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When do extreme weather events generate attention to climate change?


We analyzed the effects of 10,748 weather events on attention to climate change between December 2011 and November 2014 in local areas across the USA. Attention was gauged by quantifying the relative increase in Twitter messages about climate change in the local area around the time of each event. Coastal floods, droughts, wildfires, strong wind, hail, excessive heat, extreme cold, and heavy snow events all had detectable effects. Attention was reliably higher directly after events began, compared to directly before. Financial damage associated with the weather events had a positive and significant effect on attention, although the effect was small. The abnormality of each weather event’s occurrence compared to local historical activity was also a significant predictor. In particular and in line with past research, relative abnormalities in temperature (local warming) generated attention to climate change. In contrast, wind speed was predictive of attention to climate change in absolute levels. These results can be useful to predict short-term attention to climate change for strategic climate communications and to better forecast long-term climate policy support.

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  1. As a robustness check, we re-ran the analysis excluding all re-tweets. This produced results almost identical to the analysis of all tweets and re-tweets.

  2. Investigating the differences between trends in messages that mention “global warming” versus messages that mention “climate change” is outside the scope of this paper. However, we did re-run the mixed-effects regression using only climate change messages and then only global warming messages. These results can be seen in Appendix I.

  3. (last accessed on March 10, 2017)

  4. More information on the NCDC Storm Events Database can be found here: (last accessed on March 10, 2017).

  5. The instructions for weather event reporters including detailed definitions of each event type can be found here: (last accessed on March 10, 2017). See Appendix M for additional information on weather event definitions.

  6. Descriptive statistics on message counts for all event types are shown in Appendix K.

  7. (last accessed on March 10, 2017)

  8. (last accessed on March 10, 2017)

  9. When the events with a raw abnormality score of <1 (indicating negative abnormality) are removed from the analysis, the effect of abnormality is essentially unchanged.

  10. A visualization of the transformation from the raw abnormality scores to the final abnormality scores can be seen in Appendix D.

  11. The algorithm for this matching procedure can be found in the electronic supplementary material “control_matching_algorithm_ESM2.”

  12. We also ran this analysis with the attention before as the dependent variable. These results are included in Appendix G.

  13. We also evaluated a model excluding the outliers from the regression instead of winsorizing them, which produced almost identical results as the winsorized regression.

  14. The weather event “co-occurrence” matrix can be seen in Appendix J.

  15. See Appendix L for an analysis of the mix of political ideologies in our sample.


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The research leading to these results received funding from the European Research Council under the European Community’s Programme “Ideas”—Call identifier: ERC-2013-StG/ERC grant agreement no. 336703—project RISICO “Risk and uncertainty in developing and implementing climate change policies.” Funding was also provided under the cooperative agreement NSF SES-0951516 awarded to the Center for Research on Environmental Decisions. Funding and training for the first author was also provided by the From Data to Solutions IGERT program NSF-1144854. We thank Andrei Kirilenko for helping us access additional public Twitter messages in our time range.

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Correspondence to Matthew R. Sisco.

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Sisco, M.R., Bosetti, V. & Weber, E.U. When do extreme weather events generate attention to climate change?. Climatic Change 143, 227–241 (2017).

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