Machine Learning Reveals How Personalized Climate Communication Can Both Succeed and Backfire

Abstract

Different advertising messages work for different people. Machine learning can be an effective way to personalise climate communications. In this paper, we use machine learning to reanalyse findings from a recent study, showing that online advertisements increased climate change belief in some people while resulting in decreased belief in others. In particular, we show that the effect of the advertisements could change depending on a person's age and ethnicity. Our findings have broad methodological and practical applications.

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Cite

Text

Harinen et al. "Machine Learning Reveals How Personalized Climate Communication Can Both Succeed and Backfire." NeurIPS 2022 Workshops: CML4Impact, 2022.

Markdown

[Harinen et al. "Machine Learning Reveals How Personalized Climate Communication Can Both Succeed and Backfire." NeurIPS 2022 Workshops: CML4Impact, 2022.](https://mlanthology.org/neuripsw/2022/harinen2022neuripsw-machine/)

BibTeX

@inproceedings{harinen2022neuripsw-machine,
  title     = {{Machine Learning Reveals How Personalized Climate Communication Can Both Succeed and Backfire}},
  author    = {Harinen, Totte and Filipowicz, Alexandre and Hakimi, Shabnam and Iliev, Rumen and Klenk, Matthew and Sumner, Emily Sarah},
  booktitle = {NeurIPS 2022 Workshops: CML4Impact},
  year      = {2022},
  url       = {https://mlanthology.org/neuripsw/2022/harinen2022neuripsw-machine/}
}