Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-09T00:33:28.231Z Has data issue: false hasContentIssue false
  • English
  • Français

The state of GMOs on social media

An analysis of state-level variables and discourse on Twitter in the United States

Published online by Cambridge University Press:  03 September 2020

Christopher D. Wirz Open the ORCID record for Christopher D. Wirz [Opens in a new window] ,
Emily L. Howell Open the ORCID record for Emily L. Howell [Opens in a new window] ,
Dominique Brossard ,
Michael A. Xenos  and
Dietram A. Scheufele
Christopher D. Wirz*
Affiliation:
University of Wisconsin–Madison
Emily L. Howell
Affiliation:
University of Wisconsin–Madison
Dominique Brossard
Affiliation:
University of Wisconsin–Madison; Morgridge Institute for Research
Michael A. Xenos
Affiliation:
University of Wisconsin–Madison
Dietram A. Scheufele
Affiliation:
University of Wisconsin–Madison; Morgridge Institute for Research
*
Corresponding author: Christopher D. Wirz, University of Wisconsin–Madison, Madison, WI. Email: cwirz@wisc.edu
Get access

Abstract

This study analyzes the relationship between state-level variables and Twitter discourse on genetically modified organisms (GMOs). Using geographically identified tweets related to GMOs, we examined how the sentiments expressed about GMOs related to education levels, news coverage, proportion of rural and urban counties, state-level political ideology, amount of GMO-related legislation introduced, and agricultural dependence of each U.S. state. State-level characteristics predominantly did not predict the sentiment of the discourse. Instead, the topics of tweets predicted the majority of variance in tweet sentiment at the state level. The topics that tweets within a state focused on were related to state-level characteristics in some cases.

Keywords

content analysisscience communicationgenetic engineering
Type
Article
Information
Politics and the Life Sciences , Volume 40 , Issue 1 , Spring 2021 , pp. 40 - 55
Copyright
© The Author(s) 2020. Published by Cambridge University Press on behalf of Association for Politics and the Life Sciences

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agility PR Solutions. (2016). Daily newspapers by circulation. https://www.agilitypr.com/Google Scholar
Anderson, A. A., & Huntington, H. E. (2017). Social media, science, and attack discourse: How Twitter discussions of climate change use sarcasm and incivility. Science Communication, 39(5), 598–620.CrossRefGoogle Scholar
Berry, W. D., Fording, R. C., Ringquist, E. J., Hanson, R. L., & Klarner, C. E. (2010). Measuring citizen and government ideology in the US states: A re-appraisal. State Politics & Policy Quarterly, 10(2), 117–135.CrossRefGoogle Scholar
Berry, W. D., Ringquist, E. J., Fording, R. C., & Hanson, R. L. (1998). Measuring citizen and government ideology in the American states, 1960–93. American Journal of Political Science, 42(1), 327–348.CrossRefGoogle Scholar
Brossard, D. (2012). Social challenges: Public opinion and agricultural biotechnology. In Popp, J., Jahn, W., Matlock, M., & Kemper, N. (Eds.), The Role of Biotechnology in a Sustainable Food Supply (pp. 1–15). Cambridge University Press.Google Scholar
Brossard, D., & Nisbet, M. C. (2007). Deference to scientific authority among a low information public: Understanding U.S. opinion on agricultural biotechnology. International Journal of Public Opinion Research, 19(1), 24–52. https://doi.org/10.1093/ijpor/edl003CrossRefGoogle Scholar
Burstein, P. (2003). The impact of public opinion on public policy: A review and an agenda. Political Research Quarterly, 56(1), 29–40.CrossRefGoogle Scholar
Chan, M. S., Winneg, K., Hawkins, L., Farhadloo, M. , Jamieson, K. H., & Albarracín, D. (2018). Legacy and social media respectively influence risk perceptions and protective behaviors during emerging health threats: A multi-wave analysis of communications on Zika virus cases. Social Science & Medicine, 212, 50–59. https://doi.org/10.1016/j.socscimed.2018.07.007CrossRefGoogle ScholarPubMed
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge.CrossRefGoogle Scholar
Converse, P. E. (1964). The nature of belief systems in mass publics. In Apter, D. A. (Ed.), Ideology and Discontent (pp. 206–261). New York: Free Press.Google Scholar
Cramer, K. J. (2016). The Politics of Resentment: Rural Consciousness in Wisconsin and the Rise of Scott Walker. University of Chicago Press.CrossRefGoogle Scholar
Erikson, R. S., Wright, J., Gerald, C. , & McIver, J. P. (1989). Political parties, public opinion, and state policy in the United States. American Political Science Review, 83(3), 729–750.CrossRefGoogle Scholar
Frewer, L. J., Miles, S., & Marsh, R. (2002). The media and genetically modified foods: Evidence in support of social amplification of risk. Risk Analysis, 22(4), 701–711. https://doi.org/10.1111/0272-4332.00062CrossRefGoogle ScholarPubMed
Gupta, C. (2018). Contested fields: an analysis of anti-GMO politics on Hawai’i Island. Agriculture and Human Values, 35(1), 181–192. https://doi.org/10.1007/s10460-017-9814-8CrossRefGoogle Scholar
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate Data Analysis (Vol. 5). Prentice Hall.Google Scholar
Hall, P. K. (2016, July 15). Congress finalizes mandatory GMO labeling law. National Agricultural Law Center. http://nationalaglawcenter.org/congress-finalizes-mandatory-gmo-labeling-law-2/Google Scholar
Hallman, W. K., Cuite, C. L., & Morin, X. K. (2013, November 1). Public perceptions of labeling genetically modified foods (Working Paper No. 2013-01). Rutgers School of Environmental Science. http://humeco.rutgers.edu/documents_PDF/news/GMlabelingperceptions.pdfGoogle Scholar
Hopkins, D. J., & King, G. (2010). A method of automated nonparametric content analysis for social science. American Journal of Political Science, 54(1), 229–247.CrossRefGoogle Scholar
Howell, E. L., Li, N., Akin, H., Scheufele, D. A., Xenos, M. A., & Brossard, D. (2017). How do U.S. state residents form opinions about “fracking” in social contexts? A multilevel analysis. Energy Policy, 106, 345–355. https://doi.org/10.1016/j.enpol.2017.04.003CrossRefGoogle Scholar
Howell, E. L., Wirz, C. D., Brossard, D., Jamieson, K. H., Scheufele, D. A., Winneg, K. M., & Xenos, M. A. (2018). National Academy of Sciences report on genetically engineered crops influences public discourse. Politics and the Life Sciences, 37(2), 250–261. https://doi.org/10.1017/pls.2018.12CrossRefGoogle Scholar
Jacoby, W. G., & Schneider, S. K. (2001). Variability in state policy priorities: An empirical analysis. Journal of Politics, 63(2), 544–568.CrossRefGoogle Scholar
Jennings, F. J. (2018). Where to turn? The influence of information source on belief and behavior. Journal of Risk Research, 22(7), 909–918. https://doi.org/10.1080/13669877.2017.1422788CrossRefGoogle Scholar
Kahan, D. M. (2015). Climate‐science communication and the measurement problem. Political Psychology, 36(S1), 1–43.CrossRefGoogle Scholar
Khan, R. (2013, June 11). Do liberals oppose genetically modified organisms more than conservatives? Discover. https://www.discovermagazine.com/the-sciences/do-liberals-oppose-genetically-modified-organisms-more-than-conservativesGoogle Scholar
Kondoh, K., & Jussaume, R. A. (2006). Contextualizing farmers’ attitudes towards genetically modified crops. Agriculture and Human Values, 23(3), 341–352. https://doi.org/10.1007/s10460-006-9004-6CrossRefGoogle Scholar
Li, N., Akin, H., Su, L. Y.-F., Brossard, D., Xenos, M. A., & Scheufele, D. A. (2016). Tweeting disaster: An analysis of online discourse about nuclear power in the wake of the Fukushima Daiichi nuclear accident. Journal of Communication, 15(5), 1–20.Google Scholar
Liddy, E. D. (2001). Natural language processing. In Drake, M. (Ed.), Encyclopedia of Library and Information Science (2nd ed.). Marcel Decker.Google Scholar
Lusk, J. L., Jamal, M., Kurlander, L., Roucan, M., & Taulman, L. (2005). A meta-analysis of genetically modified food valuation studies. Journal of Agricultural and Resource Economics, 30(1), 28–44.Google Scholar
Mazur, A. (1981). Media coverage and public opinion on scientific controversies. Journal of Communication, 31(2), 106–115.CrossRefGoogle Scholar
Mendes, K., Ringrose, J., & Keller, J. (2018). #MeToo and the promise and pitfalls of challenging rape culture through digital feminist activism. European Journal of Women’s Studies, 25(2), 236–246.CrossRefGoogle Scholar
Monnat, S. M., & Brown, D. L. (2017). More than a rural revolt: Landscapes of despair and the 2016 presidential election. Journal of Rural Studies, 55, 227–236.CrossRefGoogle ScholarPubMed
Napier, T., Tucker, M., Henry, C., & Whaley, S. (2004). Consumer attitudes toward GMOs: The Ohio experience. Journal of Food Science, 69(3), CRH69–CRH76.Google Scholar
National Academies of Science, Engineering, and Medicine (NASEM). (2016). Genetically Engineered Crops: Experiences and Prospects. Washington, DC: National Academies Press.Google Scholar
Nisbet, M. C., & Huge, M. (2006). Attention cycles and frames in the plant biotechnology debate: Managing power and participation through the press/policy connection. Harvard International Journal of Press/Politics, 11(2), 3–40.CrossRefGoogle Scholar
Price, V., David, C., Goldthorpe, B., Roth, M. M., & Cappella, J. N. (2006). Locating the issue public: The multi-dimensional nature of engagement with health care reform. Political Behavior, 28(1), 33–63.CrossRefGoogle Scholar
Rose, K. M., Brossard, D., & Scheufele, D. A. (2020). Of society, nature, and health: How perceptions of specific risks and benefits of genetically engineered foods shape public rejection. Environmental Communication. Advance online publication. https://doi.org10.1080/17524032.2019.1710227CrossRefGoogle Scholar
Rose, K. M., Howell, E. L., Su, L. Y. F., Xenos, M. A., Brossard, D., & Scheufele, D. A. (2019). Distinguishing scientific knowledge: The impact of different measures of knowledge on genetically modified food attitudes. Public Understanding of Science, 28(4), 449–467. https://doi.org/10.1177/0963662518824837CrossRefGoogle ScholarPubMed
Runge, K. K., Brossard, D., Scheufele, D. A., Rose, K. M., & Larson, B. J. (2017). Attitudes about food and food-related biotechnology. Public Opinion Quarterly, 81(2), 577–596.CrossRefGoogle Scholar
Runge, K. K., Yeo, S. K., Cacciatore, M., Scheufele, D. A., Brossard, D., Xenos, M., . . . Su, L. Y.-F. (2013). Tweeting nano: How public discourses about nanotechnology develop in social media environments. Journal of Nanoparticle Research, 15(1). https://doi.org/10.1007/s11051-012-1381-8CrossRefGoogle Scholar
Scala, D. J., & Johnson, K. M. (2017). Political polarization along the rural-urban continuum? The geography of the presidential vote, 2000–2016. Annals of the American Academy of Political and Social Science, 672(1), 162–184.CrossRefGoogle Scholar
Scheufele, D. A. (2007). Opinion climates, spirals of silence and biotechnology: Public opinion as a heuristic for scientifc decision-making. In Brossard, D., Shanahan, J., & Nesbitt, T. C. (Eds.), The Public, the Media, and Agricultural Biotechnology (pp. 231–244). Oxford University Press.CrossRefGoogle Scholar
Scott, S. E., Inbar, Y., & Rozin, P. (2016). Evidence for absolute moral opposition to genetically modified food in the United States. Perspectives on Psychological Science, 11(3), 315–324.CrossRefGoogle ScholarPubMed
Scott, S. E., Inbar, Y., Wirz, C. D., Brossard, D., & Rozin, P. (2018). An overview of attitudes to genetically engineered foods. Annual Review of Nutrition, 38, 459–479. https://doi.org/10.1146/annurev-nutr-071715-051223CrossRefGoogle Scholar
Shanahan, J., Scheufele, D., & Lee, E. (2001). The polls-trends: Attitudes about agricultural biotechnology and genetically modified organisms. Public Opinion Quarterly, 65(2), 267–281.CrossRefGoogle ScholarPubMed
Simis-Wilkinson, M., Madden, H., Lassen, D., Su, L. Y.-F., Brossard, D., Scheufele, D. A., & Xenos, M. A. (2018). Scientists joking on social media: An empirical analysis of #overlyhonestmethods. Science Communication, 40(3), 314–339. https://doi.org/10.1177/1075547018766557CrossRefGoogle Scholar
Su, L. Y.-F., Cacciatore, M. A., Liang, X., Brossard, D., Scheufele, D. A., & Xenos, M. A. (2017). Analyzing public sentiments online: Combining human-and computer-based content analysis. Information, Communication & Society, 20(3), 406–427.CrossRefGoogle Scholar
Taylor, K.-Y. (2016). From# BlackLivesMatter to Black Liberation: Haymarket Books.Google Scholar
U.S. Census Bureau. (2015). 2015 American Community Survey. https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtmlGoogle Scholar
U.S. Department of Agriculture (USDA) Economic Research Service. (2015). County typology codes. https://www.ers.usda.gov/data-products/county-typology-codes.aspxGoogle Scholar
U.S. Department of Agriculture (USDA) Economic Research Service. (2016). Rural-urban continuum codes. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx#.UYJuVEpZRvYGoogle Scholar
Wang, W., & Guo, L. (2018). Framing genetically modified mosquitoes in the online news and Twitter: Intermedia frame setting in the issue-attention cycle. Public Understanding of Science, 27(8), 937–951. https://doi.org/10.1177/0963662518799564CrossRefGoogle ScholarPubMed
Webb, A. R. (2003). Statistical Pattern Recognition. John Wiley & Sons.Google Scholar
Wirz, C. D., Xenos, M. A., Brossard, D., Scheufele, D., Chung, J. H., & Massarani, L. (2018). Rethinking social amplification of risk: Social media and Zika in three languages. Risk Analysis, 38(12), 2599–2624. https://doi.org/10.1111/risa.13228CrossRefGoogle ScholarPubMed
Wojcik, S., & Hughes, A. (2019, April 24). Sizing Up Twitter Users. Pew Research Center. https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/Google Scholar
Yeo, S. K., Liang, X., Brossard, D., Rose, K. M., Korzekwa, K., Scheufele, D. A., & Xenos, M. A. (2017). The case of #arseniclife: Blogs and Twitter in informal peer review. Public Understanding of Science, 26(8), 937–952. https://doi.org/10.1177/0963662516649806CrossRefGoogle ScholarPubMed