IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i5p1509-d145554.html
   My bibliography  Save this article

Climate Change Communication in an Online Q&A Community: A Case Study of Quora

Author

Listed:
  • Hanchen Jiang

    (State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China)

  • Maoshan Qiang

    (State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China)

  • Dongcheng Zhang

    (State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China)

  • Qi Wen

    (State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China)

  • Bingqing Xia

    (State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China)

  • Nan An

    (State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China)

Abstract

An emerging research trend in climate change studies is to use user-generated-data collected from social media to investigate the public opinion and science communication of climate change issues. This study collected data from the social Q&A website Quora to explore the key factors influencing the public preferences in climate change knowledge and opinions. Using web crawler, topic modeling, and count data regression modeling, this study quantitatively analyzed the effects of an answer’s textual and auxiliary features on the number of up-votes received by the answer. Compared with previous studies based on open-ended surveys of citizens, the topic modeling result indicates that Quora users are more likely to talk about the energy, human and societal issues, and scientific research rather than the natural phenomena of climate change. The regression modeling results show that: (i) answers with more emphasis on specific subjects, but not popular knowledge, about climate change can get significantly more up-votes; (ii) answers with more terms of daily dialogue will get significantly fewer up-votes; and (iii) answers written by an author with more followers, with a longer text, with more images, or belonging to a question with more followers, can get significantly more up-votes.

Suggested Citation

  • Hanchen Jiang & Maoshan Qiang & Dongcheng Zhang & Qi Wen & Bingqing Xia & Nan An, 2018. "Climate Change Communication in an Online Q&A Community: A Case Study of Quora," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1509-:d:145554
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/5/1509/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/5/1509/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Endre Tvinnereim & Kjersti Fløttum, 2015. "Explaining topic prevalence in answers to open-ended survey questions about climate change," Nature Climate Change, Nature, vol. 5(8), pages 744-747, August.
    2. Juha Itkonen, 2015. "Social ties and concern for global warming," Climatic Change, Springer, vol. 132(2), pages 173-192, September.
    3. Kathryn Harrison & Lisa McIntosh Sundstrom, 2007. "The Comparative Politics of Climate Change," Global Environmental Politics, MIT Press, vol. 7(4), pages 1-18, November.
    4. Teresa A. Myers & Edward W. Maibach & Connie Roser-Renouf & Karen Akerlof & Anthony A. Leiserowitz, 2013. "The relationship between personal experience and belief in the reality of global warming," Nature Climate Change, Nature, vol. 3(4), pages 343-347, April.
    5. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    6. P. Sol Hart & Erik C. Nisbet & Teresa A. Myers, 2015. "Public attention to science and political news and support for climate change mitigation," Nature Climate Change, Nature, vol. 5(6), pages 541-545, June.
    7. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
    8. Francesca De Battisti & Alfio Ferrara & Silvia Salini, 2015. "A decade of research in statistics: a topic model approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 413-433, May.
    9. Helen Crompton, 2007. "Mode 2 knowledge production: Evidence from orphan drug networks," Science and Public Policy, Oxford University Press, vol. 34(3), pages 199-211, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Leon Yufeng Wu & Shannah Pinhsuan Wu & Chun-Yen Chang, 2019. "Merging Science Education into Communication: Developing and Validating a Scale for Science Edu-Communication Utilizing Awareness, Enjoyment, Interest, Opinion formation, and Understanding Dimensions ," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    2. Kirtika Deo & Abhnil Amtesh Prasad, 2020. "Evidence of Climate Change Engagement Behaviour on a Facebook Fan-Based Page," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    3. Wen Shi & Haohuan Fu & Peinan Wang & Changfeng Chen & Jie Xiong, 2020. "#Climatechange vs. #Globalwarming: Characterizing Two Competing Climate Discourses on Twitter with Semantic Network and Temporal Analyses," IJERPH, MDPI, vol. 17(3), pages 1-22, February.
    4. Wen Shi & Changfeng Chen & Jie Xiong & Haohuan Fu, 2019. "What Framework Promotes Saliency of Climate Change Issues on Online Public Agenda: A Quantitative Study of Online Knowledge Community Quora," Sustainability, MDPI, vol. 11(6), pages 1-24, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Savin, Ivan & Ott, Ingrid & Konop, Chris, 2022. "Tracing the evolution of service robotics: Insights from a topic modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Camilla Salvatore & Silvia Biffignandi & Annamaria Bianchi, 2022. "Corporate Social Responsibility Activities Through Twitter: From Topic Model Analysis to Indexes Measuring Communication Characteristics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1217-1248, December.
    3. Imran Ali & Devika Kannan, 2022. "Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review," Annals of Operations Research, Springer, vol. 315(1), pages 29-55, August.
    4. Ivan Savin & Kristina Chukavina & Andrey Pushkarev, 2023. "Topic-based classification and identification of global trends for startup companies," Small Business Economics, Springer, vol. 60(2), pages 659-689, February.
    5. Tobias Wekhof & Sébastien Houde, 2023. "Using narratives to infer preferences in understanding the energy efficiency gap," Nature Energy, Nature, vol. 8(9), pages 965-977, September.
    6. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    7. Gagliarducci, Stefano & Paserman, M. Daniele & Patacchini, Eleonora, 2019. "Hurricanes, Climate Change Policies and Electoral Accountability," IZA Discussion Papers 12334, Institute of Labor Economics (IZA).
    8. Guglielmo Zappalà, 2023. "Drought Exposure and Accuracy: Motivated Reasoning in Climate Change Beliefs," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 85(3), pages 649-672, August.
    9. Minchul Lee & Min Song, 2020. "Incorporating citation impact into analysis of research trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1191-1224, August.
    10. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    11. Islam, Moinul & Kotani, Koji & Managi, Shunsuke, 2016. "Climate perception and flood mitigation cooperation: A Bangladesh case study," Economic Analysis and Policy, Elsevier, vol. 49(C), pages 117-133.
    12. Grajzl, Peter & Murrell, Peter, 2021. "A machine-learning history of English caselaw and legal ideas prior to the Industrial Revolution I: generating and interpreting the estimates," Journal of Institutional Economics, Cambridge University Press, vol. 17(1), pages 1-19, February.
    13. Kaitlin T Raimi & Paul C Stern & Alexander Maki, 2017. "The Promise and Limitations of Using Analogies to Improve Decision-Relevant Understanding of Climate Change," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-20, January.
    14. Sam Barrett, 2015. "Subnational Adaptation Finance Allocation: Comparing Decentralized and Devolved Political Institutions in Kenya," Global Environmental Politics, MIT Press, vol. 15(3), pages 118-139, August.
    15. Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
    16. Dehler-Holland, Joris & Schumacher, Kira & Fichtner, Wolf, 2021. "Topic Modeling Uncovers Shifts in Media Framing of the German Renewable Energy Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 2(1).
    17. Marcel Fratzscher & Tobias Heidland & Lukas Menkhoff & Lucio Sarno & Maik Schmeling, 2023. "Foreign Exchange Intervention: A New Database," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(4), pages 852-884, December.
    18. Bokyong Shin & Chaitawat Boonjubun, 2021. "Media and the Meanings of Land: A South Korean Case Study," American Journal of Economics and Sociology, Wiley Blackwell, vol. 80(2), pages 381-425, March.
    19. Parijat Chakrabarti & Margaret Frye, 2017. "A mixed-methods framework for analyzing text data: Integrating computational techniques with qualitative methods in demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(42), pages 1351-1382.
    20. Li Tang & Jennifer Kuzma & Xi Zhang & Xinyu Song & Yin Li & Hongxu Liu & Guangyuan Hu, 2023. "Synthetic biology and governance research in China: a 40-year evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5293-5310, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1509-:d:145554. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.