Abstract
When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics/researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.
Highlights
When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on
Data collection An online survey instrument, based on SenseMaker (Cognitive_Edge, http://cognitive-edge.com/sensemaker/), was designed and tested by the research team and implemented on three separate occasions between June 2010 and April 2011: the first elicited responses from people attending an international scientific symposium on climate change (CC) held in Australia (CCC, n = 193); the second elicited responses from individuals working in an Australian state government department with a mandate to work on CC (AUS_GOVT, n = 121); and the third elicited responses from individuals working on CC in Canada and from a panel of residents living along the eastern seaboard of Australia (AUS_CAN, n = 627)
Fitting a topic model creates two important matrices used in the analyses presented here: the first, theta, comprises the proportion of each document assigned to each topic
Summary
When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on. In recent years people across the globe have been faced with making sense of climate change (CC; Wolf and Moser 2011, Moloney et al 2014). The last decade has witnessed a number of researchers applying SRT to climate change with most of this work documenting social representations (SRs) using the following data sources: news media reporting (Carvalho 2010, Jaspal and Nerlich 2014, Jaspal et al 2016), surveys or focus group discussions with members of the general public (Cabecinhas et al 2008, Reusswig and MeyerOhlendorf 2010, Olausson 2011, Smith and Joffe 2013, GómezMartín and Armesto-López 2014, Moloney et al 2014, Wibeck 2014, Baquiano and Mendez 2016), and both surveys and media analyses (Shrestha et al 2014). Emerging from this work has been an expanding and deepening understanding, among researchers, of how different social groups represent climate change
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