| Popis: |
This thesis examines left- and right- leaning users on Australian Twitter in an effort to understand the language use of the different parties to online climate change discourse. The data are taken from Australian Twitter users between 2020 and 2022, and split up via a political affiliation metric in order to create two distinct politically-opposed user groups. Three main techniques are used to identify linguistic differences between the two groups: sentiment analysis, multiple correspondence analysis, and keyword analysis. The findings of this thesis are threefold. Firstly, text data collected on left- and right-leaning metrics are found to be an apt proxy for examining the language of climate change activism and denial. Secondly, climate change activists and deniers on Australian social media speak similarly in terms of grammatical style, but significantly differently in terms of lexical content. Thirdly and finally, triangulating between the three aforementioned methods provides a much clearer picture of language use. In this way, this thesis offers methodological innovations in examining online discourses, as well as important findings on the language use of the various parties to climate discourses on Australian Twitter. |
