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Online misinformation during extreme weather emergencies: Short-term information hazard or long-term influence on climate change perceptions?
Daume, S. 2024. Online misinformation during extreme weather emergencies: Short-term information hazard or long-term influence on climate change perceptions?. Environmental Research Communications 6(2):022001.
Extreme weather events linked to climate change are becoming more frequent. The online public discourse on and during these events, especially on social media, attracts misinformation that can undermine short-term emergency responses, but can also be aimed at influencing long-term public perceptions of climate change. This contribution reviews existing research on online misinformation with the aim to understand the types, origins, and potential impacts of...
Mapping the automation of Twitter communications on climate change, sustainability, and environmental crises — a review of current research.
Daume, S., P. Bjersér, and V. Galaz. 2023. Mapping the automation of Twitter communications on climate change, sustainability, and environmental crises — a review of current research.. Current Opinion in Environmental Sustainability 65:101384.
Online social media such as the microblog Twitter are key digital arenas shaping the public discourse on important societal topics. Automated social media accounts, so-called ‘social bots,’ have emerged as a controversial phenomenon, proven to both disrupt and support online communications on topics such as political elections and public health. To what extent social bots also impact online conversations on climate change, environmental crises, and...
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...