Climate change misinformation leads to significant adverse impacts and has become a global concern. Identifying misinformation and investigating its characteristics are of great importance to counteract misinformation. Therefore, this study aims to characterize the semantic features (frames and authority references) of climate change misinformation in the context of Chinese social media. Posts concerning climate change were collected from Weibo between January 2010 and December 2020. First, veracity, frames, and authority references were manually labeled. Then, we applied logistic regression to examine the relationship between information veracity and semantic features. The results revealed that posts concerning environmental and health impact and science and technology were more likely to be misinformation. Moreover, posts referencing non-specific authority sources are more likely to be misinformed than posts making no references to any authority references. This study provides a theoretical understanding of the semantic characteristics of climate change misinformation and practical suggestions for combating them.
BierwischMHeidolphKE (2014) Progress in Linguistics: A Collection of Papers. Berlin: Walter de Gruyter GmbH & Co KG.
2.
BolsenTShapiroMA (2018) The US news media, polarization on climate change, and pathways to effective communication. Environmental Communication12(2): 149–163.
3.
BrismanA (2012) The cultural silence of climate change contrarianism. In: WhiteR (ed.) Climate Change from a Criminological Perspective. New York, NY: Springer, pp. 41–70.
4.
CaiWZhangCSuenHPAiSBaiYBaoJ, et al. (2021) The 2020 China report of the Lancet Countdown on health and climate change. The Lancet Public Health6(1): e64–e81.
5.
CastilloCMendozaMPobleteB (2011) Information credibility on twitter. In: Proceedings of the 20th international conference on World wide web—WWW ’11, Hyderabad, India, 28 March–1 April, p. 675. New York: ACM Press.
6.
Center for China Climate Change Communication (2017) survey report on public’s recognition of climate change communication in China 2017. Available at: http://www.weather.com.cn/climate/2017/11/2797699.shtml (accessed 26 January 2022).
7.
ChoudharyAAroraA (2021) Linguistic feature based learning model for fake news detection and classification. Expert Systems with Applications169: 114171.
8.
Committee on Science Literacy and Public Perception of Science, Board on Science Education and Division of Behavioral and Social Sciences and Education (2016) Science Literacy: Concepts, Contexts, and Consequences (eds SnowCEDibnerKA). The National Academies Collection: Reports funded by National Institutes of Health. Washington (DC): National Academies Press (US). Available at: http://www.ncbi.nlm.nih.gov/books/NBK396090/ (accessed 24 January 2022).
9.
CookJEllertonPKinkeadD (2018) Deconstructing climate misinformation to identify reasoning errors. Environmental Research Letters13(2): 024018.
10.
CookJLewandowskySEckerUKH (2017) Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. PLoS ONE12(5): e0175799.
11.
DamstraAVliegenthartRBoomgaardenHGlüerKLindgrenEStrömbäckJ, et al. (2021) Knowledge and the news: An investigation of the relation between news use, news avoidance, and the presence of (mis)beliefs. The International Journal of Press/Politics28: 29–48.
DunlapRE (2013) Climate change skepticism and denial: An introduction. American Behavioral Scientist57(6): 691–698.
14.
EckerUKHLewandowskySCookJSchmidPFazioLKBrashierN, et al. (2022) The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology1(1): 13–29.
15.
HaLAndreu PerezLRayR (2021) Mapping recent development in scholarship on fake news and misinformation, 2008 to 2017: Disciplinary contribution, topics, and impact. American Behavioral Scientist65(2): 290–315.
16.
HernonP (1995) Disinformation and misinformation through the internet: Findings of an exploratory study. Government Information Quarterly12(2): 133–139.
17.
HobbsR (2013) Multiple visions of multimedia literacy: Emerging areas of synthesis. International Handbook of Literacy and Technology2: 15–28.
18.
HovlandCIWeissW (1951) The influence of source credibility on communication effectiveness. Public Opinion Quarterly15(4): 635.
19.
HowellELBrossardD (2021) (Mis)informed about what? What it means to be a science-literate citizen in a digital world. Proceedings of the National Academy of Sciences118(15): 1912436117.
20.
JacquesPJKnoxCC (2016) Hurricanes and hegemony: A qualitative analysis of micro-level climate change denial discourses. Environmental Politics25(5): 831–852.
21.
JiJChaoNWeiSBarnettGA (2021) Microblog credibility indicators regarding misinformation of genetically modified food on Weibo. PLoS ONE16(6): e0252392.
22.
JiJZhuYChaoN (2023) A comparison of misinformation feature effectiveness across issues and time on Chinese social media. Information Processing & Management60(2): 103210.
23.
JonesMDSongG (2014) Making sense of climate change: How story frames shape cognition. Political Psychology35(4): 447–476.
24.
KhanMLIdrisIK (2019) Recognise misinformation and verify before sharing: A reasoned action and information literacy perspective. Behaviour & Information Technology38(12): 1194–1212.
25.
KiousisS (2001) Public trust or mistrust? Perceptions of media credibility in the information age. Mass Communication and Society4(4): 381–403.
26.
KirilenkoAPStepchenkovaSO (2014) Public microblogging on climate change: One year of Twitter worldwide. Global Environmental Change26: 171–182.
27.
KumarASangwanSRNayyarA (2019) Rumour veracity detection on twitter using particle swarm optimized shallow classifiers. Multimedia Tools and Applications78(17): 24083–24101.
28.
LazerDMJBaumMABenklerY (2018) The science of fake news. Science359(6380): 1094–1096.
29.
LewandowskySOberauerKGignacGE (2013) NASA faked the moon landing—therefore, (climate) science is a hoax: An anatomy of the motivated rejection of science. Psychological Science24(5): 622–633.
30.
LörcherINeverlaI (2015) The dynamics of issue attention in online communication on climate change. Media and Communication3(1): 117–133.
31.
McGlynnJBaryshevtsevMDaytonZA (2020) Misinformation more likely to use non-specific authority references: Twitter analysis of two COVID-19 myths. Harvard Kennedy School Misinformation Review1(3): 37.
32.
MaximLMansierP (2014) How is scientific credibility affected by communicating uncertainty? the case of endocrine disrupter effects on male fertility. Human and Ecological Risk Assessment: An International Journal20(1): 201–223.
33.
NisbetMC (2009) Communicating climate change: Why frames matter for public engagement. Environment: Science and Policy for Sustainable Development51(2): 12–23.
34.
O’BrienTCPalmerRAlbarracinD (2021) Misplaced trust: When trust in science fosters belief in pseudoscience and the benefits of critical evaluation. Journal of Experimental Social Psychology96: 104184.
35.
O’NeillSWilliamsHTPKurzTWiersmaBBoykoffM (2015) Dominant frames in legacy and social media coverage of the IPCC Fifth Assessment Report. Nature Climate Change5(4): 380–385.
36.
PainterJKristiansenSSchäferMS (2018) How “Digital-born” media cover climate change in comparison to legacy media: A case study of the COP 21 summit in Paris. Global Environmental Change48: 1–10.
PluvianoSDella SalaSWattC (2020) The effects of source expertise and trustworthiness on recollection: The case of vaccine misinformation. Cognitive Processing21(3): 321–330.
39.
RisbeyJS (2008) The new climate discourse: Alarmist or alarming?Global Environmental Change18(1): 26–37.
40.
Sanz-MenéndezLCruz-CastroL (2019) The credibility of scientific communication sources regarding climate change: A population-based survey experiment. Public Understanding of Science28(5): 534–553.
41.
SarathchandraDHaltinnerK (2020) Trust/distrust judgments and perceptions of climate science: A research note on skeptics’ rationalizations. Public Understanding of Science29(1): 53–60.
42.
SchäferMS (2012) Online communication on climate change and climate politics: A literature review. Wires Climate Change3(6): 527–543.
43.
ScheufeleDA (2013) Communicating science in social settings. Proceedings of the National Academy of Sciences110: 14040–14047.
44.
ScheufeleDAKrauseNM (2019) Science audiences, misinformation, and fake news. Proceedings of the National Academy of Sciences116(16): 7662–7669.
45.
ShehataAHopmannDN (2012) FRAMING CLIMATE CHANGE: A study of US and Swedish press coverage of global warming. Journalism Studies13(2): 175–192.
46.
ShuKBernardHRLiuH (2019) Studying fake news via network analysis: Detection and mitigation. In: AgarwalNDokoohakiNTokdemirS (eds) Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining (Lecture Notes in Social Networks). Cham: Springer, pp. 43–65.
47.
ShuKSlivaAWangSTangJLiuH (2017) Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter19(1): 22–36.
48.
SinghSPSwansonM (2017) How issue frames shape beliefs about the importance of climate change policy across ideological and partisan groups. PLoS ONE12(7): e0181401.
49.
Swire-ThompsonBLazerD (2020) Public health and online misinformation: Challenges and recommendations. Annual Review of Public Health41(1): 433–451.
50.
TakahashiB (2011) Framing and sources: A study of mass media coverage of climate change in Peru during the V ALCUE. Public Understanding of Science20(4): 543–557.
51.
TandocECLimZWLingR (2018) Defining “fake news”: A typology of scholarly definitions. Digital Journalism6(2): 137–153.
52.
ThommEBrommeR (2012) “It should at least seem scientific!” Textual features of “scientificness” and their impact on lay assessments of online information: Impact of “Scientificness” on Lay Assessments. Science Education96(2): 187–211.
TrumboC (1996) Constructing climate change: Claims and frames in US news coverage of an environmental issue. Public Understanding of Science5(3): 269–283.
55.
van der LindenSLeiserowitzARosenthalS (2017) Inoculating the public against misinformation about climate change. Global Challenges1(2): 1600008.
56.
VragaEKBodeL (2020) Defining misinformation and understanding its bounded nature: Using expertise and evidence for describing misinformation. Political Communication37(1): 136–144.
57.
VuHTBlombergMSeoH (2021) Social media and environmental activism: Framing climate change on Facebook by Global NGOs. Science Communication43(1): 91–115.
58.
WeberEUSternPC (2011) Public understanding of climate change in the United States. American Psychologist66(4): 315–328.
59.
WestJDBergstromCT (2021) Misinformation in and about science. Proceedings of the National Academy of Sciences118(15): e1912444117.
60.
XiangNWangLZhongSZhengCWangBQuQ (2021) How does the world view China’s carbon policy? A sentiment analysis on Twitter data. Energies14(22): MDPIAG7782.
61.
ZaboskiBATherriaultDJ (2020) Faking science: Scientificness, credibility, and belief in pseudoscience. Educational Psychology40(7): 820–837.
62.
ZhangXGhorbaniAA (2020) An overview of online fake news: Characterization, detection, and discussion. Information Processing & Management57(2): 102025.
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