Paper Status Tracking

Article
Author(s)

Petteri Repo, Kaisa Matschoss, Päivi Timonen

Affiliation(s)

University of Helsinki, Finland

ABSTRACT

There are increasing calls for engaging citizens in the development of future outlooks. At the same time, large-scale public engagement activities warrant appropriate methods for analyzing their outcomes. This paper reviews how topic modeling could provide such a methodology, which both accounts for all textual data collected in public engagement activities, however large in scope, yet also allows for meaningful topical analysis. It compares topic modeling results concerning a corpus of 179 citizen visions from 30 European countries on desirable and sustainable futures to those acquired through deliberative analysis. While both methodologies contend that European citizens’ outlook consists of education, sustainability in the economy, health concerns, and fairness in communities, and the particular strengths of topic modeling relate to its documentability, repeatability, cost efficiency, and scalability. Topic modeling can also be considered to support public engagement analytically from the perspective of knowledge formation rather than that of common sense. from exhaustible resources must be compared to the potential damage to the tourism sector, which has been a long-standing economic driving force for the archipelago.

KEYWORDS

Topic modeling, citizen visions, deliberation, public engagement, European research and innovation programs

Cite this paper

Sociology Study, May 2017, Vol. 7, No. 5, 246-262

References

Bedsted, B., J. Ibsen-Jensen, E. Kloppenborg, B. Kyhn, M. Kaarakainen, K. Matschoss, and P. Repo. 2016. European Citizens’ Visions for a Sustainable EU Future. Research Priorities and Policy Advice. Deliverable 3.3 of the CASI project. Retrieved September 12, 2017 (http://www.casi 2020.eu/app/web1/files/download/casi-d3-3-european-citizens-visions-for-sustainable-eu-future.pdf).

Blei, D. M. 2012. “Probabilistic Topic Models.” Communications of the ACM 55(4):77-84.

Blei, D. M., A. Y. Ng, and M. I. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3(Jan):993-1022.

Bowser, A. and L. Shanley. 2013. New Visions in Citizen Science. Case study series, vol. 3. Woodrow Wilson International Center for Scholars, Washington, D.C.

Burgess, J. and J. Chilvers. 2006. “Upping the Ante: A Conceptual Framework for Designing and Evaluating Participatory Technology Assessments.” Science and Public Policy 33(10):713-728.

CIVISTI (Citizen Visions on Science, Technology and Innovation). 2011. Periodic Report Summary. Retrieved September 12, 2017 (http://cordis.europa.eu/result/rcn/ 45954_ en.html).

Cobb, M. D. 2012. “Deliberative Fears Citizen Deliberation About Science in a National Consensus Conference.” Pp. 115-122 in Public Engagement and Emerging Technologies, edited by K. O’Doherty and E. Einsiedel. Vancouver, Toronto: UBC Press.

Coleman, S., and J. Gotze. 2001. Bowling Together: Online Public Engagement in Policy Deliberation. London: Hansard Society.

Corbin, J. and Strauss, A. 1990. “Grounded Theory Research: Procedures, Canons, and Evaluative Criteria.” Qualitative Sociology 13(1):3-21.

Ducci, G. 2013. “Digital Public Communication in Europe for a European Public Sphere.” Sociology Study 3(6):425-436.

Gläser, J., W. Glänzel, and A. Scharnhorst. 2017. “Same Data—Different Results? Towards a Comparative Approach to the Identification of Thematic Structures in Science.” Scientometrics 111(2):981-998.

Graham, S., S. Weingart, and I. Milligan. 2012. Getting Started With Topic Modeling and MALLET. Retrieved September 12, 2017 (http://programminghistorian.org/lessons/topic- modeling-and-mallet).

Gudowsky, N., W. Peissl, M. Sotoudeh, and U. Bechtold. 2012. “Forward-Looking Activities: Incorporating Citizens’ Visions. A Critical Analysis of the CIVISTI Method.” Poiesis & Praxis 9:101-123.

Jacobi, C., W. van Atteveldt, and K. Welbers. 2016. “Quantitative Analysis of Large Amounts of Journalistic Texts Using Topic Modelling.” Digital Journalism 4(1):89-106.

Janasik, N., T. Honkela, and H. Bruun. 2009. “Text Mining in Qualitative Research: Application of an Unsupervised Learning Method.” Organizational Research Methods 12(3):436-460.

Jasanoff, S. 2003. “Technologies of Humility: Citizens Participation in Governing Science.” Minerva 41:223-244.

Jørgensen, M.-L. and S. Schøning. 2016. Vision Catalogue. Encompassing the Visions From All 30 Countries. CIMULACT project, deliverable 1.3. Retrieved September 12, 2017 (http://www.cimulact.eu/wp-content/uploads/ 2016/06/D1.3final.pdf).

Kahane, D., K. Loptson, J. Herriman, and M. Hardy. 2013. “Stakeholder and Citizen Roles in Public Deliberation.” Journal of Public Deliberation 9(2), Article 2.

McCallum, A. K. 2002. MALLET: A Machine Learning for Language Toolkit. Retrieved September 12, 2017 (http://mallet.cs.umass.edu).

Niemeyer, S. 2011. “The Emancipatory Effect of Deliberation: Empirical Lessons From Mini-Publics.” Politics & Society 39(1):103-140.

Nyaga, D. and R. A. Torres. 2015. “The Politics of Cultural Representation.” Sociology Study 5(9):744-758.

PACITA (Parliaments and Civil Society in Technology Assessment). 2016. Final Report Summary. Retrieved September 12, 2017 (http://cordis.europa.eu/result/rcn/1779 48_fr.html).

Rehurek, R. and P. Sojka. 2010. “Software Framework for Topic Modeling With Large Corpora.” In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks.

Renn, O. and P. J. Schweizer. 2009. “Inclusive Risk Governance: Concepts and Application to Environmental Policy Making.” Environmental Policy and Governance 19(3):174-185.

Riisgaard, K., S. Schøning, and CIMULACT Consortium Partners. 2017. Vision Catalogue—Encompassing the Visions From All 30 Countries. Document originated from D1.3. CIMULACT project. Retrieved (http://www.cimulact. eu).

Smallman, M. 2017. “Science to the Rescue or Contingent Progress? Comparing 10 Years of Public, Expert and Policy Discourses on New and Emerging Science and Technology in the United Kingdom.” Public Understanding of Science, May 1, pp. 1-19.

Steyvers, M. and T. Griffiths. 2007. “Probabilistic Topic Models.” Handbook of Latent Semantic Analysis 427(7):424-440.

Stoneman, P., P. Sturgis, and N. Allum. 2013. “Exploring Public Discourses About Emerging Technologies Through Statistical Clustering of Open-Ended Survey Questions.” Public Understanding of Science 22(7):850-868.

Usman, A. K. 2014. “Analysis of the Story-Line Behind Selected Hausa Proverbs.Sociological Study 4(8):673-680.

VOICES. 2015. Final Report Summary. Retrieved September 12, 2017 (http://cordis.europa.eu/result/rcn/158173_en. html).

Wallach, H. M., I. Murray, R. Salakhutdinov, and D. Mimno. 2009. “Evaluation Methods for Topic Models.” Pp. 1105-1112 in Proceedings of the 26th Annual International Conference on Machine Learning. ACM.

Warnke, P., A. Meroni, M. Rossi, D. Selloni, and A. M. Ospina Medina. 2017. First Draft of Social Needs Based Research Scenarios. Deliverable 2.1 of the CIMULACT project. Retrieved September 12, 2017 (http://www.cimulact.eu/ wp-content/uploads/2017/03/CIMULACT-D2.1_final.pdf).

Wilsdon, J. and R. Willis. 2004. See-Through Science: Why Public Engagement Needs to Move Upstream. London: Demos.

Yau, C. K., A. Porter, N. Newman, and A. Suominen. 2014. “Clustering Scientific Documents With Topic Modeling.” Scientometrics 100(3):767-786.

About | Terms & Conditions | Issue | Privacy | Contact us
Coryright © 2015 David Publishing Company All rights reserved, 616 Corporate Way, Suite 2-4876, Valley Cottage, NY 10989
Tel: 1-323-984-7526, 323-410-1082; Fax: 1-323-984-7374, 323-908-0457 , www.davidpublisher.com, Email: order@davidpublishing.com