Skip to content
BY 4.0 license Open Access Published by De Gruyter Mouton April 1, 2022

“Climate change” vs. “global warming”: A corpus-assisted discourse analysis of two popular terms in The New York Times

  • Ming Liu EMAIL logo and Jingyi Huang


“Climate change” and “global warming” are two popular terms that may be often used interchangeably in news media. This study proposes to give a corpus-assisted discourse study of the representations of climate change and global warming in The New York Times (2000–2019) in order to examine how they are actually used in the newspaper. The findings show both similarities and differences in their representations in terms of the associated topics/themes, the particular ways of framing, and the perspectivization strategy employed. It is argued that a corpus-assisted discourse study of a large sample of news articles presents a more accurate picture of the actual use of the two terms in news media.

1 Introduction

While “global warming” and “climate change” have become two popular terms in recent years to describe the climatic phenomenon caused by changes in atmospheric concentrations of greenhouse gases (Lorenzoni and Pidgeon 2006), they are often used interchangeably as if they refer to the same phenomenon (Boykoff and Boykoff 2007; Lineman et al. 2015; Penz 2018; Schuldt et al. 2011; Whitmarsh 2009). Some researchers prefer to use “climate change” (e.g. Jaworska 2018; Koteyko et al. 2013; Leiserowitz 2006), while others choose the term “global warming” (e.g. McCright and Dunlap 2000; Olausson 2009; Ytterstad 2015). In some studies, both terms are used as keywords to extract news articles for the study of climate change (e.g. Dayrell and Urry 2015; Knox and Jacques 2016; Schmid-Petri et al. 2017). Previous studies have also demonstrated that the two terms may trigger similar affective responses in the US, Britain, and other parts of the world (Lorenzoni and Pidgeon 2006; Villar and Krosnick 2011).

Nevertheless, some other studies argue for the necessity to make a distinction between the two terms (Shi et al. 2020; Whitmarsh 2009). The term “climate change”, originally called “climatic change”, was first coined by Plass (1956) who investigated a strong relationship between carbon dioxide and the temperatures and climate of Earth’s surface in history. The term “global warming” was brought into focus later as Broecker (1975) developed a model to predict the rising temperatures due to the increases of carbon dioxide in the atmosphere. Due to their different origins, the two terms are clearly distinguished in some scientific reports (e.g. The Intergovernmental Panel on Climate Change report) and some academic research. “Global warming” is defined as an increase in the average Earth’s temperature constantly (Lineman et al. 2015; Shi et al. 2020), while climate change is conceptualized as all forms of climatic and weather variability, involving hotter summers, colder winters, higher rainfall, and increased drought (Lorenzoni and Pidgeon 2006; Schuldt et al. 2011; Whitmarsh 2009). Previous studies have further demonstrated that the interpretations of the two terms may vary with people’s political affiliations (Villar and Krosnick 2011), their gender identity (Greenhill et al. 2014), their knowledge and experience (Benjamin et al. 2017), or their beliefs (Akerlof and Maibach 2011).

While some previous studies rely on surveying and interviewing in exposing public understanding of the two terms (e.g. Villar and Krosnick 2011; Whitmarsh 2009), this study is going to give a corpus-assisted discourse study (CADS) of the actual use of the two terms in The New York Times (NYT). The primary purpose is to give a close examination of the use of the two terms in this English newspaper. Previous studies on US news media’s representations of climate change (CC) or global warming (GW) tend to center on several themes, such as the climate skepticism (Antilla 2005; Schmid-Petri et al. 2017; Stecula and Merkley 2019), journalistic norm and media bias (Bohr 2020; Boykoff 2007; Boykoff and Boykoff 2004, 2007), politicization and polarization (Bolsen and Shapiro 2017; Chinn et al. 2020; Feldman et al. 2015). They have identified some important patterns in the representations of CC and GW, such as the prevalence of skepticism, the presence of “false balance”, and the prominent feature of politicization and polarization of viewpoints from different political parties. However, there is little information about whether CC and GW are represented in the same way in the same US news media.

2 Corpus-assisted environmental discourse studies

Given the surge in attention given to climate change in recent years, there is a growing demand for the incorporation of automatic text analysis techniques into the analysis of the large volume of climate change texts (Boykoff and Roberts 2007; Grundmann and Scott 2014; Schmidt et al. 2013). In a review of automatic text analysis techniques for the study of climate change discourse, Salway (2017) makes a distinction between two general trends: (1) the automatic classification of texts in terms of subtopics and frames; and (2) corpus-assisted discourse analysis. While the former can allow the efficient processing of data on a mass scale, it treats texts as a “bag of words” and “information about word order and higher-level linguistic structures are ignored” (Salway 2017: 156). Corpus-assisted discourse analysis underlines using corpus linguistics (CL) methods in discourse analysis, such as frequency list, keywords, clusters, collocates, and concordances (Baker 2006; Liu and Zhong 2020). On the one hand, it allows the use of computer-assisted corpus-analytic tools in the processing of large samples of texts and identifying important linguistic patterns; on the other hand, it highlights the incorporation of discourse analytic methods and theories in the descriptions and interpretations of the findings generated by corpus-analytic tools (Baker and McEnery 2015; Baker et al. 2008; Cheng 2013; Liu and Ma 2021). Using CL methods in discourse analysis can allow the efficient analysis of a large sample of texts, provide empirical evidence for testing research assumptions, generate findings that cannot be acquired through mere manual analysis of a small sample of texts, reduce the researcher bias in data selection and interpretation, and make the analytic procedure replicable (Baker 2006; Baker and McEnery 2015; Friginal and Hardy 2020; Liu and Lin 2021). It has given rise to corpus-assisted environmental discourse studies on a number of topics, such as climate problems and carbon-related concepts (e.g. Bevitori 2010; Nerlich and Koteyko 2009), environmental destruction (e.g. Alexander 2010; Liu and Li, 2017; Liu and Zhang 2018; Poole 2016), animals and the nature (e.g. Sealey and Oakley 2013), sustainability (e.g. Orna-Montesinos 2015) and ecotourism (e.g. Jaworska 2017; Ruffolo 2015).

Previous studies have also resorted to automatic text analysis techniques to compare the actual use of the two terms on social media and identified some significant differences in the choice of the two terms (Jang and Hart 2015; Lineman et al. 2015; Shi et al. 2020). In a big data analysis of the use of the two terms on Twitter, Jang and Hart (2015) find that compared to “blue states”, the “red states” in the US prefer GW to CC and tend to associate GW with hoax frames. Shi et al. (2020) also conduct a big data analysis of the discourses associated with CC and GW on Twitter and find that CC “demonstrated a more scientific perspective and showed an attempt to condense climate discussions rather than diffuse the topic by frequently addressing sub-topics simultaneously”, whereas GW “triggered more political responses and showed a greater connection with phenomena” (Shi et al. 2020: 1). Besides, CC is more dominant than GW in public discussions in recent years. However, as Salway (2017) argues, these studies treat a text as a “bag of words”, and run the risk of missing many other aspects of the meaning arising from the sequencing of words in grammatical structures (Salway 2017: 155). However, Grundmann and Krishnamurthy (2010) give a corpus-based study of the US and the UK news media from 1998 to 2007 and find that the US media tend to use GW more frequently, whereas the UK media prefer to use more CC. Besides, the UK media tend to focus on the international aspects of the issue, while the US media tend to focus on domestic aspects. The UK media are inclined to cite voices from environmental and non-governmental organizations and interest groups, while the US media tend to highlight voices of individual advocates. In a more recent study, Grundmann and Scott (2014) also discover that the US media preferably discuss impacts and issues of the climate, while the UK media focus more on the dangers. Besides, more skeptical voices are found in the US newspaper. Nevertheless, few studies have given a critical examination of the use of the two terms in the same newspaper, and this study is going to fill the gap by conducting a corpus-assisted discourse analysis of the use of the two terms in NYT.

3 Methodology

3.1 Data collection

This study focuses on The New York Times, a newspaper that is known as a “newspaper of record” (Cotter 2001: 416) in the US and is important in influencing media agenda in other countries. Two large corpora have been built by collecting the news texts with CC or GW in their titles in the NYT from the electronic database Lexis-Nexis. The time-span set for data collection is from January 1, 2000 to December 31, 2019. Previous studies on CC have found that climate issues have received increasing attention since the mid-2000s in most developed and developing countries (Song et al. 2021). All the collected news texts are further examined manually to make sure that they are closely related to GW or CC. The GW corpus consists of 363 news texts (279,839 tokens), while the CC corpus consists of 1,088 news texts (1,285,183 tokens). Therefore, CC was used much more frequently than GW during the examined period. Figure 1 shows the number of news texts collected each year, and Figure 2 shows the raw frequencies of the two terms in the two corpora.

Figure 1: 
The number of news reports collected for each year.
Figure 1:

The number of news reports collected for each year.

Figure 2: 
The frequencies of two terms in each year.
Figure 2:

The frequencies of two terms in each year.

As Figure 1 shows, NYT features fewer uses in CC before 2005 (Bohr 2020; Feldman et al. 2015; Schmid-Petri et al. 2017). Before 2008, GW was more frequently used than CC in NYT, but this trend has been reversed since 2008. Compared with the growing coverage of CC, the coverage of GW was relatively stable and comparatively few after 2008. The first surge of attention to CC during 2007–2010 can be attributed to several activities, such as the release of the Fourth Assessment Report from the Intergovernmental Panel on Climate Change (IPCC), the release of an award-winning documentary by the former Vice President Al Gore, and the “Climategate” scandal (Bohr 2020). The coverage of CC reached its first peak in 2009, which can be attributed to COP15 in Copenhagen (2009). The coverage of CC has shown a dramatic increase since 2014 and reached its second peak in 2015, which can be attributed to COP21 in Paris (2015). Since then, the coverage of CC has been doubled due to the former US President Donald Trump who questioned the real existence of CC and made it a controversial issue.

3.2 Analytic methods and procedure

This study gives a corpus-assisted discourse analysis of the representations of CC and GW in NYT in terms of three aspects: (1) the topics/themes associated with CC and GW; (2) the particular ways of framing CC and GW; and (3) the perspectivization strategy taken towards CC and GW. Perspectivization strategy is a method to position writers’ viewpoints to express involvement and distance (Reisigl and Wodak 2016). It is usually reflected by the use of deictics, direct, indirect, free indirect speech, etc. We start with the identification of the topics/themes associated with the coverage of CC and GW. The corpus-analytic tool Wmatrix 3.0 is used for the analysis of the most prominent and preferential topics/themes (Liu 2017). Wmatrix 3.0 distinguishes itself from other corpus-analytic tools for the UCREL Semantic Analysis System (USAS) it incorporates. USAS divides the English vocabulary into 21 semantic domains which are further divided into 232 semantic categories (SMCs). Wmatrix 3.0 can do automatic semantic analysis and corpus comparison. It can produce a key SMC list by comparing one corpus with a specialized or general reference corpus. The key SMC list can rank these statistically significant key SMCs in terms of their log-likelihood values. The more their key values are, the more statistically significant these key SMCs are. In order to identify the most prominent themes/topics of the two corpora, this study first compares the two corpora respectively with the British National Corpus (BNC) Sampler written corpus in Wmatrix and produces two key SMC lists. Although it has to be acknowledged that an American sampler Written corpus is preferred here, the choice of American and British does not seriously affect the results because the semantic analysis categorizes these words in terms of meanings rather than spellings in Wmatrix. Then we focus on the top 20 key SMCs in each list to identify the shared topics/themes of the two corpora. After that, the two corpora are compared with each other to identify their preferential key SMCs. For the convenience of analysis, we still focus on the top 20 key SMCs in each corpus. The specific functions of each key SMC can be further examined through a close examination of the most frequently used tokens in each key SMC (L’Hôte 2010; Rayson 2008).

It is followed by a close examination of the particular ways of framing CC and GW in the two corpora (see Grundmann 2021). According to Entman (1993), to frame is to “select some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote a particular problem definition, causal interpretation, moral evaluation, and/or treatment recommendation for the item described” (Entman 1993: 52, italics in original). The particular ways of framing CC and GW can be examined through their strong collocates (Baker et al. 2008). Different statistic measures can be used to measure the strength of collocation (Baker 2006), and the log-likelihood (LL) values are used in this study to rank the collocates of CC and GW. With the corpus-analytic tool Wordsmith 7.0, this study focuses on the top 100 collocates of CC and GW in their respective corpora to identify which collocates contribute to the four aspects of framing the issue: (1) problem definition; (2) cause; (3) consequence; and (4) solution/action.

Nevertheless, the analysis of framing in terms of their collocates may not present an accurate picture about the exact stance of the newspaper towards the (un)certainty of CC or GW in view of the journalistic norm of balanced reporting (Boykoff and Boykoff 2004). In order to examine to what extent the newspaper adheres to the journalistic norm of balanced reporting and (dis)aligns with the scientific consensus on the existence of CC or GW, this study gives a further analysis of the perspectivization strategy adopted towards the (un)certainty of CC and GW. Perspectivization refers to how the news reporters position themselves towards others’ viewpoints and express involvement or distance in discourse (Reisigl and Wodak 2016), and it can manifest at all levels of discourse (KhosraviNik 2010). The concordance lines of some collocates with a contradictory evaluation of the existence of CC and GW are further examined to reveal the specific perspectivization strategies taken towards these statements.

4 Findings

4.1 Analysis of topics/themes

Table 1 shows the top 20 key SMCs of both corpora. Among them, 17 can be identified in both corpora, including W4 (“Weather”), A2.1+ (“Change”), Y1 (“Science and technology in general”), W5 (“Green issues”), Z99 (“unmatched”), W3 (“Geographical terms”), O1.3 (“Substances and materials: Gas”), O4.6+ (“Temperature: Hot/on fire”), Q2.1 (“Speech: Communicative”), G1.1 (“Government”), W1 (“The universe”), O4.6 (“Temperature”), F4 (“Farming & Horticulture”), A2.2 (“Cause & effect/connection”), N5.1+++ (“Entire; maximum”), A13 (“Degree”), and X5.2+ (“Interested/excited/energetic”). They show that the representations of CC and GW share many similarities in the choice of topics/themes.

Table 1:

Top 20 key SMCs in the two corpora.

Rank CC GW
1 W4 14,444.79 Weather W4 4,867.86 Weather
2 A2.1+ 4,967.33 Change O4.6+ 3,949.05 Temperature: hot/on fire
3 Y1 3,289.33 Science and technology in general O1.3 3,014.7 Substances and materials: gas
4 W5 3,168.83 Green issues Y1 2,680.99 Science and technology in general
5 Z99 2,677.07 Unmatched W3 2,279.73 Geographical terms
6 W3 2,627.75 Geographical terms W5 2,232.59 Green issues
7 O1.3 2,528.37 Substances and materials: Gas Z99 1,708.21 Unmatched
8 O4.6+ 2,342.64 Temperature: Hot/on fire Q2.1 1,202.25 Speech: Communicative
9 Q2.1 2,232.85 Speech: Communicative O4.6 1,036.07 Temperature
10 G1.1 2,012.46 Government A2.1+ 926.56 Change
11 W1 1,487.96 The universe W1 815.62 The universe
12 O4.6 896.87 Temperature G1.1 636.24 Government
13 F4 872.02 Farming & Hoticulture A13 564.63 Degree
14 A13 824.38 Degree F4 531.73 Farming & Horticulture
15 A2.2 782 Cause & Effect/Connection A2.2 493.97 Cause & Effect/Connection
16 A15− 621.23 Danger X5.2+ 384.92 Interested/excited/energetic
17 N5.1+++ 596.31 Entire; maximum Z3 345.97 Other proper names
18 A5.1−−− 532.96 Evaluation: Bad N5.1+++ 300.87 Entire; maximum
19 O1.2 464.5 Substances and materials: Liquid A9− 237.65 Giving
20 X5.2+ 453.84 Interested/excited/energetic W2 234.49 Light

An examination of the most frequent tokens in these shared key SMCs reveals the presence of three themes: (1) environment; (2) politics; and (3) science. The majority of the key SMCs belong to the theme of “environment”, including W4 (e.g. climate, weather, drought), A2.1+ (e.g. change, changes, changing), W5 (e.g. environmental, environment, pollution), O1.3 (e.g. gas, carbon-dioxide, air, gases), O4.6+ (e.g. warming, heat, fire), W1 (e.g. world, planet, solar), W3 (e.g. global, earth, forests), O4.6 (e.g. temperatures, temperature, Fahrenheit), F4 (e.g. farmers, agriculture, agricultural), N5.1+++ (e.g. extreme, extremes), and X5.2+ (e.g. energy). The prominence of this theme is not surprising in view of the fact that both corpora are concerned with environmental issues. Therefore, both corpora underline the change in climate, weather, and temperature, the release of greenhouse gases, the impact on agriculture, and extreme weather. The theme “politics” can be perceived from G1.1, which consists of such tokens as president, government, state, country, nations, and officials. They underline the significance of government in the representations of the two issues. The key SMC Y1 suggests the presence of the theme “science”. Its most frequent tokens are scientists, science, scientific, technology, technologies, and science. They suggest that both corpora make frequent references to science in the representations of the two issues. However, Y1 ranks higher than G1.1 in both corpora, suggesting that “science” is more highly foregrounded than “politics”.

Nevertheless, a comparison of the two corpora with each other also reveals their preference for some themes and topics (see Table 2). CC features the preference for a variety of topics/themes, including “politics” (G1.2, G1.1, G2.1−), “economy” (I1 and I1.1), “food” (F1), “education” (P1), “drinks and alcohol” (F2), “work and employment” (I3.1), “flying and aircraft” (M5). The preference for “politics” suggests that the representations of CC tend to be more politicized than the representations of GW in NYT. Besides, the emphasis on the topics of “food”, “drinks and alcohol”, “economy”, and “work and environment” actually indicates that the representations of CC tend to highlight the economic and environmental impacts of CC. This can also be seen from other key SMCs with negative meanings, including E4.1− (e.g. suffer, suffering, suffered), and A1.1.2 (e.g. damage, devastating, destruction). By contrast, GW shows the preference for a limited number of topics/themes, including “science and technology” (Y1), “gas emissions” (O1.3 and “emissions” in A9−), “substances and materials” (O1), “temperature change” (O4.6), “energy” (X5.2+), and “media” (O4.3). It may be attributed to fewer uses of GW in the recent years. The preference for “science and technology” suggests that GW tends to put more emphasis on scientific consensus than CC. This will be further illustrated in Section 4.3. The preference for “gas emissions”, “substances and materials”, and “energy” also sheds light on its preference to highlight the cause and solution of GW. This can also be seen from its preference for other key SMCs which underline the action taken towards GW, including N5.1 (e.g. limits, and limit), N5− (reduce, reducing, reductions), N3.1 (e.g. measures, measure, measured), N3.8− (e.g. slow, slowdown, slowing), and A1.7+ (e.g. regulate, limit, curb). The preference for “media” (O4.3) can be attributed to the fact that GW tended to draw more media attention in the last two decades. This is in contrast to the use of CC and GW on Twitter, where Shi et al. (2020) find that GW tends to be more politicized and concerned about the general phenomenon, while CC prefers to foreground scientific perspective and refer to specific topics and issues.

Table 2:

Top 20 preferential key SMCs in both corpora.

Rank CC GW
1 Z8 367.67 Pronouns O4.6+ 738.99 Temperature: hot/on fire
2 W4 339.36 Weather O1.3 299.61 Substances and materials: gas
3 A2.1+ 297.04 Change W3 134.79 Geographical terms
4 G1.2 186.36 Politics Z3 125.45 Other proper names
5 I1 65.46 Money generally Y1 96.98 Science and technology in general
6 M5 50.81 Flying and aircraft O4.6 96.38 Temperature
7 F1 47.8 Food A9− 69.09 Giving
8 P1 46.48 Education in general N5.1 51.7 Entirety; maximum
9 S1.1.1 38.81 Social actions, states, and processes B3 50.63 Medicines and medical treatment
10 Z1 36.76 Personal names Z99 39.2 Unmatched
11 F2 34.4 Drinks and alcohol N5− 37.19 Quantities: little
12 G2.1− 32.76 Crime N5 29.65 Quantities
13 E4.1− 30.82 Sad O4.6− 27.67 Temperature: cold
14 I3.1 28.8 Work and employment generally N3.1 27.28 Measurement: general
15 I1.1 28.27 Money and pay W5 26.84 Green issues
16 K1 28.26 Entertainment generally N3.8− 26.62 Speed: slow
17 G1.1 27.58 Government X5.2+ 23.8 Interested/excited/energetic
18 A1.1.2 25.28 Damaging and destroying A1.7+ 21.7 Constraint
19 H1 23.61 Architecture, houses, and buildings Q4.3 21.64 The Media: TV, Radio and Cinema
20 S5 21.1 Groups and affiliation O1 21.4 Substances and materials generally

4.2 Analysis of framing

An examination of the top 100 lexical collocates finds that they can be divided into four categories: (1) problem definition; (2) cause; (3) impact; and (4) solution/action (see Table 3). A number of collocates can be identified for both CC and GW, and they are marked in bold. These shared collocates suggest that CC and GW are represented in a much similar way in the NYT. As regards “problem definition”, both CC and GW are constructed as contentious issues, because these collocates communicate opposite meanings. Some underline the issue as real (i.e. real, reality), while others question the certainty of its existence (i.e. hoax, debate, deny, and skeptics). In view of “cause”, these shared collocates underline both human causes (i.e. human) and non-human causes (i.e. greenhouse, carbon, coal). With regard to “impact”, these collocates highlight the serious impacts and threats of CC and GW, including effects, threat, impact, concern, and poses. In terms of “solution/action”, these shared collocates highlight the fight against the issue (i.e. fight, fighting, combat, tackling, address, and addressing) and the mitigation of the issue (i.e. mitigate). In other words, both CC and GW are framed as contentious issues, caused by both human and non-human factors, with serious consequences and in need of addressing and mitigation actions.

Table 3:

Semantic preferences of climate change and global warming.

Types Climate change (CC) Global warming (GW)
Problem definition issue (124), real (70), hoax (47), debate (45), questions (41), deny (30), reality (30), deniers (28), denial (26), skeptics (21), discussing (15) debate (23), problem (21), issue (21), real (18), consensus (13), reality (9), skepticism (8), debating (6), skeptics (6), uncertain (5), unequivocal (4), hiatus (4), hoax (4), controversial (4), deny (4)
Cause human (136), caused (99), energy (71), emissions (42), carbon (35), causes (31), gas (27), greenhouse (21), coal (16), induced (15), power (13), oil (13), plants (4) human (23), caused (22), pollution (21), made (20), energy (18), causes (14), carbon (13), cause (11), contribute (10), greenhouse (8), automotive (4), coal (4), contributor (4)
Impact effects (211), risks (94), threat (89), impact (83), consequences (55), impacts (54), security (42), related (36), serious (34), affect (32), concern (31), poses (29), concerned (27), catastrophic (24), posed (23), threatens (16), ravages (11) effects (29), threat (28), linked (14), potential (11), impact (11), concern (8), man (7), poses (6), dangers (6), concerns (6), blame (5), dire (4), chaos (3), unrelated (3), doom (3)
Solution/Action fight (122), action (114), address (110), fighting (62), combat (55), addressing (53), adapt (34), tackle (30), tackling (23), mitigate (21), combating (20), dealing (20), counteract (18) fight (30), combat (21), solutions (16), address (15), act (15), fighting (13), curb (11), slow (10), limit (10), battle (8), unchecked (7), limiting (7), slowdown (6), addressing (6), solving (5), mitigate (4), tackling (3)

Their preference for other collocates suggests their divergence in framing the two issues. In terms of “problem definition”, while CC shows a strong collocation with issue (124), GW shows a strong collocation with both problem (21) and issue (21). An issue refers to a situation that requires a discussion or a debate, whereas a problem involves a negative situation that requires an instant solution. Therefore, the nomination of CC or GW as an issue underlines that it is a topic in which different people might have different viewpoints, while the framing of GW as a problem suggests that it must be solved. The representations of GW thus tend to be more negative than the representations of CC. The strategic difference in the choice of the two words can be detected in the following example from NYT:

The president and his aides often described climate change as a “serious issue,” but rarely as a serious problem. (NYT, 03/06/2002)

The difference can also be identified in the collocates related to “solution/action”. While solutions (16) and solving (5) occur as strong collocates of GW, they do not appear as strong collocates for CC. Besides, there is more consensus on the certainty of GW than on the certainty of CC. CC shows a strong collocation with hoax (40), deniers (28), and denial (26). They underline the uncertainty or non-existence of CC. However, GW shows a strong collocation with consensus (13) and unequivocal (4), which underlines the consensus on GW.

As regards the “causes” of CC and GW, while both CC and GW underline human causes, CC tends to put more emphasis on the anthropogenic causes than GW. This can also be seen from the predication strategy for CC and GW. Predication strategy is a strategy to attribute features to entities (Reisigl and Wodak 2016). It can be reflected in the form of adjectives, appositions, prepositional phrases, etc. CC tends to be pre-modified by such expressions as human-caused (51), human-induced (10), and human-driven (3). However, GW is seldom pre-modified by such expressions as human-caused (3), human-induced (2), and human-driven (2). As Taylor (2003: 455) suggests, ‘‘attributive adjectives tend to characterize a thing in terms of a stable, inherent property, whereas predicative adjectives tend to denote more temporary, circumstantial properties”. It suggests that NYT tends to underline human causes as an inherent property of CC, as in the following:

President Vladimir V. Putin has publicly scoffed at the science of human-caused climate change. (NYT, 14/12/2014)

In terms of the “impact” of CC and GW, they show differences in both the nature and severity of the impacts. CC tends to be concerned with national security, as can be seen from the strong collocate security (42). However, security does not occur as a strong collocate of GW. Besides, CC tends to collocate with such tokens which dramatize the negative impact of CC, including serious (34), catastrophic (24). However, they do not appear as strong collocates with GW. Even though GW also shows a strong collocation with other tokens like dire (4) and doom (3). Their frequencies are comparatively low.

With regard to the “solution/action”, while NYT underlines the necessity to address and mitigate the issue, it also underlines the adaptation strategy to CC, as can be seen from the strong collocate adapt (34). Mitigation strategy refers to “activities to reduce or prevent carbon emissions”, while adaptation strategy concerns “activities to adjust economic and social systems to the effects of climate change” (Günay et al. 2021: 1). This is in contradiction to previous studies which claim that news reporting on CC rarely covers adaptation (Boykoff and Roberts 2007; Ford and King 2015; Liu et al. 2008; Moser 2014). However, adapt does not appear as a strong collocate of GW. Instead, as mentioned above, NYT also underlines the solution of the problem of GW, as can be seen from the strong collocates solutions (16) and solving (5). Therefore, while mitigation strategies have been emphasized for both CC and GW, adaption strategy has also been emphasized for CC, solution strategy for GW.

Figure 3 shows the percentages of the total frequencies of the collocates in the four categories when they are compared with the total frequencies of GW and CC in their respective corpus. It shows that CC shows the preference for “impact”, while GW shows the preference for “solution/action”, and “cause”. This further supports the findings in Section 3.1.

Figure 3: 
Percentages of five semantic preferences in the corpus.
Figure 3:

Percentages of five semantic preferences in the corpus.

4.3 Analysis of perspectivization

This section gives a close examination of the perspectivization strategy adopted by the NYT in representing the (un)certainty of CC and GW. The concordance lines of the collocation of GW/CC with two collocates with contradictory meanings (i.e. real and hoax) are further examined. As Boykoff and Boykoff (2004) argue, the adherence to the norm of balanced reporting in US prestige press has resulted in biased coverage of “anthropogenic contributions to global warming and resultant action” (Boykoff and Boykoff 2004: 125). This is what they call “false balance”. Therefore, an examination of the perspectivization strategy in the representations of the (un)certainty of CC or GW can further expose how the contradictory viewpoints are coherently organized and whether “false balance” can still be identified in NYT. A distinction between attribution and averral can be made in examining these concordance lines (Partington 2012; Sinclair 1988). Attribution refers to the explicit attribution of what is being said to an external source, while averral refers to the statement of the viewpoints by the newspaper itself. As Example (3) shows, the statement that CC is real is attributed to hundreds of scientists rather than the news reporter. Therefore, it is an example of attribution rather than averral.

Hundreds of scientists are also telling Mr. Trump in a new letter that climate change is real and needs to be addressed […]. (NYT, 05/12/2016)

An examination of the collocation of real (70) and CC finds that 60 (86%) address whether CC is real or not. All of the 60 concordance lines underline CC as real, and all the statements are attributed to external sources rather than averred by the news reporters themselves. Five groups of external sources can be identified, including “science” (16), “common people” (15), “politics” (13), “business” (4), and “Religion” (1). Examples are as follows:

During his seven and a half years in office, Mr. Obama said, a majority of Americans have come to believe “that climate change is real, that it’s important and we should do something about it”. (Common people) (NYT, 08/09/2016)
Exxon Mobil has acknowledged climate change is real since the mid-2000s. (Business) (NYT, 24/08/2017)
Democrats have long agreed that climate change is real and ought to be addressed, but they have never made it a decisive issue before. (Politics) (NYT, 29/04/2019)
We Are Conservatives and We Believe Climate Change Is Real. (Politics) (NYT, 21/04/2018)
But if the pope can convince some people that climate change is a real and serious problem, that’s a good thing. (Religion) (NYT, 22/09/2015)
Among the targets of the suits was Exxon Mobil, whose own scientists found , as most scientists have, that climate change was real and that human behavior was contributing to it. (Science) (NYT, 11/01/2018)

Therefore, “science”, “common people” and “politics” occur as the three dominant groups of sources. Their similar frequencies can be attributed to the journalistic norm of “balanced” reporting (Boykoff 2008; Boykoff and Boykoff 2004), which requires the relatively equal recognition to multiple sides of contentious issues (Bennett 1996). Therefore, NYT still features the balanced representations of the voices from politics, science, and common people in its representations of CC as real, even though Boykoff (2007) claims that this “false balance” is less noticeable after 2005.

While adhering to the journalistic norm of “balanced” reporting, the news reporters also indicate their stance towards these statements through reporting verbs. The most frequently used reporting verbs in these examples include say* (8), agree* (5), acknowledge* (4), convince* (3), believe* (5), know* (2), tell* (2), and understand* (2). Among them, acknowledge*, accept*, know*, agree*, believe*, understand*, and endorse* are mental process verbs (Halliday 1994), and some of them carry the implicature that the represented statement is true, such as acknowledge*, accept*, know*, and understand* (Haarman and Lombardo 2009).

NYT’s explicit stance can also be revealed through its use of hoax. An examination of the 49 concordance lines of the collocation of hoax and CC finds that 46 (94%) underline that CC is a hoax. However, all of them are attributed to the politicians in the US, the former US President Donald Trump in particular. Besides, NYT also shows disalignment with these statements in three ways. First, scare quotes are often used in these examples to show that these are the words used by the attributed sources and that the news reporter does not share the responsibility for these words (Fairclough 1992, 1995), as in the following:

Mr. Trump has called human-caused climate change a “hoax”. (NYT, 10/11/2016)

Reporting verbs are also used to indicate the news reporter’s disalignment with these viewpoints. The most frequently used reporting verbs in these concordance lines are call* (15), think* (7), claim* (3), argue* (3), denounce* (1), mock* (1), and deride* (1). Most of them are verbal process verbs (Halliday 1994) and suggest that what is stated is personal and malicious, as in the following:

He often mocks the established science of human-caused climate change and dismisses it as a hoax. (NYT, 02/08/2016)
But nowhere is the difference as stark as it is on climate change, which Mr. Trump has mocked as a hoax. (NYT, 05/07/2017)

Finally, the newspaper’s dealignment with these statements can also be perceived through their overt negative evaluation of these statements, as in the following:

And a determined faction even argues that climate change is a hoax, as President Donald Trump has falsely stated at various times. (NYT, 19/07/2019)
Ted Cruz’s argument that climate change is a hoax to justify a government takeover of the world is absurd. (NYT, 20/04/2016)

In these examples, the claim that CC is a “hoax” is often negatively evaluated by such expressions as falsely and absurd. They suggest that the represented statements are not true. In other words, the news reporter does not share the same view with the attributed sources.

The same is true for the collocation of GW and real (18). An examination of all the concordance lines finds that 13 out of 18 (72%) instances state whether GW is real or not. 12 of them underline GW as real, and only 1 occurrence underlines GW as not real. Examples are as follows:

A panel of top American scientists declared today that global warming was a real problem and was getting worse. (NYT, 07/06/2001)
But if Bush says our plan is to acknowledge that global warming is real and create incentives and voluntary commitments, that will be wholly unpersuasive to Europe. (NYT, 08/06/2001)

Similar to the representations of CC, the sources attributed include “politics”, “science”, “common people”. These statements are often introduced by the reporting verbs and some expressions which also carry the implicature that what is stated is a matter of fact, as can be seen from the expressions like declare*, acknowledge*, the mounting evidence, and actually the mainstream view.

Hoax (4) does not appear as a frequent collocate of GW. This is also in contradiction to the statement that GW frequently collocates with hoax (Shi et al. 2020). Like CC, the external sources are also attributed to the politicians in the US.

The Republican presidential contenders regard global warming as a hoax or, at best, underplay its importance. (NYT, 07/09/2011)
Ron Paul of Texas calls global warming “the greatest hoax I think that has been around for many, many years”. (NYT, 07/09/2011)

Therefore, NYT still adheres to the journalistic norm of “balanced” reporting by citing contrarian viewpoints (Boykoff and Boykoff 2004). However, it also seeks to correct “false balance” through showing explicit and implicit alignment with the scientific consensus on the existence of CC and GW and disalignment with these viewpoints that challenge or question the existence of CC and GW (Brüggemann and Engesser 2014, 2017; Schmid-Petri et al. 2017).

5 Conclusion

To sum up, a corpus-assisted discourse study of the representations of CC and GW in NYT reveals both similarities and differences in their representations. The analysis of the topics/themes associated with CC and GW finds that both show the preference for the themes of environment, politics, and science. Nevertheless, the representations of CC tend to be more politicized, while GW shows more preference for science. Meanwhile, CC features the preference for a richer variety of topics associated with the impacts of CC, whereas GW prefers to underline a limited number of topics concerning the causes of GW and the action taken towards it. The analysis of framing reveals that CC and GW tend to be framed as contentious issues, caused by both human and non-human factors, with serious impacts and in need of some actions to address and mitigate it. CC is framed as more contentious as GW. More emphasis is put on the human causes of CC than the human-causes of GW. The impacts of CC are framed as more serious than the impacts of GW. While mitigation strategy is emphasized for both CC and GW, an adaption strategy is highlighted for CC, but a solution strategy is suggested for GW. The analysis of perspectivization strategy finds that while NYT still adheres to the journalistic norm of “balanced reporting”, it corrects the “false balance” by showing explicit and implicit alignment with the scientific consensus on CC and GW and disalignment with these viewpoints which question the scientific consensus on the issue. Future studies can further investigate the use of these two terms in different US news media and overcome the limitation of this study, i.e. GW is less frequently researched in the present study since it is infrequently used in recent years. It would be more desirable to examine whether GW has been gradually replaced by CC in the US news reports.

This study shows the benefits of a corpus-assisted discourse analysis approach in the study of the representations of CC and GW. By giving an extensive examination of a large number of news articles, it provides empirical evidences for the actual use of the two terms in NYT. The use of different corpus analytic tools and methods provides different entry points for the further analysis of the data, thus allowing the analysis of the corpora at different levels (Baker et al. 2008). The incorporation of the theories and methods from discourse analysis can present a more fine-grained description and interpretation of the findings generated by the corpus-analytic tools, and it will overcome the weaknesses of pure automatic computer-assisted text analysis on a large scale (Salway 2017). Although this study further supports the findings of some previous studies in some aspects, it also provides some findings which are inconsistent with the findings of previous studies. Although some studies suggest US news media fail to balanced reporting (Brüggemann and Engesser 2014, 2017; Shi et al. 2020), our finding still demonstrates that US news media still prefer “balanced reporting” but show supportive attitudes towards the scientific consensus. This can be attributed to the different methodologies employed and the different media platforms examined. Future studies on other news media are required to examine whether they show different patterns in the representations of CC and GW and how these different news media affect public understandings of CC and GW.

Corresponding author: Ming Liu, Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China, E-mail:

Funding source: The National Social Science fund Project of China

Award Identifier / Grant number: 17CYY065

  1. Research funding: This work was supported by the National Social Science fund Project of China (grant number 17CYY065).


Akerlof, Karen & Edward W. Maibach. 2011. A rose by any other name …? What members of the general public prefer to call “climate change”. Climatic Change 106(4). 699–710. in Google Scholar

Alexander, Richard. 2010. Framing discourse on the environment: A critical discourse approach. London: Routledge.10.4324/9780203890615Search in Google Scholar

Antilla, Liisa. 2005. Climate of scepticism: US newspaper coverage of the science of climate change. Global Environmental Change 15(4). 338–352. in Google Scholar

Baker, Paul. 2006. Using corpora in discourse analysis. London: Continuum.10.5040/9781350933996Search in Google Scholar

Baker, Paul, Costas Gabrielatos, Majid KhosraviNik, Michał Krzyżanowski, Tony McEnery & Wodak Ruth. 2008. A useful methodological synergy? Combining critical discourse analysis and corpus linguistics to examine discourses of refugees and asylum seekers in the UK press. Discourse & Society 19(3). 273–306. in Google Scholar

Baker, Paul & Tony McEnery (eds.). 2015. Corpora and discourse studies: Integrating discourse and corpora. New York: Palgrave Macmillan.10.1057/9781137431738Search in Google Scholar

Benjamin, Daniel, Han-Hui Por & David Budescu. 2017. Climate change versus global warming: Who is susceptible to the framing of climate change? Environment and Behavior 4(7). 745–770. in Google Scholar

Bennett, W. Lance. 1996. An introduction to journalism norms and representations of politics. Political Communication 13(4). 373–384. in Google Scholar

Bevitori, Cinzia. 2010. Representations of climate change: News and opinion discourse in UK and US quality press: A corpus-assisted discourse study. Bologna: Bononia University Press.Search in Google Scholar

Bohr, Jeremiah. 2020. Reporting on climate change: A computational analysis of US newspapers and sources of bias, 1997–2017. Global Environmental Change 61. 1–12. in Google Scholar

Bolsen, Toby & Matthew A. Shapiro. 2017. The US news media, polarization on climate change, and pathways to effective communication. Environmental Communication 12(2). 149–163. in Google Scholar

Boykoff, Maxwell T. 2007. Flogging a dead norm? Newspaper coverage of anthropogenic climate change in the United States and United Kingdom from 2003 to 2006. Area 39(4). 470–481. in Google Scholar

Boykoff, Maxwell T. 2008. Lost in translation? United States television news coverage of anthropogenic climate change, 1995–2004. Climatic Change 86(1). 1–11. in Google Scholar

Boykoff, Maxwell T. & Jules M. Boykoff. 2004. Balance as bias: Global warming and the US prestige press. Global Environmental Change 14(2). 125–136. in Google Scholar

Boykoff, Maxwell T. & Jules M. Boykoff. 2007. Climate change and journalistic norms: A case-study of US mass-media coverage. Geoforum 38(6). 1190–1204. in Google Scholar

Boykoff, Maxwell T. & J. Timmons Roberts. 2007. Media coverage of climate change: Current trends, strengths, weaknesses. (accessed 3 March 2022).Search in Google Scholar

Broecker, Wallace S. 1975. Climatic change: Are we on the brink of a pronounced global warming? Science 189(4201). 460–463. in Google Scholar

Brüggemann, Michael & Sven Engesser. 2014. Between consensus and denial. Science Communication 36(4). 399–427.10.1177/1075547014533662Search in Google Scholar

Brüggemann, Michael & Sven Engesser. 2017. Beyond false balance: How interpretive journalism shapes media coverage of climate change. Global Environmental Change 42. 58–67.10.1016/j.gloenvcha.2016.11.004Search in Google Scholar

Cheng, Winnie. 2013. Corpus‐based linguistic approaches to critical discourse analysis. In Carol Chapelle (ed.), The encyclopedia of applied linguistics, 1353–1360. West Sussex: Wiley-Blackwell.10.1002/9781405198431.wbeal0262Search in Google Scholar

Chinn, Sedona, P. Sol Hart & Soroka Stuart. 2020. Politicization and polarization in climate change news content, 1985–2017. Science Communication 42(1). 112–129. in Google Scholar

Cotter, Colleen. 2001. Discourse and media. In Deborah Schiffrin, Deborah Tannen & Heidi E. Hamilton (eds.), The handbook of discourse analysis, 416–436. Malden, Mass: Blackwell Publishers.Search in Google Scholar

Dayrell, Carmen & John Urry. 2015. Mediating climate politics: The surprising case of Brazil. European Journal of Social Theory 18(3). 257–273. in Google Scholar

Entman, Robert M. 1993. Framing: Toward clarification of a fractured paradigm. Journal of Communication 43(4). 51–58. in Google Scholar

Fairclough, Norman. 1992. Discourse and social change. Cambridge: Polity Press.Search in Google Scholar

Fairclough, Norman. 1995. Media discourse. London: Arnold.Search in Google Scholar

Feldman, Lauren, P. Sol Hart & Tijana Milosevic. 2015. Polarizing news? Representations of threat and efficacy in leading US newspapers’ coverage of climate change. Public Understanding of Science 26(4). 481–497. in Google Scholar

Ford, James D. & Diana King. 2015. Coverage and framing of climate change adaptation in the media: A review of influential North American newspapers during 1993–2013. Environmental Science & Policy 48. 137–146. in Google Scholar

Friginal, Eric & Jack A. Hardy (eds.). 2020. The Routledge handbook of corpus approaches to discourse analysis. London: Routledge.10.4324/9780429259982Search in Google Scholar

Greenhill, Murni, Zoe Leviston, Rosemary Leonard & Iain Walker. 2014. Assessing climate change beliefs: Response effects of question wording and response alternatives. Public Understanding of Science 23(8). 947–965. in Google Scholar

Grundmann, Reiner. 2021. Using large text news archives for the analysis of climate change discourse: Some methodological observations. Journal of Risk Research 25(3). 395–406. in Google Scholar

Grundmann, Reiner & Ramesh Krishnamurthy. 2010. The discourse of climate change: A corpus-based approach. Critical Approaches to Discourse Analysis across Disciplines 4(2). 125–146.Search in Google Scholar

Grundmann, Reiner & Mike Scott. 2014. Disputed climate science in the media: Do countries matter? Public Understanding of Science 23(2). 220–235. in Google Scholar

Günay, Defne, Emre İşeri, Metin Ersoy & Adeola Abdulateef Elega. 2021. Media framing of climate change action in carbon locked-in developing countries: Adaptation or mitigation? Environmental Communication 15(5). 663–677.10.1080/17524032.2021.1885462Search in Google Scholar

Haarman, Louann & Linda Lombardo (eds.). 2009. Evaluation and stance in war news. London: Continuum.Search in Google Scholar

Halliday, Michael A. K. 1994. An introduction to functional grammar, 2nd edn. London: Arnold.Search in Google Scholar

Jang, S. Mo & P. Sol Hart. 2015. Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Global Environmental Change 32. 11–17. in Google Scholar

Jaworska, Sylvia. 2017. Metaphors we travel by: A corpus-assisted study of metaphors in promotional tourism discourse. Metaphor and Symbol 32(3). 161–177. in Google Scholar

Jaworska, Sylvia. 2018. Change but no climate change: Discourses of climate change in corporate social responsibility reporting in the oil industry. International Journal of Business Communication 55(2). 194–219. in Google Scholar

KhosraviNik, Majid. 2010. Actor descriptions, action attributions, and argumentation: Towards a systematization of CDA analytical categories in the representation of social groups. Critical Discourse Studies 7(1). 55–72. in Google Scholar

Knox, Claire & Peter Jacques. 2016. Hurricanes and hegemony: A qualitative analysis of micro-level climate change denial discourses. Environmental Politics 25(5). 831–852.10.1080/09644016.2016.1189233Search in Google Scholar

Koteyko, Nelya, Rusi Jaspal & Brigitte Nerlich. 2013. Climate change and ‘climategate’ in online reader comments: A mixed methods study. The Geographical Journal 179(1). 74–86. in Google Scholar

Leiserowitz, Anthony. 2006. Climate change risk perception and policy preferences: The role of affect, imagery, and values. Climatic Change 77(1–2). 45–72. in Google Scholar

L’Hôte, Emilie. 2010. New Labour and globalization: Globalist discourse with a twist? Discourse & Society 21(4). 355–376.10.1177/0957926510366212Search in Google Scholar

Lineman, Maurice, Yuno Do, Ji Yoon Kim & Gea-Jae Joo. 2015. Talking about climate change and global warming. PLoS One 10(9). 23–25. in Google Scholar

Liu, Ming. 2017. “Contesting the cynicism of neoliberalism”: A corpus-assisted discourse study of press representations of the Sino-US currency dispute. Journal of Language and Politics 16(2). 242–263. in Google Scholar

Liu, Ming & Chaoyuan Li. 2017. Competing discursive constructions of China’s smog in Chinese and Anglo-American English-language newspapers: A corpus-assisted discourse study. Discourse & Communication 11(4). 386–403. in Google Scholar

Liu, Ming & Ling Lin. 2021. “One country, two systems”: A Corpus-assisted discourse analysis of the politics of recontextualization in British, American and Chinese newspapers. Critical Arts 35(3). 17–34. in Google Scholar

Liu, Ming & Jingxue Ma. 2021. The politics of fear in Hong Kong protest representations: A corpus-assisted discourse study. Journal of Language and Politics 17(1). 37–59.10.1075/jlp.21019.liuSearch in Google Scholar

Liu, Ming & Yiheng Zhang. 2018. Discursive constructions of scientific (un)certainty about the health risks of China’s air pollution: A corpus-assisted discourse study. Language & Communication 60. 1–10. in Google Scholar

Liu, Ming & Jiali Zhong. 2020. Between national and local: Identity representations of post-colonial Hong Kong in a local English newspaper. Discourse, Context & Media 36. 100401.10.1016/j.dcm.2020.100401Search in Google Scholar

Liu, Xinsheng, Arnold Vedlitz & Letitia Alston. 2008. Regional news portrayals of global warming and climate change. Environmental Science & Policy 11(5). 379–393. in Google Scholar

Lorenzoni, Irene & Nick F. Pidgeon. 2006. Public views on climate change: European and USA perspectives. Climatic Change 77(1–2). 73–95. in Google Scholar

McCright, Aaron M. & Riley E. Dunlap. 2000. Challenging global warming as a social problem: An analysis of the conservative movement’s counter-claims. Social Problems 47(4). 499–522. in Google Scholar

Moser, Susanne C. 2014. Communicating adaptation to climate change: The art and science of public engagement when climate change comes home. WIREs Climate Change 5(3). 337–358. in Google Scholar

Nerlich, Brigitte & Nelya Koteyko. 2009. Compounds, creativity and complexity in climate change communication: The case of ‘carbon indulgences’. Global Environmental Change 19(3). 345–353. in Google Scholar

Olausson, Ulrika. 2009. Global warming – global responsibility? Media frames of collective action and scientific certainty. Public Understanding of Science 18(4). 421–436. in Google Scholar

Orna-Montesinos, Concepción. 2015. The discourses of sustainability in news magazines: The rhetorical construction of journalistic stance. Revista Española de Lingüística Aplicada/Spanish Journal of Applied Linguistics 28(2). 442–464. in Google Scholar

Partington, Alan. 2012. The changing discourses on antisemitism in the UK press from 1993 to 2009: A modern-diachronic corpus-assisted discourse study. Journal of Language and Politics 11(1). 51–76. in Google Scholar

Penz, Hermine. 2018. “Global warming” or “climate change”. In Alwin F. Fill & Hermine Penz (eds.), The Routledge handbook of ecolinguistics, 277–292. London: Routledge.10.4324/9781315687391-19Search in Google Scholar

Plass, Gilbert N. 1956. The carbon dioxide theory of climatic change. Tellus 8(2). 140–154. in Google Scholar

Poole, Robert. 2016. A corpus-aided ecological discourse analysis of the Rosemont Copper Mine debate of Arizona, USA. Discourse & Communication 10(6). 576–595. in Google Scholar

Rayson, Paul. 2008. From key words to key semantic domains. International Journal of Corpus Linguistics 13(4). 519–549. in Google Scholar

Reisigl, Martin & Ruth Wodak. 2016. The discourse-historical approach. In Ruth Wodak & Michael Meyer (eds.), Methods of critical discourse analysis, 23–61. London: Sage.10.4324/9781315739342-4Search in Google Scholar

Ruffolo, Ida. 2015. The greening of hotels in the UK and Italy: A cross-cultural study of the promotion of environmental sustainability of comparable corpora of hotel websites. Procedia-Social and Behavioral Sciences 198. 397–408. in Google Scholar

Salway, Andrew. 2017. Data-driven approaches to climate change discourse, illustrated through case studies of blogs and international climate negotiation. In Kjersti Flottum (ed.), The role of language in the climate change debate, 151–170. New York: Routledge.10.4324/9781315456935-9Search in Google Scholar

Schmid-Petri, Hannah, Silke Adam, Ivo Schmucki & Thomas Häussler. 2017. A changing climate of skepticism: The factors shaping climate change coverage in the US press. Public Understanding of Science 26(4). 498–513. in Google Scholar

Schmidt, Andreas, Ana Ivanova & Mike S. Schäfer. 2013. Media attention for climate change around the world: A comparative analysis of newspaper coverage in 27 countries. Global Environmental Change 23(5). 1233–1248. in Google Scholar

Schuldt, Jonathon P., Sara H. Konrath & Norbert Schwarz. 2011. “Global warming” or “climate change”? Whether the planet is warming depends on question wording. Public Opinion Quarterly 75(1). 115–124. in Google Scholar

Sealey, Alison & Lee Oakley. 2013. Anthropomorphic grammar? Some linguistic patterns in the wildlife documentary series Life. Text & Talk 33(3). 399–420. in Google Scholar

Shi, Wen, 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. International Journal of Environmental Research and Public Health 17(3). 1–22. in Google Scholar

Sinclair, John. 1988. Mirror for a text. Journal of English and Foreign Languages 1. 15–44.Search in Google Scholar

Song, Yunya, Zeping Huang, Jonathon P. Schuldt & Y. Connie Yuan. 2021. National prisms of a global phenomenon: A comparative study of press coverage of climate change in the US, UK and China. Journalism. (accessed 3 March 2022).Search in Google Scholar

Stecula, Dominik A. & Eric Merkley. 2019. Framing climate change: Economics, ideology, and uncertainty in American news media content from 1988 to 2014. Frontiers in Communication 4. 1–15. in Google Scholar

Taylor, John R. 2003. Cognitive grammar. Oxford: Oxford University Press.Search in Google Scholar

Villar, Ana & Jon A. Krosnick. 2011. Global warming vs. climate change, taxes vs. prices: Does word choice matter? Climatic Change 105. 1–12. in Google Scholar

Whitmarsh, Lorraine. 2009. What’s in a name? Commonalities and differences in public understanding of “climate change” and “global warming”. Public Understanding of Science 18(4). 401–420. in Google Scholar

Ytterstad, Andreas. 2015. Framing global warming: Is that really the question? A realist, Gramscian critique of the framing paradigm in media and communication research. Environmental Communication 9(1). 1–19. in Google Scholar

Received: 2021-01-10
Accepted: 2022-03-16
Published Online: 2022-04-01

© 2022 Ming Liu and Jingyi Huang, published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

Downloaded on 10.12.2023 from
Scroll to top button