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Heated conversations in a warming world: affective polarization in online climate change discourse follows real-world climate anomalies

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Abstract

Climate change research describes online discourse as sharply polarized, echoing real-world divides in society. Yet while polarization in online climate change discourse has been extensively studied in terms of isolated communities and echo chambers, less is known about the extent of affective polarization that characterizes the hostile nature of intergroup interactions. Utilizing a combination of machine learning and network science tools, we design a methodological pipeline that quantifies the extent to which stance groups interact with more negative sentiment toward out-group members than in-group members. We apply this framework to 100 weeks of Twitter discourse about climate change. We find that deniers of climate change (Disbelievers) are more hostile towards people who believe (Believers) in the anthropogenic cause of climate change than vice versa. We also observe that Disbelievers use more words and hashtags related to natural disasters during more affectively polarized weeks as compared to Believers. Finally, using vector autoregression analysis, we find that climate anomalies in terms of both severe temperature and storms predict asymmetric shifts in online climate change discourse: Disbelievers grow more hostile toward out-groups, while Believers become less affectively polarized. These findings resonate with prior work on the asymmetric nature of polarization in contentious discourse, both around climate change and beyond. Our work also extends existing findings around temporal associations between climate anomalies, divided media representations, and real-world conflicts. We conclude with implications for science communication and studying affective polarization in online discourse, especially concerning the subject of climate change.

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Notes

  1. https://developer.Twitter.com/en/docs/tweets/search/overview/standard.

  2. We randomly sampled 1000 users from each group to manually validate the results. We label a user as Disbeliever if we find any Tweet akin to someone who does not believe in climate change or anthropogenic cause of climate change. Otherwise, we label the user as Believer. We observe that the average precision from manual validation of 2000 users is 81.2%. We use the parameter values as defined in Kumar (2020) as \(k = 5000, p = 5000, \theta (I) = 0.1, \theta (U) = 0.0, \theta (T) = 0.7\).

  3. http://infolab.stanford.edu/pub/cstr/reports/cs/tr/99/1620/CS-TR-99-1620.ch4.pdf.

  4. https://www.ncdc.noaa.gov/cdo-web/.

  5. https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php.

  6. https://www.ncdc.noaa.gov/stormevents/ftp.jsp.

  7. https://en.wikipedia.org/wiki/Hurricane_Maria.

  8. https://en.wikipedia.org/wiki/2017_Las_Vegas_shooting.

  9. For more information, please refer to (Heider 2013).

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Authors and Affiliations

  1. CASOS Center, Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA

    Aman Tyagi

  2. CASOS Center, Institute for Software Research, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Joshua Uyheng & Kathleen M. Carley

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  1. Aman Tyagi
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  2. Joshua Uyheng
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  3. Kathleen M. Carley
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Correspondence to Aman Tyagi.

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This work was supported in part by the Knight Foundation and the Office of Naval Research Grants N000141812106 and N000141812108. Additional support was provided by the Center for Computational Analysis of Social and Organizational Systems (CASOS), the Center for Informed Democracy and Social Cybersecurity (IDeaS), and the Department of Engineering and Public Policy of Carnegie Mellon University. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Knight Foundation, Office of Naval Research or the US government.

Appendix

Appendix

List of natural disaster related words used in the analysis: avalanche, blizzard, bushfire, cataclysm, cloud, cumulonimbus, cyclone, disaster, drought, duststorm, earthquake, erosion, fire, flood, forestfire, gale, gust, hail, hailstorm, heatwave, high-pressure, hurricane, lava, lightning, low-pressure, magma, naturaldisasters, nimbus, permafrost, rainstorm, sandstorm, seismic, snowstorm, storm, thunderstorm, tornado, tremor, tsunami, twister, violentstorm, volcano, whirlpool whirlwind, windstorm.

1.1 Regression tables

See Tables 1 and 2.

Table 1 Estimation results for believers as discussed in Fig. 3 (left)
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Table 2 Estimation results for disbelievers as discussed in Fig. 3 (right)
Full size table

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Tyagi, A., Uyheng, J. & Carley, K.M. Heated conversations in a warming world: affective polarization in online climate change discourse follows real-world climate anomalies. Soc. Netw. Anal. Min. 11, 87 (2021). https://doi.org/10.1007/s13278-021-00792-6

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