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 data.gov 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
E-Participation, Decision Support Systems, Multi-document Natural Language Processing and Cognitive Bias Mitigation
Natural Language Technology and Public Opinion Polling
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.
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.
Decision Support Systems, Cognitive Bias, Cognitive Bias Mitigation
Fact checker, Epistemology
Argumentation Theory, Theory of Justification
Spin, Persuasion, Manipulation, Media Manipulation
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)
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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.
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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.
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References (Point 4)
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References (Point 5)
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References (Point 6)
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References (Point 7)
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References (Point 8)
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References (Point 9)
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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.
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