Applications of social data science to environmental communication and activism on social media

My proposed doctoral research is the development of a novel social data science model and framework for the purpose of studying and explaining the uniquely challenging dynamics of online climate change communication across cultural contexts. The model will leverage deep learning and network analysis paradigms to identify and characterise climate change discourse in various geographic regions, using qualitative analysis to control for unique cultural, political,
and social features of different contexts. The ultimate objective is to use this information to predict the most effective strategies for disseminating climate change content online given a region's unique contextual attributes. For the purposes of diversity and generalisability, the countries of focus include those that have been most active in generating climate change content and legislative action, along with those that have been most directly affected by the
consequences of climate change. Thus far, most computational approaches to studying climate change communication online have been limited to descriptive analysis of the polarisation and segregation of the discourse; our new paradigm seeks to go beyond this by developing a tool and general research framework that will enable greater explanatory and predictive analysis of the issue, as well as other similarly sophisticated and context-dependent online social
phenomena.
The impact of this research is two-fold. First, it will contribute to the technical toolbox of data science research by providing an open source model for other researchers to use and augment. This work will pioneer an advanced interdisciplinary approach to ethical and culturally-inclusive computational social science. It will also prove social data science as a truly symbiotic combination of social and data science and not a distortion of social science questions to fit data science methods.