Automated framing of climate change? The role of social bots in the Twitter climate change discourse during the 2019/2020 Australia bushfires

Daume, S., V. Galaz, and P. Bjersér. 2023. Automated framing of climate change? The role of social bots in the Twitter climate change discourse during the 2019/2020 Australia bushfires. Social Media + Society 9(2).

Extreme weather-related events like wildfires have been increasing in frequency and severity due to climate change. Public online conversations that reflect on these events as climate emergencies can create awareness and build support for climate action but are also used to spread misinformation and climate change denial. To what extent automated social media accounts—“social bots”—amplify different perspectives of such events and influence climate change discourses,...

Climate misinformation in a climate of misinformation.

Galaz, V., H. Metzler, S. Daume, A. Olsson, B. Lindström, A. Markström. 2023. Climate misinformation in a climate of misinformation.. Online research brief. Stockholm Resilience Centre (Stockholm University) and Beijer Institute of Ecological Economics (Royal Swedish Academy of Sciences) .

Foundations for behavioral change

Lindahl, T., C. Schill, D. Collste, A.-S. Crépin, C. Folke, and V. Galaz. 2022. Foundations for behavioral change. In: Galaz, V. and D. Collste (eds.) Economy and Finance for a Just Future on a Thriving Planet. Report for Stockholm+50. Beijer Institute of Ecological Economics (Royal Swedish Academy of Sciences) and the Stock­holm Resilience Centre (Stockholm University), Chapter 6.

Transforming societies towards sustainability requires that individuals, groups and the private and societal sectors alike, change their behaviours. Since behaviour is, to a large extent, guided by social norms, a change of norms has the potential to ignite the necessary large-scale behavioural shifts.

Data mining and pattern recognition

Rocha, J.C. and S. Daume. 2021. Data mining and pattern recognition. In: Biggs, R., A. De Vos, R. Preiser, H. Clements, K. Maciejewski, and M. Schlüter. The Routledge Handbook of Re­search Methods for Social-Ecological Systems. Routledge, London, UK. Pp. 24-251.

Data Mining and Pattern Recognition

Rocha, C.J., and S. Daume. 2021. Data Mining and Pattern Recognition. In: Biggs, R., de Vos, A., Preiser, R., Clements, H., Maciejewski, K. and M. Schlüter (Eds.). The Routledge Handbook of Research Methods for Social-Ecological Systems. Routledge, London, UK. Pp. Chapter 17.

Chapter 17 deals with data mining and pattern recognition, which are methods in data science. A general purpose of data science is pattern discovery from unstructured and heterogeneous sources of data through data mining and machine learning. The chapter discusses data wrangling, clustering analysis, regression trees, neural networks, sentiment analysis and topic models. It goes on to discuss the types of social-ecological systems (SES) problems...