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Coding communications across time: Documenting changes in interaction patterns across adopter categories

Published online by Cambridge University Press:  30 October 2017

KAR-HAI CHU ,
STEPHANIE R. PITTS ,
HEATHER WIPFLI  and
THOMAS W. VALENTE
KAR-HAI CHU
Affiliation:
Center for Research on Media, Technology, and Health, University of Pittsburgh, 230 McKee Place Suite 600, Pittsburgh, PA 15213, USA (e-mail: [email protected])
STEPHANIE R. PITTS
Affiliation:
Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723, USA (e-mail: [email protected])
HEATHER WIPFLI
Affiliation:
Department of Preventive Medicine, University of Southern California, 2001 North Soto Street, 3rd Floor, Los Angeles, CA 90032, USA (e-mail: [email protected]; [email protected])
THOMAS W. VALENTE
Affiliation:
Department of Preventive Medicine, University of Southern California, 2001 North Soto Street, 3rd Floor, Los Angeles, CA 90032, USA (e-mail: [email protected]; [email protected])
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Abstract

GLOBALink, a large online network of tobacco control professionals, was active in the promotion of the World Health Organization's Framework Convention on Tobacco Control treaty, an international treaty aimed at reducing the global burden of tobacco-related death and disease. We examined and compared the roles that different countries served in the GLOBALink community during FCTC negotiation and ratification. Previous studies of FCTC ratification found the process adhered to a diffusion of innovation model (Valente et al., 2015). We followed that work by conducting content analyses of discussion messages posted by GLOBALink members representing different countries. Based on the time when they ratified the FCTC, each country was labeled by one of the four adoption stages of the diffusion model and we investigated the amount of shared word use between the different stages. A goodness-of-fit chi-squared test indicated that content was not shared in an expected manner between stages (χ2 = 11,856.45, N = 51,447, p < 0.001). A deeper look at the specific words shared between countries within and between adoption stages provided insight into how interactions between certain countries might have served to support the ratification process.

Keywords

diffusioncontent analysisonline activismglobal tobacco control
Type
Research Article
Information
Network Science , Volume 5 , Issue 4 , December 2017 , pp. 441 - 460
Copyright
Copyright © Cambridge University Press 2017 

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References

Anderson, L. (2011). Demystifying the Arab spring: Parsing the differences between Tunisia, Egypt, and Libya. Foreign Affairs, 90. Retrieved from http://www.comfama.com/contenidos/Servicios/GerenciaSocial/Cursos/Universidad%20de%20Columbia%202012/Lecturas/LisaAnderson_DemystifyingtheArabSpring.pdf.Google Scholar
van Atteveldt, W. H.. (2008). Semantic network analysis: Techniques for extracting, representing, and querying media content. Retrieved from http://dare.ubvu.vu.nl/handle/1871/15964.Google Scholar
Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006). Group formation in large social networks: Membership, growth, and evolution. Presented at the Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. (pp. 44–54). Retrieved from https://doi.org/10.1145/1150402.1150412.Google Scholar
Beer, D., & Burrows, R. (2007). Sociology and, of and in Web 2.0: Some initial considerations. Sociological Research Online, 12. Retrieved from https://doi.org/10.5153/sro.1560.CrossRefGoogle Scholar
Borgatti, S. (2009). 2-mode concepts in social network analysis. In Encyclopedia of complexity and systems (pp. 8279–8291). Berlin: Springer. Retrieved from http://steveborgatti.com/papers/2modeconcepts.pdf.Google Scholar
Boyd, D., Golder, S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In Proceedings of the 2010 43rd Hawaii International Conference on System Sciences (HICSS) (pp. 1–10). IEEE Computer Society Washington, DC.Google Scholar
Breiger, R. (1974). The duality of persons and groups. Social Forces, 53, 181–190. Retrieved from https://doi.org/10.1093/sf/53.2.181.Google Scholar
Caren, N., & Gaby, S. (2011). Occupy online: Facebook and the spread of occupy wall street. Social Science Research Network. Retrieved from https://doi.org/10.2139/ssrn.1943168.CrossRefGoogle Scholar
Chu, K.-H., Wipfli, H., & Valente, T. W. (2013). Using visualizations to explore network dynamics. Journal of Social Structure, 14. Retrieved from http://www.cmu.edu/joss/content/articles/volume14/ChuWipfliValente.pdf.Google Scholar
Danowski, J. A., Riopelle, K., & Gluesing, J. (2011). The revolution in diffusion models caused by new media: The shift from s-shaped to convex curves. The diffusion of innovations: A communication science perspective (pp. 123–144). Peter Lang Publishing, Inc. New York, NY.Google Scholar
de Laat, M. (2002). Network and content analysis in an online community discourse. In Proceedings of the Conference on Computer Support for Collaborative Learning: Foundations for a CSCL Community (pp. 625–626). Boulder, Colorado: International Society of the Learning Sciences. Retrieved from http://dl.acm.org/citation.cfm?id=1658616.;1658755.Google Scholar
Deibert, R. J. (2000). International plug'n play? Citizen activism, the Internet, and global public policy. International Studies Perspectives, 1, 255–272. Retrieved from https://doi.org/10.1111/1528-3577.00026.Google Scholar
Diesner, J., & Carley, K. M. (2005). Revealing social structure from texts: Meta-matrix text analysis as a novel method for network text analysis. In In Narayanan, V. K. & Armstrong, D. J. (Eds.), Causal mapping for information systems and technology research: Approaches, advances, and illustrations (pp. 81–108). Idea Group Publishing, Hershey, PA.Google Scholar
Ellison, N., Steinfield, C., & Lampe, C. (2007). The benefits of facebook “friends:” social capital and college students use of online social network sites. Journal of Computer-Mediated Communication, 12, 1143–1168.CrossRefGoogle Scholar
Firth, D. R., Lawrence, C., & Clouse, S. F. (2006). Predicting internet-based online community size and time to peak membership using the bass model of new product growth. Interdisciplinary Journal of Information, Knowledge, and Management, 1, 1–12.Google Scholar
Fisher, D., Smith, M., & Wesler, H. T. (2006). You are who you talk to: Detecting roles in usenet newsgroups. Presented at the Proceedings of the 39th Hawaii International Conference on the System Sciences (HICSS), New Brunswick: Institute of Electrical and Electronics Engineers, Inc. (IEEE). Retrieved from https://doi.org/10.1109/HICSS.2006.536.Google Scholar
Galston, W. (2000). Does the internet strengthen community? National Civic Review, 89, 193–202. Retrieved from https://doi.org/10.1002/ncr.89302.CrossRefGoogle Scholar
Garton, L., Haythornthwaite, C., & Wellman, B. (1997). Studying online social networks. Journal of Computer-Mediated Communication, 3, 0–0.Google Scholar
Gleave, E., Welser, H., Lento, T., & Smith, M. (2009). A conceptual and operational definition of “social role” in online community. Presented at the 42nd Hawaii International Conference on System Sciences. Retrieved from citeulike-article-id:4302669.Google Scholar
Gloor, P. A., & Zhao, Y. (2006). Analyzing actors and their discussion topics by semantic social network analysis. In 10th International Conference on Information Visualisation (IV'06) (pp. 130–135). Retrieved from https://doi.org/10.1109/IV.2006.23.CrossRefGoogle Scholar
Gloor, P. A., Krauss, J., Nann, S., Fischbach, K., & Schoder, D. (2009). Web Science 2.0: Identifying trends through semantic social network analysis. In International Conference on Computational Science and Engineering, 2009. CSE '09 (Vol. 4, pp. 215–222). Retrieved from https://doi.org/10.1109/CSE.2009.186.Google Scholar
Hardt, M., & Negri, A. (2011). The fight for “real democracy” at the heart of occupy wall street. Foreign Affairs, 11. Retrieved from http://relooney.fatcow.com/0_New_11478.pdf.Google Scholar
Kajikawa, Y., Ohno, J., Takeda, Y., Matsushima, K., & Komiyama, H. (2007). Creating an academic landscape of sustainability science: An analysis of the citation network. Sustainability Science, 2 (2), 221. Retrieved from https://doi.org/10.1007/s11625-007-0027-8.Google Scholar
Kollock, P. (1998). The economies of online cooperation: Gifts and public goods in cyberspace. In Communities in the cyberspace (pp. 259–262). London: Routledge. Retrieved from citeulike-article-id:201583 http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0415191408.Google Scholar
Krippendorff, K. (2012). Content analysis: An introduction to its methodology. SAGE Publications, Incorporated, Thousand Oaks, CA. Retrieved from http://www.uk.sagepub.com/booksProdDesc.nav?prodId=Book234903.Google Scholar
Leischow, S. J., Ayo-Yusuf, O., & Backinger, C. L. (2012). Converging research needs across framework convention on tobacco control articles: Making research relevant to global tobacco control practice and policy. Nicotine & Tobacco Research, nts199. Retrieved from https://doi.org/10.1093/ntr/nts199.Google Scholar
Mamudu, H. M., & Glantz, S. A. (2009). Civil society and the negotiation of the framework convention on tobacco control. Global Public Health, 4, 150–168.Google Scholar
Matthew, R. A., & Rutherford, K. R. (2003). The evolutionary dynamics of the movement to ban landmines. Alternatives: Global, Local, Political, 28, 29–56. Retrieved from https://doi.org/10.1177/030437540302800102.CrossRefGoogle Scholar
Pazzani, M., & Billsus, D. (1997). Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27 (3), 313–331. Retrieved from https://doi.org/10.1023/A:1007369909943.Google Scholar
Ridings, C., & Gefen, D. (2004). Virtual community attraction: Why people hang out online. Journal of Computer-Mediated Communication, 10, 00–00.CrossRefGoogle Scholar
Rogers, E. (2003). Diffusion of innovations (5th ed.). New York: Free Press. Retrieved from http://www.amazon.de/exec/obidos/ASIN/0743222091.Google Scholar
Rutherford, K. R. (2000). Internet activism: NGOs and the mine ban treaty. International Journal on Grey Literature, 1, 99–106. Retrieved from https://doi.org/10.1108/14666180010345528.Google Scholar
Salton, G., Fox, E. A., & Wu, H. (1983). Extended boolean information retrieval. Communications of the ACM, 26 (11), 1022–1036. Retrieved from https://doi.org/10.1145/182.358466.Google Scholar
Shea, P., Hayes, S., Vickers, J., Gozza-Cohen, M., Uzuner, S., Mehta, R., . . . Rangan, P. (2010). A re-examination of the community of inquiry framework: Social network and content analysis. The Internet and Higher Education, 13 (1–2), 10–21. Retrieved from https://doi.org/10.1016/j.iheduc.2009.11.002.Google Scholar
Sidhu, A. K., & Barnett, G. (2012). The semantic structure of tobacco control initiatives: An analysis of missions, goals and objectives of state tobacco control programs. Presented at the International Network of Social Network Analysis (INSNA), Redondo Beach, CA.Google Scholar
Smith, M., & Kollock, P. (1999). Communities in cyberspace. Psychology Press, London, UK. Retrieved from http://www.amazon.com/Communities-Cyberspace-Peter-Kollock/dp/0415191408.Google Scholar
Stepanova, E. (2011). The role of information communication technologies in the “Arab Spring.” PONARS Eurasia, 15, 1–6.Google Scholar
Valente, T. W. (2010). Social networks and health. Oxford: Oxford University Press. Retrieved from http://www.amazon.com/Social-Networks-Health-Methods-Applications/dp/0195301013.Google Scholar
Valente, T. W., Dyal, S. R., Chu, K.-H., Wipfli, H., & Fujimoto, K. (2015). Diffusion of innovations theory applied to global tobacco control treaty ratification. Social Science & Medicine, 145, 89–97. Retrieved from https://doi.org/10.1016/j.socscimed.2015.10.001.Google Scholar
van, Zaanen, M., & Kanters, P. H. M. (2010). Automatic mood classification using tf*idf based on lyrics. In 11th International Society for Music Information Retrieval Conference (ISMIR 2010) (pp. 75–80). Creative computing. Retrieved from https://pure.uvt.nl/portal/en/publications/automatic-mood-classification-using-tfidf-based-on-lyrics(bdd6389b-f19e-499e-9fea-e7ad7bd16e0e).html.Google Scholar
Wellman, B., & Gulia, M. (1998). Virtual communities as communities: Net surfers don't ride alone. In Communities in cyberspace. London: Routledge. Retrieved from citeulike-article-id:4299353.Google Scholar
WHO. (2003). WHO Framework Convention on Tobacco Control.Google Scholar
Wipfli, H., Fujimoto, K., & Valente, T. W. (2010). Global tobacco control diffusion: The case of the framework convention on tobacco control. American Journal of Public Health, 100, 1260. Retrieved from https://doi.org/10.2105/AJPH.2009.167833.CrossRefGoogle ScholarPubMed