concerning features (traits) of the thing that has been commented on insistently or conflictingly (features extraction and examination).
This subcomponent performs three tasks, firstly it classifies the opinion text, a document which includes various declarations like a dialogue or a blog spot conveying a positive, negative or unprejudiced end. This is suggested as the record level evaluation examination. Secondly further focusing on sentence-level which deals with the gathering of a sentence as objective or passionate, it organizes each sentence in such a structure, that atmosphere it is a unique or targets (demonstrating whether it can express the inclination or not). For each sentence that is a conceptual (infers conveying an inclination) further, the portrayal is done as imparting an appositive, negative or unprejudiced supposition. Lastly extracting the most commented features of the commented articles, and for each commented feature further classification of relevant opinion is executed as positive, negative or unprejudiced.
References
Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.). (2015). Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research (Vol. 10). Springer
Need response 2:
nformation and communication technology platform has an important role to play in active crowdsourcing. A policy maker of a government agency initiates ...
Engaging Times - We are the Engagement Generation (Online)Steven Clift
A fresh keynote to the Consultation Institute annual conference in London. 5 key lessons from 20 years of e-democracy and 3 major themes for the next decade. (And two more bonus themes in slides only.)
To schedule an updated version of this speech, contact Steven Clift: http://stevenclift.com
Live/updated Google Slides version from: http://e-democracy.org/learn
[Design Sprint Workshop] Engagement Metrics for Social Impact: Alisa Zomer (M...mysociety
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Agent that Facilitates Crowd Discussion (ACM CI 2019 at CMU)
1. Agent that Facilitates Crowd Discussion
Takayuki Ito,
Daichi Shibata, Shota Suzuki, Naoko Yamaguchi,
Tomohiro Nishida, Kentaro Hiraishi, and Kai Yoshino
Nagoya Institute of Technology,
Japan.
2. Agent that facilitate crowd discussion
• Crowd-scale discussion platforms are receiving great attention as
next-generation democratic citizen platforms [Malone &
Klein 2007; Malone 2018]
• COLLAGREE [Ito CI2014, Ito CI2015, Sengoku &Ito CI2016, Ito
2018]
• Large-scale experiment with Nagoya-city in 2013.
• 266 users, 1151 opinions, and 18,466 views.
• Human facilitators promote crowd-scale online discussion.
• More than 30 other experiments
• Problem
• Human facilitators cannot scale
• Discussion often have over a thousand opinions posted
simultaneously.
• Many discussion threads become tangled with overlapping
opinions.
• We propose automated facilitation agent
3. D-agree : crowd-scale discussion support system
based on automated facilitation agent
Posting form
Discussion board
Theme
Points
Important keywords
4.
5. D-agree :
Crowd-scale discussion support system based on
facilitation agent
Discussion structure
Question
mediation
Idea/Position
extraction
How can we solve
congestion in Nagoya
city?
How about to introduce
a traffic tax mechanism?
That is a good idea
What are merits for
this idea?
(facilitation)
Only people who
want to enter can
enter the city.
Discussion
Issue
extraction Issue
Idea
Pros/Cons
Pros/Cons
Idea
AutomatedFacilitationAgent
ELSI committee
Consensus DB
Knowledge graph
SNS, Twitter, etc.
IBIS
Arguments
extraction
6. Web server(Apache)
Discussion DB
(Amazon RDS)
Theme data
Browser
Posting data
User data
Keyword data
CoffeeScript
JavaScript
Spine.js
PHP
Keyword
Extraction
Incentive
Calculation
Keywords
Discussion
Data
AWS ( Amazon Web Service )
Specification
Visualization
OS Ubuntu 16 (on AWS)
Language PHP, Python
DB MySQL, AWS RDS
Web server Apache
Framework Ruby on Rails3, Spine.js
AWS LB
Posting
Observation
Data
Data
acquistion
Observing & Posting
mechanism
Structure extraction and
Visualization mechanism
Data
acquisition
visualize Structure data
Knowledge DB
Data
instruction
Automated Facilitation Agent
Python
Python
Sentence Word Relations
Data sharing by black board model
System Architecture
7. Node and Link identification with DNN
F issue idea pros cons
Apl - June 0.382 0.436
July - Aug 0.816 0.216 0.293 0.526
Sep - Oct 0.743 0.708 0.460 0.518
Nov 0.887 0.761 0.530 0.543
Node identification
Precision idea->issue pros->idea cons->idea issue->idea
Apl - June 0.070
July - Aug 0.648
Sep - Oct 0.678 0.573 0.735
Nov 0.895 0.818 0.918 0.900
Link identification
fastText Bi-LSTM
4 class
output
Dense
An
idea’s
fastText
Real
Issue1
Real
Issue2
Real
Issue3
An issue’s
Prediction
similarity
Bi-
LSTM
Dense
9. January 15, 2019 Intelligence Augmentation and Amplification 9
Nagoya-city Social Experiment in 2018
Date: November 1st – December 7th, 2018
1st phase: 30 days to discuss
2nd phase: 7 days to express agreement for ideas
5 Theme: How to realize plans in Nagoya city?
- Theme 1: Respectful city for human rights (FA: Human)
- Theme 2: Kind city to raise children (FA: Human)
- Theme 3 : Safe and secure city (FA: Auto)
- Theme 4 : City that has comfortable urban environment
and nature (FA: Auto)
- Theme 5 : City that attract people and companies from
the world (FA: Auto & Human)
Page views: 15199
Visited participants: 798
Registered participants: 157
Posts: 432
10. Theme
Postings
All Human FA Auto FA Participants
1: Human FA 81 43 0 38
2: Human FA 56 21 0 35
3: Auto FA 88 0 24 64
4: Auto FA 70 0 18 52
5: A & H FA 137 17 21 99
Sum 432 81 63 288
• Human FA is Human Facilitator. Auto FA is Automated Facilitation Agent.
• Auto FA (red numbers) successfully extracted more posts from participants than
Human FA (blue numbers).
Results: the number of postings
11. 3.4 3.4
3.6
3.2
3.7
0
1
2
3
4
5
Theme 1
Human FA
Theme 2
Human Fa
Theme 3
Auto FA
Theme 4
Auto FA
Theme 5
A & H FA
Average
score
“Are you satisfied with the discussion of city plan?”
• Questionnaire (N=20) results of “are you satisfied with the
discussion of the city plan?”.
• Satisfaction scores on discussion by Auto FA were satisfied at
same level as discussion by Human FA.
Results: satisfaction scores
12. Issue 1: We need to increase
interest in Nagoya worldwide
Automated Facilitation Agent:
How can we solve it?
Idea 1: Advertising on TV
Automated Facilitation Agent (Issue 2):
What is necessary for the idea?
Idea 2: More collaboration between
Nagoya City and TV production
companies was proposed.
Results: a successful real case
Automated Facilitation Agent:
Identified as an issue
Automated Facilitation Agent:
Identified as an idea
14. Conclusions
• D-agree : A crowd-scale discussion support system based on
an automated facilitation agent, which extracts discussion
structures from text discussions, analyzes them, and posts facilitation
messages.
• A large-scale experiment with Nagoya City’s local government
in which our automated facilitation agent worked quite well.
• Auto FA extracted more postings
• Users satisfactions were at the same label between Human FA & Auto FA
• 15199 page views, 798 visited participants, 157 registered participants, and 432
posts.
• Future work
• Measures for good discussoin
• The automated facilitation agent can be biased, which is related to
Ethics (ELSI). How much bias people can accept?
15. IBIS(Issue—Based Information System)
The elements of IBIS are: issues (questions that need to be answered), each
of which are associated with (answered by) alternative positions (possible
answers or ideas), which are associated with arguments which support or
object to a given position; arguments that support a position are called
"pros", and arguments that object to a position are called "cons”. In the
course of the treatment of issues, new issues come up which are treated
likewise. (Wikipedia)
15
- Rittel, Horst W. J.; Noble, Douglas E. Issue-based information systems for design. Berkeley:
Institute of Urban and Regional Development, University of California, Berkeley. OCLC 20155825.
492. Jan 1989.
This is the whole architecture of our system.
Our system is working on Amazon Web Service, which enables people can use our system from everywhere in the world.
Because of AWS, our system is scalable enough.
Facilitation agent extracts words, sentences, and relations by using Bilateral LSTM and temporal CNN.
Also, facilitation agent is being invoked by using Amazon CloudWatch and it uses Amazon Lambda function.
This is a real case in which the automated facilitation agent successfully facilitated discussion among participants.
Issue 1 was raised by the participants. The automated facilitation agent identified this post as an Issue. Then he/she asked “How can we do to solve it?” Then a participant posted idea 1. The automated facilitation agent identified this post as an idea and raised an issue to deepen the idea. Then a participant posted idea 2. The automated facilitation agent works very efficiently. In particular, identifying posting type is accurate because of deep learning technology and well-trained data.
This is an outline of our facilitator agent.
Agent visualizes discussion structure from online text discussion, and
also post facilitation messages based on the extracted discussion structure.
Also, agent manages risks for inadequate posts, and also mataintaing knowledge base for discussion.