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Social ties and concern for global warming

Abstract

Recent research focusing on social factors affecting risk perceptions has suggested that social networks might help to explain why differences of opinion about climate change persist across segments of the lay public despite the scientific consensus. Even though concern for global warming in itself might seem irrelevant for most social ties, we show that it is significant enough to be reflected in the structure of social networks. To do this, we programmed a Facebook application that collected survey data on concerns and network data on friendships. We found that respondents tend to have friends with similar concerns as their own, the unconcerned respondents have fewer friends, and any two respondents who disagreed about the seriousness of global warming were less than half as likely to be friends. The results indicate that the structure of the social network may hinder changes in opinions, explaining why opinions persist despite the scientific consensus. The results suggest that the communication of climate science could be improved by strategies that aim to overcome these network effects.

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Notes

  1. 1.

    The employment question was adapted from the World Value Survey (WVS 2009) allowing the respondent to choose one from a list of options.

  2. 2.

    The question asking the respondent to choose a position on a left–right scale was adapted from the World Value Survey (WVS 2009), to enable comparison.

  3. 3.

    The question was adapted from the World Value Survey (WVS 2009), to enable comparison with a representative sample of the Finnish population.

  4. 4.

    We found a small but significant negative correlation between the respondent’s distance from the friends’ average and the number of friends (ρ = −0.053, t = −3.72, degrees of freedom 4825, p-value <0.001). This means that people who deviate from the average opinion of their friends tend to have fewer friends. The friends’ average was estimated by the average among friends who participated in the survey.

  5. 5.

    We tested each model for possible non-linearity with respect to concern for global warming with nested models, but none resulted in a significantly better fit according to F-tests.

  6. 6.

    This can be calculated by multiplying the regression coefficient for concern with the distance between ends of the scale: 3×17.232≈52.

  7. 7.

    Note that the local clustering coefficient needs to be estimated, as we observed a tie between the respondent’s two friends only if at least one of them had participated (see Appendix).

  8. 8.

    We added a separate dummy variable for both being men or both being women.

  9. 9.

    The marginal effects were calculated for two people of the same age and opposite sex.

  10. 10.

    Full regression tables are included in the Appendix.

References

  1. Allcott H (2011) Social norms and energy conservation. J Public Econ 95(910):1082–1095

    Article  Google Scholar 

  2. Anderegg WRL, Prall JW, Harold J, Schneider SH (2010) Expert credibility in climate change. Proc Natl Acad Sci 107(27):12,107–12109

  3. Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc Natl Acad Sci 106(51):21,544–9

  4. Bain PG, Hornsey MJ, Bongiorno R, Jeffries C (2012) Promoting pro-environmental action in climate change deniers. Nat Clim Chang 2(8):600–603

    Article  Google Scholar 

  5. Banerjee A, Chandrasekhar AG, Duflo E, Jackson MO (2013) The diffusion of microfinance. Science New York NY 341 6144 1236 498

  6. Barabási AL, Albert R (1999) Emergence of Scaling in Random Networks. Sci 286(5439):509–512

    Article  Google Scholar 

  7. Bikhchandani S, Hirshleifer D, Welch I (1992) A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades. J Polit Econ 100(5):992–1026

    Article  Google Scholar 

  8. Bond R, Fariss C, Jones J, Kramer A (2012) A 61-million-person experiment in social influence and political mobilization. Nat 489(7415):295–298

    Article  Google Scholar 

  9. Borgatti SP (2005) Centrality and network flow. Soc Networks 27(1):55–71

    Article  Google Scholar 

  10. Bowman TE, Maibach E, Mann ME, Moser SC, Somerville RCJ (2009) Creating a common climate language. Science 324(5923):36–37

    Article  Google Scholar 

  11. Boykoff MT, Boykoff JM (2004) Balance as bias: global warming and the US prestige press. Glob Environ Chang 14(2):125–136

    Article  Google Scholar 

  12. Boykoff MT, Boykoff JM (2007) Climate change and journalistic norms: A case-study of US mass-media coverage. Geoforum 38(6):1190–1204

    Article  Google Scholar 

  13. Boykoff MT, Yulsman T (2013) Political economy, media, and climate change: sinews of modern life Wiley Interdisciplinary Reviews. Clim Chang 4(5):359–371

    Google Scholar 

  14. Brulle RJ, Carmichael J, Jenkins JC (2012) Shifting public opinion on climate change: an empirical assessment of factors influencing concern over climate change in the U.S., 2002-2010. Clim Chang 114(2):169–188

    Article  Google Scholar 

  15. Burt RS (2000) Decay functions. Soc Networks 22(1):1–28

    Article  Google Scholar 

  16. Carvalho A (2007) Ideological cultures and media discourses on scientific knowledge: Re-reading news on climate change. Public Underst Sci 16(2):223–243

    Google Scholar 

  17. Centola D (2011) An experimental study of homophily in the adoption of health behavior. Science 334(1269):1269–1272

    Article  Google Scholar 

  18. Contractor NS, DeChurch LA (2014) Integrating social networks and human social motives to achieve social influence at scale. Proc Natl Acad Sci 111(Supplement 4):13,650–13,657

    Article  Google Scholar 

  19. Cook J, Nuccitelli D, Green SA, Richardson M, Winkler B, Painting R, Way R, Skuce PJA (2013) Quantifying the consensus on anthropogenic global warming in the scientific literature, vol 8, pp 24–024

  20. Currarini S, Jackson MO, Pin P (2009) An Economic Model of Friendship: Homophily, Minorities, and Segregation. Econometrica 77(4):1003–1045

    Article  Google Scholar 

  21. Currarini S, Jackson MO, Pin P (2010) Identifying the roles of race-based choice and chance in high school friendship network formation. In: Proceedings of the National Academy of Sciences 107 11 4857 4861

  22. Dunlap RE, McCright AM (2008) A Widening Gap: Republican and Democratic Views on Climate Change Environment. Sci Policy Sustain Dev 50(5):26–35

    Article  Google Scholar 

  23. Ellison NB, Steinfield C, Lampe C (2007) The Benefits of Facebook Friends: Social Capital and College Students Use of Online Social Network Sites. J Computer-Mediated Commun 12(4):1143–1168

    Article  Google Scholar 

  24. Golub B, Jackson M (2012) How Homophily Affects the Speed of Learning and Best-Response Dynamics. Q J Econ 127(3):1287–1338

    Article  Google Scholar 

  25. Granovetter MS (1973) The Strength of Weak Ties. Am J Sociol 78(6):1360–1380

    Article  Google Scholar 

  26. Hampton K, Goulet L, Rainie L, Purcell K (2011) Social networking sites and our lives Pew internet American life project Pew Research Center

  27. Hine DW, Reser JP, Morrison M, Phillips WJ, Nunn P, Cooksey R (2014) Audience segmentation and climate change communication: conceptual and methodological considerations. Wiley Interdisciplinary Reviews: Climate Change pp n/a–n/a

  28. Hoffman AJ (2011) Sociology: The growing climate divide. Nat Clim Chang 1(4):195–196

    Article  Google Scholar 

  29. Irwin A, Wynne B (1996) Misunderstanding science? The public reconstruction of science and technology. Cambridge University Press

  30. Jackson M (2012) Social capital and social quilts: Network patterns of favor exchange. Am Econ Rev 10(5):1857–1897

    Article  Google Scholar 

  31. Jackson M, Rogers B (2007) Meeting strangers and friends of friends. Am Econ Rev 97(3)

  32. Kahan D (2010) Fixing the communications failure. Nature 463(7279):296–297

    Article  Google Scholar 

  33. Kahan DM, Peters E, Wittlin M, Slovic P, Ouellette LL, Braman D, Mandel G (2012) The polarizing impact of science literacy and numeracy on perceived climate change risks. Nat Clim Chang 2(10):732–735

    Article  Google Scholar 

  34. Kasperson RE, Renn O, Slovic P, Brown HS, Emel J, Goble R, Kasperson JX, Ratick S (1988) The Social Amplification of Risk: A Conceptual Framework. Risk Anal 8(2):177–187

    Article  Google Scholar 

  35. Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893

    Article  Google Scholar 

  36. Kossinets G, Watts DJ (2009) Origins of homophily in an evolving social network. Am J Sociol 115(2):405–450

    Article  Google Scholar 

  37. van der Leij MJ (2011) Experimenting with Buddies. Science 334(6060):1220–1221

    Article  Google Scholar 

  38. Leviston Z, Walker I, Morwinski S (2013) Your opinion on climate change might not be as common as you think. Nat Clim Chang 3(4):334–337

    Article  Google Scholar 

  39. Lewis K, Kaufman J, Gonzalez M, Wimmer A, Christakis N (2008) Tastes, ties, and time: A new social network dataset using Facebook.com. Soc Networks 30(4):330–342

    Article  Google Scholar 

  40. Markowitz EM, Shariff AF (2012) Climate change and moral judgement. Nat Clim Chang 2(4):243–247

    Article  Google Scholar 

  41. Marx SM, Weber EU, Orlove BS, Leiserowitz A, Krantz DH, Roncoli C, Phillips J (2007) Communication and mental processes: Experiential and analytic processing of uncertain climate information. Glob Environ Chang 17:47–58

    Article  Google Scholar 

  42. McCright AM, Dunlap RE (2011) The politicization of climate change and polarization in the American public’s views of global warming, 20012010. Sociol Q 52(2):155–194

    Article  Google Scholar 

  43. Mcpherson M, Smith-Lovin L, Cook JM (2001) Birds of a Feather: Homophily in Social Networks. Annu Rev Sociol 27:415–444

    Article  Google Scholar 

  44. Milkman KL, Berger J (2014) The science of sharing and the sharing of science. In: Proceedings of the National Academy of Sciences of the United States of America 111(Supplement 4):13,642–9

  45. Moser SC (2010) Communicating climate change: history, challenges, process and future directions Wiley Interdisciplinary Reviews. Clim Chang 1(1):31–53

    Google Scholar 

  46. Newman MEJ (2003) Mixing patterns in networks. Phys Rev E 67(2):26–126

    Article  Google Scholar 

  47. Onnela J P, Saramäki J, Hyvönen J, Szabó G, Lazer D, Kaski K, Kertész J, a L Barabási AL (2007) Structure and tie strengths in mobile communication networks. In: Proceedings of the National Academy of Sciences 104(18):7332–7336

  48. OSF (2014) Official Statistics of Finland: Use of information and communications technology by individuals. Tech.rep., Statistics Finland, Helsinki

  49. Pidgeon N, Fischhoff B (2011) The role of social and decision sciences in communicating uncertain climate risks. Nat Clim Chang 1(1):35–41

    Article  Google Scholar 

  50. Rainie L, Smith A (2012) Social networking sites and politics, Pew internet American life project, Pew Research Center

  51. Schultz PW, Nolan JM, Cialdini RB, Goldstein NJ, Griskevicius V (2007) The Constructive, Destructive, and Reconstructive Power of Social Norms. Psychol Sci 18(5):429–434

    Article  Google Scholar 

  52. Schwartz SH, Melech G, Lehmann A, Burgess S, Harris M, Owens V (2001) Extending the Cross-Cultural Validity of the Theory of Basic Human Values with a Different Method of Measurement. J Cross-Cult Psychol 32(5):519–542

    Article  Google Scholar 

  53. Senbel M, Ngo VD, Blair E (2014) Social mobilization of climate change: University students conserving energy through multiple pathways for peer engagement. J Environ Psychol 38:84–93

    Article  Google Scholar 

  54. Shalizi CR, Thomas AC (2011) Homophily and Contagion Are Generically Confounded in Observational Social Network Studies. Sociol Methods Res 40(2):211–239

    Article  Google Scholar 

  55. Sturgis P, Allum N (2004) Science in Society: Re-Evaluating the Deficit Model of Public Attitudes. Public Underst Sci 13(1):55–74

    Article  Google Scholar 

  56. Sunstein CR (2006) The Availability Heuristic, Intuitive Cost-Benefit Analysis, and Climate Change. Clim Chang 77(1-2):195–210

    Article  Google Scholar 

  57. Watts DJ (2002) A simple model of global cascades on random networks. In: Proceedings of the National Academy of Sciences 99(9):5766–5771

  58. Watts DJ, Strogatz SH (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):440–442

    Article  Google Scholar 

  59. Weber EU (2006) Experience-Based and Description-Based Perceptions of Long-Term Risk: Why Global Warming does not Scare us Yet. Clim Chang 77(1-2):103–120

    Article  Google Scholar 

  60. Wilson RE, Gosling SD, Graham LT (2012) A Review of Facebook Research in the Social Sciences. Perspect Psychol Sci 7(3):203–220

    Article  Google Scholar 

  61. Wimmer A, Lewis K (2010) Beyond and Below Racial Homophily: ERG Models of a Friendship Network Documented on Facebook. Am J Sociol 116(2):583–642

    Article  Google Scholar 

  62. Wolf J, Moser SC (2011) Individual understandings, perceptions, and engagement with climate change: insights from in-depth studies across the world, Wiley Interdisciplinary Reviews. Clim Chang 2(4):547–569

    Google Scholar 

  63. Wood BD, Vedlitz A (2007) Issue Definition, Information Processing, and the Politics of Global Warming. Am J Polit Sci 51(3):552–568

    Article  Google Scholar 

  64. WVS (2009) World Values Survey 2005 official data file v.20090901. World Values Survey Association

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Acknowledgments

This research was conducted while working at the University of Helsinki and made possible by the funding from the Graduate School fellowship of the Finnish Doctoral Programme in Economics. The Facebook application and survey was designed and carried out together with Panu Poutvaara. The author also wishes to thank Jan-Erik Lönnqvist and Markku Verkasalo for help with the survey design and Pekka Ilmakunnas, Heikki Kauppi, Anssi Kohonen, Otto Kässi, Jaakko Nelimarkka, Aino Silvo, Essi Eerola and Markku Ollikainen for valuable comments on the paper. The raffle prices were funded by Ifo Institute and Koneen Säätiö.

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Correspondence to Juha V. A. Itkonen.

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Appendices

Appendix A: Local clustering coefficient

We did not observe the local clustering coefficient directly, but we could estimate it for those who have friends in the sample. Consider the full undirected Facebook network (N, G), where N is the set of nodes and \(G \subset \{(i,j) \ \colon i,j\in N,\ i\neq j \}\) is the set of friendships.

Let N i = {jN :(i, j) ∈ G} be node i’s set of friends and d i the number of friends. The number of pairs of friends is p i = d i (d i −1)/2. Let c i = |{(j, k) ∈ G :j, kN i }|/2 be the number of friendships between the members of N i . Now the true local clustering coefficient is

$$t_{i}= \frac{c_{i}}{p_{i}}.$$

Let \(\tilde {N} \subset N\) be the set of nodes we sampled. Let \(\tilde {G} = \{(i,j) \in G \ \colon i\in \tilde {N} \text { or } j\in \tilde {N} \} \subset G\) be the set of friendships we sampled. Now, for each \(i\in \tilde {N}\) we observed N i .

Let \(\tilde {d_{i}}= |N_{i} \cap \tilde {N} |\) be the number of i’s friends in the sample. We observed all pairs except the ones where both nodes were outside of the sample. The number of pairs we observed was then the number of all pairs less the unobserved pairs, which we denote by \(\tilde {p}_{i} = d_{i}(d_{i}-1)/2 - (d_{i}-\tilde {d}_{i})(d_{i}-\tilde {d}_{i}-1)/2= d_{i}\tilde {d}_{i} - (\tilde {d}_{i}^{2} +\tilde {d}_{i})/2.\)

Let \( \tilde {c}_{i} = | \{ (j,k)\in G\ \colon j,k\in N_{i} \text { and } (j \in \tilde {N} \text { or } k \in \tilde {N}) \} |/2\) be the number of friendships between the members of N i that we observed.

The estimated clustering coefficient is then

$$\tilde{t}_{i}= \frac{\tilde{c_{i}}}{\tilde{p_{i}}}. $$

This is what we could observe and use for our analysis.

Appendix B: Full regression tables

Here we report in full the regression tables of the main text. Tables 4 and 5 present all coefficient estimates of Tables 2 and 3, respectively.

Table 4 Regression analysis of number of friends and local clustering coefficient
Table 5 Logistic regression on the probability of friendship

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Itkonen, J.V.A. Social ties and concern for global warming. Climatic Change 132, 173–192 (2015). https://doi.org/10.1007/s10584-015-1424-0

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Keywords

  • Climate change
  • Social networks
  • Science communication