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Community & Business Groups

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.

Multi-document natural language processing topics include:

1. Performing fact-checking

2. Performing argument analysis

3. Detecting spin and persuasion

4. Performing sentiment analysis

5. Detecting frame building and frame setting

6. Detecting agenda building and agenda setting

7. Detecting various sociolinguistic, social semiotic, sociocultural and memetic events

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

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

10. Presenting the detected real-time information to individuals, groups and the public

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.

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