Using Transfer Entropy and Data Mining to Analyze Climate Information Communication Flow in China

Meifen Wu (吴梅芬); Ruyin Long (龙如银); Jingwen Na (那静文); Wenbo Li (李文博)

Abstract

In the era of globalization and digitalization, this study employs transfer entropy and data mining to analyze climate information flows before and after China’s dual-carbon target. The analysis is based on 904,837 posts collected from 2019 to November 2021, including 17,380 original posts, 767,171 first-layer retweets, and 120,286 second-layer retweets, and combines Latent Dirichlet Allocation (LDA) topic modeling, Bidirectional Encoder Representations from Transformers (BERT)-based emotion classification, information entropy, and transfer entropy. Results show a policy-driven topic shift from “climate reporting” to “carbon neutrality” following the policy announcement, accompanied by changes in the distribution of public emotions, including greater visibility of anger and sadness in some dissemination layers. Government and celebrities dominate agenda-setting, while emotional polarization among ordinary and talent users indicates hierarchical differences in aggregate emotional expression. During diffusion, emotional expression shifted from positive toward negative categories, a pattern that is consistent with an “emotional cycle effect”. Geographic differences were observed in climate-related posting activity and emotional expression. Information entropy differed across dissemination layers, indicating changes in the diversity and uncertainty of emotional distributions. These insights can inform more effective climate-policy communication and dissemination strategies.

References

1.
Ford, J.D.; Tilleard, S.E.; Berrang-Ford, L.; et al. Big Data Has Big Potential for Applications to Climate Change Adaptation. Proc. Natl. Acad. Sci. USA 2016, 113, 10729–10732. https://doi.org/10.1073/pnas.1614023113.
2.
Stroik, P.; Chakraborty, D.; Ge, W.; et al. Effect of Reciprocity on Public Opinion of International Climate Treaties: Experimental Evidence from the US and China. Clim. Policy 2019, 19, 959–973.
3.
Foderaro, A.; Lorentzen, D.G. Argumentative Practices and Patterns in Debating Climate Change on Twitter. Aslib J. Inf. Manag. 2023, 75, 131–148. https://doi.org/10.1108/ajim-06-2021-0164.
4.
Navío-Marco, J.; Ruiz-Gómez, L.M.; Sevilla-Sevilla, C. Progress in Information Technology and Tourism Management: 30 Years on and 20 Years after the Internet-Revisiting Buhalis & Law’s Landmark Study about eTourism. Tour. Manag. 2018, 69, 460–470.
5.
Huang, H.; Long, R.; Chen, H.; et al. Exploring Public Attention about Green Consumption on Sina Weibo: Using Text Mining and Deep Learning. Sustain. Prod. Consum. 2022, 30, 674–685. https://doi.org/10.1016/j.spc.2021.12.017.
6.
Duijndam, S.; van Beukering, P. Understanding Public Concern about Climate Change in Europe, 2008–2017: The Influence of Economic Factors and Right-Wing Populism. Clim. Policy 2021, 21, 353–367. https://doi.org/10.1080/14693062.2020.1831431.
7.
Hickman, C.; Marks, E.; Pihkala, P.; et al. Climate Anxiety in Children and Young People and Their Beliefs about Government Responses to Climate Change: A Global Survey. Lancet Planet. Health 2021, 5, e863–e873. https://doi.org/10.1016/s2542-5196(21)00278-3.
8.
Li, D.; Zhao, L.; Ma, S.; et al. What Influences an Individual’s Pro-Environmental Behavior? A Literature Review. Resour. Conserv. Recycl. 2019, 146, 28–34. https://doi.org/10.1016/j.resconrec.2019.03.024.
9.
Gong, P.; Wang, L.; Wei, Y.; et al. Public Attention, Perception, and Attitude towards Nuclear Power in China: A Large-Scale Empirical Analysis Based on Social Media. J. Clean. Prod. 2022, 373, 133919. https://doi.org/10.1016/j.jclepro.2022.133919.
10.
Li, L.; Du, Y.; Ma, S.; et al. Environmental Disaster and Public Rescue: A Social Media Perspective. Environ. Impact Assess. Rev. 2023, 100, 107093. https://doi.org/10.1016/j.eiar.2023.107093.
11.
Leonardi, P.M.; Vaast, E. Social Media and Their Affordances for Organizing: A Review and Agenda for Research. Acad. Manag. Ann. 2017, 11, 150–188. https://doi.org/10.5465/annals.2015.0144.
12.
Ma, X.Y. The Attention Flow, Emotion Flow and Symbol Flow in Weibo’s Interaction. News Writ. 2012. 41–43.
13.
Sun, G.H.; Aoki, M.A.; Dong, S.H. Quantum Information Entropies of the Eigenstates for the PöSchl—Teller-like Potential. Chin. Phys. B 2013, 22, 050302.
14.
Schreiber, T. Measuring Information Transfer. Phys. Rev. Lett. 2000, 85, 461.
15.
Prokopenko, M.; Lizier, J.T. Transfer Entropy and Transient Limits of Computation. Sci. Rep. 2014, 4, 5394.
16.
Behrendt, S.; Dimpfl, T.; Peter, F.J.; et al. RTransferEntropy—Quantifying Information Flow between Different Time Series Using Effective Transfer Entropy. SoftwareX 2019, 10, 100265. https://doi.org/10.1016/j.softx.2019.100265.
17.
Yao, W.; Wang, J. Multi-Scale Symbolic Transfer Entropy Analysis of EEG. Phys. A Stat. Mech. Appl. 2017, 484, 276–281. https://doi.org/10.1016/j.physa.2017.04.181.
18.
Dimpfl, T.; Peter, F.J. The Impact of the Financial Crisis on Transatlantic Information Flows: An Intraday Analysis. J. Int. Financ. Mark. Inst. Money 2014, 31, 1–13. https://doi.org/10.1016/j.intfin.2014.03.004.
19.
Assaf, A.; Bilgin, M.H.; Demir, E. Using Transfer Entropy to Measure Information Flows between Cryptocurrencies. Phys. A Stat. Mech. Its Appl. 2022, 586, 126484. https://doi.org/10.1016/j.physa.2021.126484.
20.
Bertelsen, K.P.; Borup, D.; Jakobsen, J.S. Stock Market Volatility and Public Information Flow: A Non-Linear Perspective. Econ. Lett. 2021, 204, 109905. https://doi.org/10.1016/j.econlet.2021.109905.
21.
Chou, K.H.; Day, M.Y.; Chiu, C.L. Do Bitcoin News Information Flow and Return Volatility Fit the Sequential Information Arrival Hypothesis and the Mixture of Distribution Hypothesis? Int. Rev. Econ. Financ. 2023, 88, 365–385.
22.
Huang, Y.J.; Chen, F.J.; You, D.D. Research on the Prediction of Network Public Opinion Based on Hybrid Algorithm and BP Neural Network. Inf. Sci. 2018, 36, 24–29. https://doi.org/10.13833/j.issn.1007-7634.2018.02.005.
23.
Yang, Q.; Shen, Y. Microboss’s Source Characteristics on Consumer’s Purchase Intention. J. Dalian Univ. Technol. Soc. Sci. 2017, 38, 27–32. https://doi.org/10.19525/j.issn1008-407x.2017.02.005.
24.
Liu, Z.; Guo, S.M. Optimization Path of Network Information Ecosystem. Inf. Sci. 2017, 35, 31–36.
25.
Blei, D.M.; Ng, A.Y.; Jordan, M.I. Latent Dirichlet Allocation. J. Mach. Learn. Res. 2003, 3, 993–1022.
26.
Apaza, R.G.; Cervantes, E.V.; Quispe, L.C.; Luna, J.O. Online Courses Recommendation Based on LDA. In Proceedings of the 1st Symposium on Information Management and Big Data (SIMBig 2014), Cusco, Peru, 8–10 September 2014; CEUR Workshop Proceedings, Vol. 1318; pp. 42–48.
27.
Chang, T.M.; Hsiao, W.F. LDA-Based Personalized Document Recommendation. In Proceedings of the 17th Pacific Asia Conference on Information Systems (PACIS 2013), Jeju Island, Republic of Korea, 18–22 June 2013; p. 13.
28.
Kang, K.; Park, J.; Kim, W.; et al. Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling. In Proceedings of the 28th ACM international conference on information and knowledge management, Beijing, China, 3–7 November 2019; pp. 1543–1552. https://doi.org/10.1145/3357384.3358054.
29.
Joshy, A.; Sundar, S. Analyzing the Performance of Sentiment Analysis Using Bert, Distilbert, and Roberta. In Proceedings of the 2022 IEEE International Power and Renewable Energy Conference (IPRECON), Kollam, India, 16–18 December 2022; pp. 1–6. https://doi.org/10.1109/iprecon55716.2022.10059542.
30.
Devlin, J.; Chang, M.W.; Lee, K.; et al. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv 2018, arXiv:1810.04805.
31.
Pota, M.; Ventura, M.; Fujita, H.; et al. Multilingual Evaluation of Pre-Processing for BERT-Based Sentiment Analysis of Tweets. Expert Syst. Appl. 2021, 181, 115119.
32.
Tao, P.; Ma, H.; Li, C.; et al. Intelligent Grid Load Forecasting Based on BERT Network Model in Low-Carbon Economy. Front. Energy Res. 2023, 11, 1197024. https://doi.org/10.3389/fenrg.2023.1197024.
33.
Zeng, Q.; Shi, C.; Zhu, W.; et al. Sequential Data-Driven Carbon Peaking Path Simulation Research of the Yangtze River Delta Urban Agglomeration Based on Semantic Mining and Heuristic Algorithm Optimization. Energy 2023, 285, 129415. https://doi.org/10.1016/j.energy.2023.129415.
34.
Hlaváčková-Schindler, K.; Paluš, M.; Vejmelka, M.; et al. Causality Detection Based on Information-Theoretic Approaches in Time Series Analysis. Phys. Rep. 2007, 441, 1–46. https://doi.org/10.1016/j.physrep.2006.12.004.
35.
Montalto, A.; Faes, L.; Marinazzo, D. MuTE: A MATLAB Toolbox to Compare Established and Novel Estimators of the Multivariate Transfer Entropy. PLoS ONE 2014, 9, e109462.
36.
Kullback, S.; Leibler, R.A. On Information and Sufficiency. Ann. Math. Stat. 1951, 22, 79–86. https://doi.org/10.1214/aoms/1177729694.
37.
Barnett, L.; Barrett, A.B.; Seth, A.K. Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables. Phys. Rev. Lett. 2009, 103, 238701. https://doi.org/10.1103/physrevlett.103.238701.
38.
Barbosa Escobar, F.; Velasco, C.; Motoki, K.; et al. The Temperature of Emotions. PloS ONE 2021, 16, e0252408. https://doi.org/10.1371/journal.pone.0252408.
39.
Jin, S.; Stokes, G.; Hamilton, C. Empirical Evidence of Urban Climate Adaptation Alignment with Sustainable Development: Application of LDA. Cities 2023, 136, 104254. https://doi.org/10.1016/j.cities.2023.104254.
40.
He, H.; Sun, R. Sentiment Variations Affected by Urban Temperature and Landscape Across China. Cities 2024, 149, 104933. https://doi.org/10.1016/j.cities.2024.104933.
41.
Adger, W.N.; Barnett, J.; Heath, S.; et al. Climate Change Affects Multiple Dimensions of Well-Being through Impacts, Information and Policy Responses. Nat. Hum. Behav. 2022, 6, 1465–1473. https://doi.org/10.1038/s41562-022-01467-8.
42.
Berry, H.L.; Waite, T.D.; Dear, K.B.; et al. The Case for Systems Thinking about Climate Change and Mental Health. Nat. Clim. Chang. 2018, 8, 282–290. https://doi.org/10.1038/s41558-018-0102-4.
43.
Riley, P.; Wang, R.; Wang, Y.; et al. Global Warming: Chinese Narratives of the Future. Glob. Media China 2016, 1, 12–31.
44.
Shen, C.; Wang, Y. Concerned or Apathetic? Exploring Online Public Opinions on Climate Change from 2008 to 2019: A Comparative Study between China and Other G20 Countries. J. Environ. Manag. 2023, 332, 117376.
45.
Vosoughi, S.; Roy, D.; Aral, S. The Spread of True and False News Online. Science 2018, 359, 1146–1151. https://doi.org/10.1126/science.aap9559.
46.
Shi, W.; Tao, F. Spatio-Temporal Distributions of Climate Disasters and the Response of Wheat Yields in China from 1983 To 2008. Nat. Hazards 2014, 74, 569–583.
47.
Shannon, C.E. A Mathematical Theory of Communication: The Bell System Technical Journal. J. Frankl. Inst. 1938, 196, 519–520.
48.
Li, R.; Crowe, J.; Leifer, D.; et al. Beyond Big Data: Social Media Challenges and Opportunities for Understanding Social Perception of Energy. Energy Res. Soc. Sci. 2019, 56, 101217. https://doi.org/10.1016/j.erss.2019.101217.
49.
Chen, H. Beyond Technological Intelligence: Cultivating Societal Wisdom for Human Flourishing and Its Values, Logic, and Pathways. ASTRA MAN 2026, 1, 1.
50.
Wu, Y.; Xie, L.; Yuan, Z.; et al. Investigating Public Biodiversity Conservation Awareness Based on the Propagation of Wildlife-Related Incidents on the Sina Weibo Social Media Platform. Environ. Res. Lett. 2020, 15, 094082. https://doi.org/10.1088/1748-9326/ab9ed1.
51.
Gu, M.; Guo, H.; Zhuang, J.; et al. Social Media User Behavior and Emotions during Crisis Events. Int. J. Environ. Res. Public Health 2022, 19, 5197. https://doi.org/10.3390/ijerph19095197.
52.
Li, L.; Ma, S.; Wang, R.; et al. Citizen Participation in the Co-Production of Urban Natural Resource Assets: Analysis Based on Social Media Big Data. J. Glob. Inf. Manag. 2021, 30, 1–21. https://doi.org/10.4018/jgim.291514.
53.
Khan, Z.; Ali, M.; Kirikkaleli, D.; et al. The Impact of Technological Innovation and Public-Private Partnership Investment on Sustainable Environment in China: Consumption-Based Carbon Emissions Analysis. Sustain. Dev. 2020, 28, 1317–1330. https://doi.org/10.1002/sd.2086.
54.
Nuwan Gunarathne, A.D.; Hitigala Kaluarachchilage, P.K.; Rajasooriya, S.M. Low-Carbon Consumer Behaviour in Climate-Vulnerable Developing Countries: A Case Study of Sri Lanka. Resour. Conserv. Recycl. 2020, 154, 104592.
55.
Lei, L.; Zhang, D.; Ji, Q.; et al. A Text-Based Managerial Climate Attention Index of Listed Firms in China. Financ. Res. Lett. 2023, 55, 103911. https://doi.org/10.1016/j.frl.2023.103911.
56.
Weber, H.; Weber, M. When Means of Implementation Meet Ecological Modernization Theory: A Critical Frame for Thinking about the Sustainable Development Goals Initiative. World Dev. 2020, 136, 105129. https://doi.org/10.1016/j.worlddev.2020.105129.
57.
Wu, J.; Liao, H.; Wang, J.W.; et al. The Role of Environmental Concern in the Public Acceptance of Autonomous Electric Vehicles: A Survey from China. Transp. Res. Part F Traffic Psychol. Behav. 2019, 60, 37–46. https://doi.org/10.1016/j.trf.2018.09.029.
58.
Liu, P.; Segovia, M.; Tse, E.C.Y.; et al. Become an Environmentally Responsible Customer by Choosing Low-Carbon Footprint Products at Restaurants: Integrating the Elaboration Likelihood Model (ELM) and the Theory of Planned Behavior (TPB). J. Hosp. Tour. Manag. 2022, 52, 346–355.
59.
Yilmaz, V.; Can, Y. Impact of Knowledge, Concern and Awareness about Global Warming and Global Climatic Change on Environmental Behavior. Environ. Dev. Sustain. 2020, 22, 6245–6260. https://doi.org/10.1007/s10668-019-00475-5.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us