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Collaborative Software Community Group

The mission of the Collaborative Software Community Group is to provide a forum for experts in collaborative software and groupware for technical discussions, gathering use cases and requirements to align the existing formats, software, platforms, systems and technologies (e.g. wiki technology) with those used by the Open Web Platform. The goal is to ensure that the requirements of collaborative technology and groupware can be answered, when in scope, by the Recommendations published by W3C. This group is chartered to publish documents when doing so can enhance collaborative technology and groupware. The goal is to cooperate with relevant groups and to publish documents to ensure that the requirements of the collaborative software and groupware community are met.

Note: Community Groups are proposed and run by the community. Although W3C hosts these conversations, the groups do not necessarily represent the views of the W3C Membership or staff.

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Opinion Polling Systems and Virtual Opinion Pollsters

Intelligent personal assistants (i.e. Cortana, Google Now, Siri) or other “bots” can schedule and perform opinion polls. Specialized artificial intelligence systems, opinion polling systems or virtual opinion pollsters, can be of use to opinion polling organizations, to news organizations and to city governments.

Opinion polls can be created to open and to close at certain times. Opinion polling systems can schedule and coordinate multiple opinion polls with large numbers of respondents. Scheduling processes are envisioned as involving interactions with users and software coordination with users’ digital personal assistant software and calendar apps.

The Microsoft Bot Framework supports multiple channels of interaction, including Skype. Opinion polling systems can, while providing anonymity in terms of polling results, authenticate users including based upon biometrics, resembling the functionality of Windows Hello.

The Microsoft Bot Framework also supports hyperlinks; opinion polling systems can provide hyperlinks before or during opinion poll questions. Web browsing could become part of modeling the contexts which occur as respondents complete questionnaires. Virtual opinion pollsters can also, in dialogs, answer questions which respondents might ask.

Virtual opinion pollsters can generate and perform open-ended questions, including follow-up questions which explore the rationale, justification and argumentation of respondents’ answers, processing their natural language responses. Virtual opinion pollsters can perform structured, semi-structured and unstructured interviews. Virtual opinion pollsters can perform lists, trees or graphs of questions, paths varying based upon respondents’ answers, dynamic natural language dialogs generated in accordance with the best practices of survey methodology and questionnaire construction.

The software components of opinion polling systems can process human-generated questionnaires and other dialogs and transcripts, sequences of questions or of both questions and answers, for topics such as questionnaire construction issues, question wording issues, question sequence issues, or similar issues with dialogs. The components can also provide functionality for computer-aided questionnaire authoring tools.

Opinion polling systems can facilitate large-scale group reasoning and decision-support systems, systems where reason prevails from inclusive and participatory processes, including such processes which involve multiple or periodic polls or dialogs.

Electronic and Postal Voting

Ideas to provide citizens with the convenience of voting from home, ideas pertaining to electronic and postal voting, are broached.

After voting online, voters can print their ballots. Printed ballots can be designed for purposes including rapid visual inspection by voters to compare their printed ballots to post-printing on-screen contents. Printed ballots can include cryptographic hashes of and other visual representations of voting results, such as barcodes or QR codes. Printed ballots can include one or more confirmation numbers, barcodes or QR codes which indicate or confirm each voting event. Printed ballots can include the text of the ballots with voters’ votes or other text-based versions of voters’ votes. Printed ballots can include voters’ regional codes, postal zip codes and voting district codes. Printed ballots or the addresses upon envelopes can be recognized by post offices for free delivery for purposes of concurrent postal voting. At vote-processing locations, computer vision and optical character recognition can expedite the processing of printed ballots; the confirmation numbers, barcodes or QR codes upon printed ballots provide for interoperation with electronic systems at other locations. The two systems, electronic and postal voting, can mutually secure one another.

Using desktop apps, web apps or websites, voters can make use of one or more confirmation numbers, barcodes or QR codes to view or to confirm their individual votes and to view voting results per voting location.


Machine Learning, Artificial Intelligence and Decision Support for Large-scale Deliberative and Epistemic Democratic Processes

Advancements to machine learning and artificial intelligence technologies, advancements to argumentation and collaboration technologies, can support large-scale deliberative and epistemic democratic processes and can provide the public with a number of large-scale and transparent decision support systems.

Machine learning and artificial intelligence topics include:

1. Detecting the emergence of topics and occurrences of agenda building and agenda setting; processing news stories and social media data; during election seasons, processing every ballot nationwide

2. Modeling the processes of individuals and groups of becoming better-informed about topics, processes during which the comprehension of information results in more questions and web searches and during which certain content is relevant; modeling individual and group decision-making processes to predict questions or web searches that subsets of populations might have

3. Informing search engines and journalism organizations, produced and retrieved content then more relevant to audiences, increasing click-through; informing and government transparency advocates where predicted public processes or processes of journalism requires government data

4. Making transparent such analyses of topics and processes, providing data, visualizations and models; assembling such data for uses in science and education

5. Modeling and explaining public opinion and opinion dynamics; correlations, models, explanations and logical equivalencies, entailments or other relationships amongst questions from opinion polls can contribute to securing sets of opinion polls

6. Inspecting sequences of questions from opinion polls and from sequences of opinion polls for various questionnaire construction issues

7. Generating questions or sequences of questions, including to discover or to improve models which explain distributions of opinions or correlations amongst related questions from previous opinion polls, understanding that public opinion is dynamic

8. Processing text-based responses to questions including open-ended questions, follow-up questions and questions regarding rationale or reasoning; utilizing natural language based data in ways interoperable with modeling which explains public opinion dynamics

9. Determining when or how often to poll the public on specific topics including in response to political speeches, social media dynamics or news stories

10. Providing the public with resources for the visualization of news trends, public opinion models and dynamics, social media dynamics, search trends and search sequences or processes

11. Measuring the influence of public opinion data on the processes of public opinion, including models or explanations of public opinion, data which include follow-up questions or sequences of questions, or data which include text-based rationales and reasoning; mitigating the adverse effects and biases of public opinion data on public opinion processes

12. Coordinating varieties of questions or opinion polls at scales, national, state and local

13. Using opinion polls to discover opportunities for industry and service

14. Uses of mixed-initiative spoken dialog systems and computer-generated question sequences and dialog, including in mobile computing scenarios, in opinion polling, web search, news search and other processes through which citizens become better-informed

Related Articles

E-Participation, Decision Support Systems, Multi-document Natural Language Processing and Cognitive Bias Mitigation

Natural Language Technology and Public Opinion Polling


American Public School and University Curricula and the Teaching of E-Participation Proficiencies

Public school and university curricula topics include new literacies, computer literacy, Web literacy, collaborative and productivity software literacy, and e-participation proficiencies.

The National Council for the Social Studies defines social studies as “the integrated study of the social sciences and humanities to promote civic competence.” “Social studies educators teach students the content knowledge, intellectual skills, and civic values necessary for fulfilling the duties of citizenship in a participatory democracy.”

Collaborative and productivity software are the software of coursework, of workplaces, as well as of e-participation. Computer literacy, Web literacy and collaborative and productivity software literacy are components of civic competence, are components of e-participation proficiencies.

Student government activities, at American public schools and universities, can also be of use for teaching students e-participation proficiencies.


National Education Association
National Council for the Social Studies
American Association of University Professors

Common Core » English Language Arts Standards » History/Social Studies » Grade 11-12
Common Core » English Language Arts Standards » Speaking & Listening » Grade 11-12

American Student Government Association
National Association of Student Councils

ACM Special Interest Group on Computer Science Education
ACM Special Interest Group on Computers and Society


Kahne, Joseph E., and Susan E. Sporte. “Developing citizens: The impact of civic learning opportunities on students’ commitment to civic participation.” American Educational Research Journal 45, no. 3 (2008): 738-766.

E-Participation, Decision Support Systems, Multi-document Natural Language Processing and Cognitive Bias Mitigation

Collaborative, productivity and e-participation software topics include those of decision support systems and cognitive bias mitigation, including mitigating cognitive biases and fallacies of individual or group reasoning pertaining to misinformation, disinformation, manipulation, spin, persuasion and framing effects.

In a previous article, multi-document natural language processing technology innovations were indicated including those of real-time fact checking, argument analysis, spin and persuasion detection and sentiment analysis.

Multi-document natural language processing topics include:

1. Performing fact-checking upon collections of documents generated by e-participants in their interactions and processes and upon external collections of documents, the news, the arts and the Web

2. Performing argument analysis upon collections of documents generated by e-participants in their interactions and processes and upon external collections of documents, the news, the arts and the Web

3. Detecting spin and persuasion in collections of documents generated by e-participants in their interactions and processes and in external collections of documents, the news, the arts and the Web

4. Performing sentiment analysis upon collections of documents generated by e-participants in their interactions and processes and upon external collections of documents, the news, the arts and the Web

5. Detecting frame building and frame setting in collections of documents generated by e-participants in their interactions and processes and in external collections of documents, the news, the arts and the Web

6. Detecting agenda building and agenda setting in collections of documents generated by e-participants in their interactions and processes and in external collections of documents, the news, the arts and the Web

7. Detecting various sociolinguistic, social semiotic, sociocultural and memetic events in collections of documents generated by e-participants in their interactions and processes and in external collections of documents, the news, the arts and the Web

8. Detecting the dynamics of the attention of individuals, groups and the public

9. Detecting framing effects and other cognitive biases resulting from simultaneous or proximate, parallel and sequential, discussions of topics and subtopics

10. Presenting the detected real-time information to individuals and groups, the users of e-participation venues; supporting situation awareness and sensemaking from detected real-time information to individuals and groups, the users of e-participation venues

Multi-document processing topics expand beyond those of natural language processing to those of multimedia processing, for instance processing the images in, photographs in and layouts of the e-participation documents, slide shows and presentations, generated, utilized and hyperlinked to by individuals and groups.

The topics pertain to the modeling of user contexts, to dialogue systems technology, to digital personal assistants, to digital group assistants, to intelligent tutoring systems and to contextual or task-based information search and retrieval technology.

The topics pertain to the planning of, the scheduling of and to the automated planning and scheduling of group tasks, activities and discussion topics. Real-time accurate information and reasoning processes empower individuals, team leaders, groups and communities.

With 19,354 cities in the United States of America and with city governments and journalism organizations in nearly each, there is a market for the services described (points 1 to 10). Such service providers could access city resources, including cloud-based, as well as third-party services, such as regional search trends, to inform each individual participant and group, ensuring the quality of e-participation venues, their real-time dashboards, their group discussions, their group reasoning and their democratic processes.

See Also

Decision Support Systems, Cognitive Bias, Cognitive Bias Mitigation

Fact checker, Epistemology

Argumentation Theory, Theory of Justification

Spin, Persuasion, Manipulation, Media Manipulation

Sentiment Analysis

Framing, Framing Effect, Frame Building, Frame Setting

Agenda Building, Agenda Setting

Pragmatics, Situated Cognition, Frame Analysis, Sociolinguistics, Sociology of Culture, Umwelten

Multitasking, Task Switching, Task Interference, Task Set, Mental Set, Sensemaking, Situation Awareness, Mental Models

Group Cognition, Distributed Cognition, Social Cognition

Computational Journalism, Computer-assisted Reporting, Data-driven Journalism

References (Point 1)

Ciampaglia, Giovanni Luca, Prashant Shiralkar, Luis M. Rocha, Johan Bollen, Filippo Menczer, and Alessandro Flammini. “Correction: Computational fact checking from knowledge networks.” PloS one 10, no. 10 (2015).

Cohen, Sarah, James T. Hamilton, and Fred Turner. “Computational journalism.” Communications of the ACM 54, no. 10 (2011): 66-71.

Goasdoué, François, Konstantinos Karanasos, Yannis Katsis, Julien Leblay, Ioana Manolescu, and Stamatis Zampetakis. “Fact checking and analyzing the Web.” In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 997-1000. ACM, 2013.

Hassan, Naeemul, Bill Adair, James T. Hamilton, Chengkai Li, Mark Tremayne, Jun Yang, and Cong Yu. “The quest to automate fact-checking.” world (2015).

Pomares, Julia, and Noelia Guzmán. “Measuring the impact of fact-checking.”

Walenz, Brett, You Will Wu, Seokhyun Alex Song, Emre Sonmez, Eric Wu, Kevin Wu, Pankaj K. Agarwal et al. “Finding, monitoring, and checking claims computationally based on structured data.”

Wu, You, Pankaj K. Agarwal, Chengkai Li, Jun Yang, and Cong Yu. “Toward computational fact-checking.” Proceedings of the VLDB Endowment 7, no. 7 (2014): 589-600.

References (Point 2)

Boltuzic, Filip, and Jan Šnajder. “Back up your stance: Recognizing arguments in online discussions.” In Proceedings of the First Workshop on Argumentation Mining, pp. 49-58. 2014.

Boltuzic, Filip, and Jan Šnajder. “Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity.”

Ghosh, Debanjan, Smaranda Muresan, Nina Wacholder, Mark Aakhus, and Matthew Mitsui. “Analyzing argumentative discourse units in online interactions.” In Proceedings of the First Workshop on Argumentation Mining, pp. 39-48. 2014.

Goudas, Theodosis, Christos Louizos, Georgios Petasis, and Vangelis Karkaletsis. “Argument extraction from news, blogs, and social media.” In Artificial Intelligence: Methods and Applications, pp. 287-299. Springer International Publishing, 2014.

Lawrence, John, and Chris Reed. “Combining Argument Mining Techniques.”

Park, Joonsuk, and Claire Cardie. “Identifying appropriate support for propositions in online user comments.” ACL 2014 (2014): 29.

Salah Z, Coenen F, Grossi D. Extracting debate graphs from parliamentary transcripts: A study directed at UK House of Commons debates. InProceedings of the Fourteenth International Conference on Artificial Intelligence and Law 2013 Jun 10 (pp. 121-130). ACM.

Sergeant, Alan. “Automatic argumentation extraction.” In The semantic web: Semantics and big data, pp. 656-660. Springer Berlin Heidelberg, 2013.

Sobhani, Parinaz, Diana Inkpen, and Stan Matwin. “From Argumentation Mining to Stance Classification.”

Swanson, Reid, Brian Ecker, and Marilyn Walker. “Argument Mining: Extracting Arguments from Online Dialogue.” In 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p. 217. 2015.

References (Point 3)

Gilbert, Henry T. “Persuasion detection in conversation.” PhD diss., Monterey, California. Naval Postgraduate School, 2010.

Mills, Harry. Artful persuasion: How to command attention, change minds, and influence people. AMACOM Div American Mgmt Assn, 2000.

Ortiz, Pedro. “Machine learning techniques for persuasion dectection in conversation.” PhD diss., Monterey, California. Naval Postgraduate School, 2010.

Stab, Christian, and Iryna Gurevych. “Identifying argumentative discourse structures in persuasive essays.” In Conference on Empirical Methods in Natural Language Processing (EMNLP 2014)(Oct. 2014), Association for Computational Linguistics, p.(to appear). 2014.

Stab, Christian, and Iryna Gurevych. “Annotating argument components and relations in persuasive essays.” In Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), pp. 1501-1510. 2014.

Young, Joel, and Pedro Ortiz. “Automated Persuasion Detection in Conversation.” GSTF Journal on Computing 1, no. 3 (2011).

References (Point 4)

Boiy E, Hens P, Deschacht K, Moens M F. Automatic sentiment analysis in on-line text. In ELPUB 2007 Jun 13 (pp. 349-360).

Godbole N, Srinivasaiah M, Skiena S. Large-scale sentiment analysis for news and blogs. ICWSM. 2007 Mar 26;7:21.

Grijzenhout S, Marx M, Jijkoun V. Sentiment analysis in parliamentary proceedings. From Text to Political Positions: Text analysis across disciplines. 2014 May 15;55:117.

Li N., Wu D D. Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems. 2010 Jan 31;48(2):354-68.

Liu B. Sentiment analysis and subjectivity. Handbook of natural language processing. 2010;2:627-66.

Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies. 2012 May 22;5(1):1-67.

Liu B, Zhang L. A survey of opinion mining and sentiment analysis. In Mining Text Data 2012 Jan 1 (pp. 415-463). Springer US.

Pang B, Lee L. Opinion mining and sentiment analysis. Foundations and trends in information retrieval. 2008 Jan 1;2(1-2):1-35.

Sadegh M, Ibrahim R, Othman Z A. Opinion mining and sentiment analysis: A survey. International Journal of Computers & Technology. 2012 Jun;2(3):171-8.

References (Point 5)

Borah, Porismita. “Conceptual issues in framing theory: A systematic examination of a decade’s literature.” Journal of communication 61, no. 2 (2011): 246-263.

Goffman, Erving. Frame analysis: An essay on the organization of experience. Harvard University Press, 1974.

De Vreese, Claes H. “News framing: Theory and typology.” Information design journal+ document design 13, no. 1 (2005): 51-62.

Hänggli, Regula. “Key factors in frame building: How strategic political actors shape news media coverage.” American Behavioral Scientist (2011): 0002764211426327.

Hänggli, Regula, and Hanspeter Kriesi. “Frame construction and frame promotion (strategic framing choices).” American Behavioral Scientist 56, no. 3 (2012): 260-278.

Matthes, Jörg, and Matthias Kohring. “The content analysis of media frames: Toward improving reliability and validity.” Journal of Communication 58, no. 2 (2008): 258-279.

Matthes, Jörg. “What’s in a frame? A content analysis of media framing studies in the world’s leading communication journals, 1990-2005.” Journalism & Mass Communication Quarterly 86, no. 2 (2009): 349-367.

Matthes, Jörg. “Framing politics: An integrative approach.” American Behavioral Scientist (2011): 0002764211426324.

Pan, Zhongdang, and Gerald M. Kosicki. “Framing as a strategic action in public deliberation.” Framing public life: Perspectives on media and our understanding of the social world (2001): 35-65.

Zhou, Yuqiong, and Patricia Moy. “Parsing framing processes: The interplay between online public opinion and media coverage.” Journal of Communication 57, no. 1 (2007): 79-98.

References (Point 6)

Cobb, Roger, Jennie-Keith Ross, and Marc Howard Ross. “Agenda building as a comparative political process.” American political science review 70, no. 01 (1976): 126-138.

Cobb, Roger William. Participation in American politics: The dynamics of agenda-building. Johns Hopkins University Press, 1983.

McCombs, Maxwell, and Salma I. Ghanem. “The convergence of agenda setting and framing.” Framing public life: Perspectives on media and our understanding of the social world (2001): 67-81.

References (Point 7)

Chaoqun, Xie, and He Ziran. “Some notes on language memes.” Modern Foreign Languages 1 (2007): 005.

Fasold, Ralph. The sociolinguistics of society. Vol. 1. Oxford: Basil Blackwell, 1984.

Leskovec, Jure, Lars Backstrom, and Jon Kleinberg. “Meme-tracking and the dynamics of the news cycle.” In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 497-506. ACM, 2009.

Linxia, Chen, and He Ziran. “Analysis of memes in language.” Foreign Language Teaching and Research2 (2006): 114-118.

Shah, Dhavan V., Jaeho Cho, William P. Eveland, and Nojin Kwak. “Information and expression in a digital age modeling Internet effects on civic participation.” Communication research 32, no. 5 (2005): 531-565.

Zi-ran, He. “Linguistic memes and their rhetoric effects.” Foreign Language Research 1 (2008): 012.

References (Point 8)

Downs, Anthony. “The issue–attention cycle.” The public interest 28 (1972): 38-50.

Miller, M. Mark, and Bonnie Parnell Riechert. “The spiral of opportunity and frame resonance: Mapping the issue cycle in news and public discourse.” Framing public life: Perspectives on media and our understanding of the social world (2001): 107-121.

References (Point 9)

Altmann, E. M. & Gray, W. D. (2000). An integrated model of set shifting and maintenance. In N. Taatgen & J. Aasman (Eds.), In Proceedings of the third international conference on cognitive modeling (pp. 17-24). Veenendaal, The Netherlands: Universal Press.

Altmann, E. M., & Gray, W. D. (2008). An integrated model of cognitive control in task switching. Psychological Review, 115, 602-639.

Lebiere, C. (2001). A theory-based model of cognitive workload and its applications. In Proceedings of the 2001 Interservice/Industry Training, Simulation and Education Conference (I/ITSEC). Arlington, VA: NDIA.

Nijboer, Menno, Jelmer P. Borst, Hedderik Van Rijn, and Niels A. Taatgen. “Predicting interference in concurrent multitasking.” In Proceedings of the 12th International Conference on Cognitive Modeling. Ottawa, Canada. 2013.

Salvucci, D. D., Taatgen, N. A., & Kushleyeva, Y. (2006). Learning when to switch tasks in a dynamic multitasking environment. In Proceedings of the Seventh International Conference on Cognitive Modeling (pp. 268-273). Trieste, Italy.

Schoelles, M. J. & Gravy, W. D. (2003). Top-down versus bottom-up control of cognition in a task switching paradigm. In F. Detje, D. Doerner, & H. Schaub (Eds.), In Proceedings of the Fifth International Conference on Cognitive Modeling (pp. 295-296). Bamberg, Germany: Universitats-Verlag Bamberg.

Sohn, M.-H., & Anderson, J. R. (2003). Stimulus-related priming during task switching. Memory & Cognition, 31 (5), 775-780.

Sun, Ron. “Introduction to computational cognitive modeling.” Cambridge handbook of computational psychology (2008): 3-19.

Vandierendonck, André, Baptist Liefooghe, and Frederick Verbruggen. “Task switching: interplay of reconfiguration and interference control.” Psychological bulletin 136, no. 4 (2010): 601.

E-Participation, Social Networks and the Well-informedness of Communities

During e-participation, participants produce, consume and review city-scale government data, documents and multimedia, including real-time varieties. Participants are well-informed about topics relevant to city governance, relevant to communities.

Journalists could be amongst e-participants and, alongside journalists, e-participants are envisioned as distributing information to communities. Members of communities beyond regular e-participants could also visit e-participation venues or utilize related online resources and services to become well-informed.

Social media could be a component of information distribution. Alongside local news, well-informed participants could post the real-time information of e-participation, of city governance, as well as other information. A large portion of the municipal governments in the United States are small and medium-sized cities; 80% of American cities have populations fewer than 10,000 people.

Collaborative and productivity software, the software of e-participation, facilitate city-scale government transparency. Local newspapers could utilize the same dashboard software of city governments to obtain situational awareness from city-scale government transparency data.


Helsley, Robert W., and Yves Zenou. “Social networks and interactions in cities.” Journal of Economic Theory 150 (2014): 426-466.

Huckfeldt, Robert, and John Sprague. “Networks in context: The social flow of political information.” American Political Science Review 81, no. 04 (1987): 1197-1216.

Huckfeldt, Robert, Paul Allen Beck, Russell J. Dalton, and Jeffrey Levine. “Political environments, cohesive social groups, and the communication of public opinion.” American Journal of Political Science (1995): 1025-1054.

Huckfeldt, Robert. “Networks, contexts, and the combinatorial dynamics of democratic politics.” Political Psychology 35, no. S1 (2014): 43-68.

Kennedy, Bruce M. “Community Journalism: A Way of Life.” (1974).

Lake, Ronald La Due, and Robert Huckfeldt. “Social capital, social networks, and political participation.” Political Psychology (1998): 567-584.

Yamamoto, Masahiro. “Community newspaper use promotes social cohesion.” Newspaper Research Journal 32, no. 1 (2011): 19.

Recommender Systems, Machine Learning and Multi-document Natural Language Processing

A number of technologies including Office Graph can ensure that relevant, fresh, information and documents are available to individuals and groups during the performance of their tasks. Items that can be recommended, that can be routed, sorted and presented, include documents, multimedia and data. Software such as Office Graph utilize sophisticated machine learning algorithms to connect people to relevant content, conversations and people around them, including based upon their multiple simultaneous interests, tasks, groups or roles.

Innovations are possible with regard to the determination of contextual, task-based, relevance for recommending, routing, sorting and presenting content to individuals and to groups, enhancing their performance or providing them with serendipitous discovery.

Multi-document natural language processing algorithms can provide new conveniences to individuals and to groups, processing collections of documents and multimedia utilized by individuals and by groups during their various tasks including those of business, education and e-participation scenarios. Multi-document natural language processing algorithms are interoperable with advanced machine learning algorithms including those utilized by software such as Office Graph. Multi-document natural language processing technology innovations include, but are not limited to, real-time fact checking, argument analysis, spin and persuasion detection and sentiment analysis.


E-Participation and Online Identity Management

Extending the Sitemap protocol can facilitate new conveniences and features for e-participation venues.

Presently, in the Sitemap protocol, scalar priorities between 0.0 and 1.0 can be indicated for pages or URL’s. Ideas for extending the protocol include scalars per page or URL per sets or sequences of keywords. Specific scenarios include the names of participants.

Discussions or activities that participants indicate, for example with like buttons or other user interface elements, could then be prioritized for search retrieval utilizing Sitemap XML files generated by e-participation software.

Such technology can provide individuals with opportunities to be proactive with regard to their online identities, utilizing user interface elements at e-participation venues for online identity management, empowering individuals to participate more freely. Lawyers, for example, might advise others, their neighbors, in unfolding group dialogues, regardless of or orthogonally to their individual political opinions per issue or topic.

All experts, in their expert roles, tend to contribute on topics proximate to those of their careers or fields, thus utilizing terminology or keywords proximate to their careers or fields. By providing each participant, in particular each expert, with user interface elements to optionally prioritize the organic, emergent, interactions and group discussions that they participate in at city-scale e-participation venues, to proact in terms of online identity management, every participant, every expert, is more empowered to contribute and to provide service to others.

Social Demographics that Enhance City-scale E-Participation Processes

There are a number of social demographics to encourage to participate to enhance processes at city-scale e-participation venues.

Groups to encourage to e-participate include the young professional lawyers of each city. Participation can provide opportunities for individuals to distinguish themselves as well as to network with their neighbors, with other lawyers, with bureaucrats and with politicians.

Groups to encourage to e-participate include social studies educators. According to the National Council for the Social Studies, social studies teachers are “role models for civic participation”, “will know how to use and employ the latest technology applications to facilitate learning” and should have “equal access to all the resources they need.” The NCSS strategic plan includes that the NCSS “will work with other groups to identify the future direction of technology and sciences.”

Groups to encourage to e-participate include university professors, including but not limited to political science professors, law professors, history professors, law history professors, urban planning professors, city science professors and civics professors. The American Association of University Professors protects academic freedom and provides resources on the topic.

E-Participation and Role-based E-Participation Systems

Role-based e-participation is where participants can toggle their sociological, professional, expert and user roles per activity or contribution at e-participation venues. Such features are envisioned as pertaining to organizational processes at e-participation venues, to the indexing, search and retrieval of content on e-participation venues as well as to the Web-based searching of their contributions.

Lawyers, for example, can participate at e-participation venues in multiple roles and lawyers should be able to indicate or toggle roles. In legal roles, lawyers can advise ad hoc groups about the democratic processes and laws of their communities; in citizen roles, they can express opinions and discuss topics alongside their neighbors.