Growing Deviations Between Elite and Non-Elite Media Coverage of Climate Change:  The American Story

doi:10.21203/rs.3.rs-3784008/v1

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Growing Deviations Between Elite and Non-Elite Media Coverage of Climate Change: The American Story

https://doi.org/10.21203/rs.3.rs-3784008/v1

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Empirical research aimed at understanding public awareness and opinion on climate change has focused heavily on media coverage. Nearly all prior media studies focus on the United States and on a small number of elite news sources, including the national newspapers of record. Here we take advantage of a database (MediaCloud) that covers a much larger array of print and word media: 168 million articles about all subjects, derived from 9000 unique U.S. news sources. Coverage of climate change from these “heartland” sources—dominated by state and local news outlets far from the headquarters of national newspapers of record—has risen 144% from 2011 until 2022. Elite news coverage, however, has risen at twice that pace (299%). Both news sources show spikes in coverage related to similar events (e.g. negotiation of the Paris Accords or President Trump’s withdrawal from Paris); both see a decline in coverage when other events become more prominent (e.g., the first COVID-19 cases). Heartland news sources pay close attention to events such as the discussion of climate change by Pope Francis and to right-wing media campaigns; elite news sources are more focused on climate conferences and extreme weather. Over time, the propensity to cover climate change has diverged. In 2011 there were 104 days when the heartland news sources had more coverage of climate change than elite news outlets such as The New York Times and The Wall Street Journal. By 2022 there were only 11 such days. That year, elite news outlets produced roughly three times the coverage of climate change as heartland news outlets, most of which cover state and local matters.

What explains the motivation of governments to take costly actions to address climate change? In many countries, especially democracies, the answer to that question depends heavily on public opinion. While many factors shape public opinion, a rich vein of scholarship has focused empirical attention on the media (Walgrave, Soroka, and Nuytemans 2008; Francke 1995; Yagade and Dozier 1990; Erbring, Goldenberg, and Miller 1980; Tan and Weaver 2007; Commission on Accelerating Climate Action, Communication Working Group 2023; Sisco et al. 2021). Such a focus has been based on the idea that the media is both a shaper of public opinion and a good barometer of what the public thinks (M. T. Boykoff and Rajan 2007; Elias and Nelkin 1996; Programme 2007; Schoenfeld, Meier, and Griffin 1979; Sampei and Aoyagi-Usui 2009; Wonneberger, Meijers, and Schuck 2020; Hart 2011; Sisco et al. 2021). Many studies have looked at overall trends in news coverage about climate change—in general, rising—along with the factors that are associated with such variation.

The literature on media attention is hard to summarize because it is so diverse. Following the approach in Schmidt et al (2013), updated to include recently published studies, we offer one systematic look at the prior scholarship (Table 1). Our emphasis is on the empirical base for each study—media sources and search terms.

Table 1

Summary of sources that studied the trends in climate change-related coverage in U.S. news sources (based on the work of Schmidt et al.).
Authors	Years of Study	U.S. News Outlets Analyzed	Search Terms
(Liu, Huang, and Brown 1998)	1969–2005	New York Times	“Climate Change” or “Global Warming” or “Greenhouse Gas”
(Ravazzani and Maier 2017)	1970–1992	New York Times	Unspecified
(Grundmann and Krishnamurthy, Ramesh 2010)	1980–2007	The Full Lexis Nexis Database	“Climate Change” or “Global Warming” or “Greenhouse Effect”
(Chinn, Hart, and Soroka 2020)	1980–2017	Boston Globe, Chicago Tribune, Denver Post, Houston Chronicle, Los Angeles Times, New York Times, Orange County Register, Philadelphia Inquirer, Tampa Bay Times, USA Today, Washington Post	“Climate Change” or “Global Warming” or “Greenhouse Gas”
(McComas and Shanahan 1999)	1980–1995	New York Times and Washington Post	“Climate Change” or “Global Warming” or “Greenhouse”
(Trumbo 1996)	1985–1995	Christian Science Monitor, Los Angeles Times, New York Times, Washington Post, and Wall Street Journal	“Climate Change” or “Global Warming” or “Greenhouse Effect”
(Grundmann 2006)	1985–2002	New York Times, Washington Post, and Wall Street Journal	“Climate Change” or “Global Warming” or “Greenhouse Effect”
(Brossard, Shanahan, and McComas 2004)	1987–1997	New York Times	“Climate Change” or “Global Warming” or “Greenhouse Effect”
(Boykoff and Boykoff 2007)	1988–2004	Los Angeles Times, New York Times, Washington Post, and Wall Street Journal	“Climate Change” or “Global Warming”
(Holt and Barkemeyer 2012)	1990–2008	Unspecified: 112 “Broadsheet” Global Papers	Unspecified
(Corfee-Morlot, Maslin, and Burgess 2007)	1993–2006	Unspecified: “Major Global Newspapers”	Unspecified
(Schmidt, Ivanova, and Schäfer 2013)	1996–2010	New York Times and Washington Post	(climat* W/5 (chang* OR XXXncreaseXXXhe* OR disaster* OR transform* OR adjust* OR trend* OR warm* OR heat* OR cool* OR variab)) OR (greenhouse W/3 effect) OR ((global OR earth* OR world* OR international* OR hemisphere) W/5 (warm OR heat* OR cool* OR chill)) OR ((temperature W/5 (global* OR earth* OR world* OR international* OR hemisphere) W/8 (XXXncrease OR rising* OR rise* OR decreas*)).
(Shehata and Hopmann 2012)	1998–2007	New York Times and Washington Post	“Climate Change” or “Global Warming” or “Kyoto”
(Nerlich, Forsyth, and Clarke 2012)	2000–2009	New York Times	“Carbon”
(Grundmann and Scott 2014)	2000–2010	The Full Lexis Nexis Database	“Climate Change” or “Global Warming” or “Greenhouse Effect”
(Feldman, Hart, and Milosevic 2017)	2006–2011	New York Times, USA Today, Washington Post, and Wall Street Journal	“Climate Change” or “Global Warming”
(Zamith, Pinto, and Villar 2013)	2009	New York Times	“Climate Change” or “Global Warming” or “Greenhouse Effect”
(Nisbet 2011)	2009–2010	Politico, New York Times, Washington Post, and Wall Street Journal	“Climate Change” or “Global Warming”
(Molder and Calice 2023)	2016–2021	New York Times, USA Today, Washington Post, and Wall Street Journal	“Climate Change” or “Global Warming” or “Greenhouse Gas”

From that perspective, one pattern is clear: nearly all the existing research has concentrated an analytical spotlight on a small number of marquis sources of news, such as The New York Times. These studies reflect a combination of convenience (elite news sources are easy to spot and have long had fully searchable databases) and relevance. Elite news sources are key agenda-setters in policy circles and also frame how other news outlets cover complex topics like climate change. (Many sources simply reprint stories from elite sources; others engage in re-reporting—filing new stories that carry similar messages as the elite news outlet that broke the original story.). As such, many older studies that relied on researchers manually reading through each article individually, understandably, chose to focus their efforts on the most widely circulated, and read, papers of record. Of all the studies on Table 1, only two (Grundmann et al., 2010 and Grundmann and Scott, 2014) looks beyond an elite sample of news sources—in those studies, still, a restricted sample of news outlets covered by Lexis Nexis was used. A few studies in countries other than the United States have attempted to look at non-elite news sources (Ghosh and Boykoff, 2019; Boykoff, 2008; Boykoff and Mansfield, 2008).

In this paper we explore insights that could emerge from empirical research that looks at a much wider array of news sources. We use a new database, MediaCloud, which differs from nearly all earlier databases for its extremely broad coverage. As with earlier studies, we focus on the country that already has been the main subject of analysis—the United States. The expanded coverage of MediaCloud is particularly notable for its inclusion of news outlets based where most Americans live: over the full database examined in this study about 81% of all MediaCloud news articles covered are from the “State and Local” category (see table SI-1 in Supplemental Information “SI”). A significant portion of Americans still rely on local news as their primary source of information; and on average, Americans place a higher trust in their local news outlets when compared with nation-wide elite sources (Knight Foundation 2023).

In this paper we are focused on two questions. First, does looking at a fuller sample of American news sources rather than a narrow slice of elite outlets lead to differences in the news coverage? The answer to that question, we will show, is that coverage is rising but highly variable with notable intense spikes. That leads to a second research question: are there significant differences in the events that appear to drive spikes in climate change-related coverage between elite news outlets and the broader array of heartland news sources? Some of the existing literature on media coverage of climate change has been probing what drives media coverage, such as links between extreme weather events or particular political events and coverage of climate change (e.g., (Saunders, Grasso, and Hedges 2018; Schäfer, Ivanova, and Schmidt 2014). The answers, we suggest, may depend on the type of media coverage.

The centerpiece of this paper is a novel source of primary data: the news-article repository housed by MediaCloud. This is an open-source platform developed across numerous institutions, including the University of Massachusetts Amherst, Northeastern University, and the Berkman Klein Center for Internet & Society at Harvard University. We examine the entire MediaCloud repository from its origin (01/01/2011) through the end of the last full calendar year before we began our analysis (12/31/2022).

Our aim is to be as inclusive as possible. Thus, we built a dataset that included both MediaCloud’s “United States – National” and “United States – State & Local” datasets. In totality, this paper examined 170,477,176 news articles over 12 years (see SI-1). MediaCloud’s coverage has expanded over time (see Fig. 1), with a large expansion in 2018 that roughly doubled the number of annual articles covered in the database. (Future research will need to pay close attention to these shifts in data coverage. For this paper, we note that the proportion of news types—local and national—shifts towards a preponderance of local articles as MediaCloud coverage becomes more inclusive.)

Mindful of the first research question in this paper—differences between elite and non-elite news sources—we then created two distinct datasets. One is what we call “elite” news sources—the five most often the subject of prior empirical research (Table 1): The New York Times, The Wall Street Journal, The Washington Post, USA Today, and The Los Angeles Times. By focusing on these five media sources we aim to use methods aligned with earlier studies rather than opine on whether any particular news source, such as USA Today, is elite in its orientation. The other is what we call “heartland” news coverage—all the news sources included in MediaCloud’s “United States – National” and “United States – State & Local” datasets except for the five elite sources. In American parlance, the heartland is the broad swatch of territory in the middle of the country—geographically and intellectually distinct from more urban, more elite worlds of the coasts. Indeed, most of the coverage in MediaCloud—especialy after the expansion—involves state and local sources that are not typically visible in elite spheres. All told, the narrower elite dataset accounts for only 2% of the stories of the combined datasets (see Fig. 1 and also table SI-1).

Analytically we then follow three steps for each of the two datasets. First, we identify all climate-related coverage, defined as any article that uses the terms “climate change” or “global warming” or “greenhouse gas” or greenhouse effect” in their article headline or body. While this approach may prove expansive (e.g., tagging an article about a climate change in Middle Eastern politics) we spot check the results to find these situations to be quite rare. We also expect that such hits will be unbiased with respect to time or clustering in time. Some scientists and federal agencies have stressed the differences between the terms “climate change” and “global warming” with the former being more encompassing and technical accurate for the broader phenomena in question (U.S. Environmental Protection Agency 2023). We find that the media are, indeed, shifting their vocabulary. In elite news sources in 2011, 52% of all news articles found using the search terms “climate change” or “global warming” used only the term “climate change.” By 2022 that share had risen to 82%. In heartland news sources the pattern is similar: a rise from 52–86%.

Nonetheless, using both terms in our search does not seem to bias our results (see figure SI-2). In addition, this multi-phrase approach is consistent with other studies (e.g. Brossard, Shanahan, and McComas 2004; Grundmann and Scott 2014; and Molder and Calice 2023). For completeness, we report in SI the main results for the paper had we taken a more restrictive search of just “climate change.” The differences are small and don’t materially alter any of the main results reported in this paper (see figure SI-3 and figure SI-7 to figure SI-12).

Second, we compute averages across the entire 11 years of coverage for two intervals. We calculate a six-month average over 22 discrete six-month periods for the full 11 years (six-month buckets were selected as a balanced choice between minimizing the day-to-day noise while still capturing the yearly and inter-year fluctuations). Then we calculate discrete three-day averages across the entire dataset (yielding 1,461 discrete three-day periods). These two intervals allow us to quantify spikes in coverage by calculating short-term (three day) deviations from the background (six-month) pattern of coverage. (For completeness, see equation SI-4 for a sample calculation.)

Having calculated deviations from background we then rank the top fifteen spikes in coverage—the episodes with the highest percent deviation from background six-month rates. For each spike we then use MediaCloud’s keyword analysis and Google News to identify the underlying events that are driving the news cycle for each spike.

As every earlier study has found, attention to climate change is rising. In the full calendar year of 2011, 0.68% of all U.S. news articles (elite and heartland datasets combined) were related to climate change. In 2022 coverage had risen to 1.60%. This growth is notable (135% over 11 years, see Table 2) but extremely volatile as shown in Fig. 2.

Four features stand out: the spike in coverage in late 2015 (the meetings in Paris that yielded the Paris Agreement), the spike in coverage in early 2017 (President Trump’s withdrawal from Paris), the decline in coverage in early 2020 (the shift in media attention to the global pandemic) and the spike in coverage in late 2021 (the 26th Conference of the Parties meetings in Glasgow).

Looking at the two datasets distinctly there are big differences in annual coverage. The heartland coverage saw a 144% increase between 2011 and 2022. The five elite sources saw roughly double that rise: a 299% increase in their volume of climate-related coverage. (Of those five, The New York Times accounts for more of the increase in coverage than any other.) Table 2, below, elaborates on this point and lays out the median, mean, and standard deviation for each dataset by year.

Table 2

Median, means, and standard deviations for climate coverage in the heartland and elite datasets. These data are for the percentage of articles related to climate change, not the total number of articles queried. The values for the total column are not exactly equal to the heartland dataset – just up to three significant figures. The heartland and elite dataset both showed significant variability within each year’s data. By in large, the elite dataset had a higher variability than the full dataset (see Fig. 5 in the SI). This is potentially an indication of a more reactive coverage of climate change from the elite dataset, but it is also possible that the noise in the data is simply higher because the dataset is only looking at five sources.
HEARTLAND				ELITE				TOTAL
Year	Median	Mean	Standard Deviation	Year	Median	Mean	Standard Deviation	Year	Median	Mean	Standard Deviation
2011	0.006	0.007	0.002	2011	0.009	0.010	0.005	2011	0.006	0.007	0.002
2012	0.006	0.007	0.003	2012	0.011	0.012	0.007	2012	0.006	0.007	0.003
2013	0.005	0.006	0.003	2013	0.010	0.011	0.007	2013	0.005	0.006	0.003
2014	0.006	0.006	0.003	2014	0.010	0.011	0.007	2014	0.006	0.006	0.003
2015	0.007	0.008	0.005	2015	0.011	0.014	0.009	2015	0.007	0.008	0.005
2016	0.007	0.008	0.003	2016	0.013	0.012	0.008	2016	0.007	0.008	0.003
2017	0.008	0.009	0.005	2017	0.016	0.018	0.011	2017	0.008	0.009	0.005
2018	0.007	0.008	0.004	2018	0.012	0.014	0.007	2018	0.007	0.008	0.004
2019	0.013	0.014	0.005	2019	0.029	0.032	0.016	2019	0.013	0.014	0.005
2020	0.010	0.011	0.006	2020	0.022	0.026	0.013	2020	0.010	0.011	0.006
2021	0.014	0.015	0.006	2021	0.028	0.032	0.017	2021	0.014	0.015	0.006
2022	0.015	0.016	0.005	2022	0.037	0.040	0.017	2022	0.015	0.016	0.005

With such a noisy dataset there will be random days when heartland coverage of climate change exceeds coverage from the elite sources. If the medians of the two datasets are similar then those random days will be frequent; as the medians diverge such occurrences will be more rare. Figure 3 shows those occurrences for the entire dataset (the full pie) and by year (slices). The driver of these patterns is seen in Table 2. In 2011 the median coverage of climate change in elite newspapers was only 50% higher than the median in heartland news sources. In 2022 the elite median was more than double the heartland median. As time passes the chance that heartland news sources, on any day, will have more to say about climate change becomes quite rare. That shift is evident, as well, in the average coverage levels. In 2011, the heartland sources discussed climate change proportionally more on 24% of the days – in 2022, it was 2.8% (Fig. 3).

Figure 5 shows the six-month background rate of coverage for climate change along with three-day spikes. We then report the top 15 events for the heartland dataset (table 3) and the elite dataset (table 4).

Table 3

Heartland dataset - top 15 three-day spikes in climate change-related coverage
Event	Three-day period (with center date)	Percent deviation from contemporaneous 6-month average
President Trump Announces U.S. Withdrawal from Paris Accords	JUNE 2, 2017	467%
2014 UN Climate Summit	SEPTEMBER 22, 2014	397%
Heartland Institute Issues Billboard on Climate Change	MAY 5, 2012	366%
Barack Obama Second Inaugural Address	JANUARY 21, 2013	264%
Opening of Paris COP21	NOVEMBER 28, 2015	202%
Durban COP17	DECEMBER 10, 2015	182%
Conclusion of COP21	DECEMBER 13, 2015	181%
Continuing Coverage of COP21	DECEMBER 1, 2015	180%
Pope Francis Comments on Climate Change and UN General Assembly Meeting	SEPTEMBER 22, 2015	176%
EPA Proposal to Cut Power Plant Emissions	JUNE 3, 2014	176%
August-Complex Wildfires	SEPTEMBER 14, 2020	166%
Pope Francis Comments on Climate Change	JUNE 19, 2015	159%
Opening of Durban COP17	DECEMBER 7, 2011	159%
Release of the 5th Assessment of the IPCC	SEPTEMBER 27, 2013	157%
G20 Summit Following President Trump's Withdrawal from Paris	JULY 8, 2017	156%

Table 4

Elite dataset - top 15 three-day spikes in climate change-related coverage
Event	Three-day period (with center date)	Percent deviation from contemporaneous 6-month average
President Trump Announces U.S. Withdrawal from Paris Accords	JUNE 2, 2017	483%
2014 UN Climate Summit	SEPTEMBER 22, 2014	399%
Paris COP21	DECEMBER 1, 2015	273%
Barack Obama Second Inaugural Address	JANUARY 21. 2013	268%
EPA Proposal to Cut Power Plant Emissions	JUNE 3, 2014	262%
U.S. and China Agree to Climate Change Deal	NOVEMBER 12, 2014	244%
Continuing Coverage of COP21	DECEMBER 7, 2015	224%
Durban COP17	DECEMBER 7, 2011	224%
Doomsday Clock Moved 1 Minute Closer to Midnight	JANUARY 9, 2012	192%
President Obama Delivers State of the Union	JANUARY 24, 2012	188%
Opening of Durban COP17	DECEMBER 4, 2011	183%
EPA Releases New Methane Emission Standards	MAY 11, 2016	178%
Typhoon Haiyan Hits the Philippines	NOVEMBER 11, 2013	176%
Slate Of Legislation Proposed by New Congress Dealing with EPA Regulations and Nuclear Power Plants	FEBRUARY 1, 2011	176%
August-Complex Wildfires	SEPTEMBER 14, 2020	175%

Our statistical study is, to our knowledge, the broadest coverage of news sources in the US to date.

Expanding coverage leads to some findings that are robust when compared with elite news outlets. Both heartland and elite sources show upward trends. This is consistent with results already widely reported (e.g., AAAS Commission, etc): the public is paying more attention to climate change, and so are the media. Heartland coverage more than doubled (increase in 144%) from 2011 and 2022. The five elite sources rose three-fold (299%) over that same period, when measured as the fraction of all news sources that addressed climate change. Using just a narrower search on the term “climate change” yielded a 227% growth for the heartland sources and a 449% growth for the elite sources.

While the daily data are volatile, even semi-yearly averages show some volatility. There are six periods for the elite dataset and seven periods for the heartland dataset when semi-yearly coverage declined (Figure SI-6). Those include:

early 2013 when coverage appears to shift to the early days of the second term of the Obama administration, a period with little attention to climate change.
early 2016 as the US national election began to heat up;
early 2018 as the high average coverage of 2017—driven by President Trump’s withdrawal from the Paris Agreement—abated;
throughout the first half of 2020 when news coverage shifted, for a while, to the COVID-19 pandemic.

Of these declines, the largest for the heartland sources occurred in the latter half of 2013, where the six-month average dropped 39% from the previous six-month average. For the elite sources, the largest drop occurred in the first half of 2020 as Covid-19 began to spread in the United States. The shift in attention to the pandemic resulted in the six-month average in elite sources dropping 41% from their previous six-month average. The covid crash was not long lived, and the upward trend resumed within a year.

The single most striking finding in this study is the divergence between the two datasets over time. In the first four years of the study (2011–2015) on any given day there was a 20% probability that the fraction of total daily stories addressing climate in heartland news sources would exceed the fraction in elite sources. Elite news outlets have always given more attention to climate change than heartland outlets. But in the early 2010s those differences were small enough that one day in five climate change would play a more prominent role in the average American’s news feed than it did for the elite. In the last four years of our study the differences were much more extreme: only 5% of days saw more coverage in heartland sources than the elite sources.

When looking at the events that drive extreme peaks in coverage of climate change there is a lot of similarity across heartland and elite news sources. The top two spikes in both datasets were increases in coverage by a factor of 4 to five due to President Trump’s withdrawal from Paris in 2017 and the convening of the UN Climate Summit in September 2014, which set the final stage for the Paris conference a few months later (Tables 3 & 4). Both types of new outlets give extensive coverage to the Paris conference (2021) and the climate conference four years earlier, in Durban, that framed the diplomatic road that ended in Paris. (The Durban conference in 2011, known as COP17, was the first major reset after the disastrous COP15 in Copenhagen that ended in disarray with no agreement on the road ahead. By Durban the road was coming into focus.).

There are notable differences in the spikes in coverage. In the heartland dataset there is extensive coverage of a series of billboards erected by the heartland Institute (no relation) in 2012 opposing action on climate change. Heartland news sources also gave a lot of attention to two clusters of comments on climate change by Pope Francis in 2015 in the runup to the negotiation of the UN Climate summit and the issuance that year of the Pope’s encyclical Laudato Si. In the elite dataset there is heavy coverage of the US-China climate summit in 2014, which many analysts see as the key political agreement that made it possible to conclude, a year later in Paris, a similar agreement with nonbinding emission pledges that included all countries. The elite dataset also saw a spike in coverage when the Bulletin of the Atomic Scientists changed its Doomsday Clock to a mere one minute before midnight. Diplomatic agreements with China and the machinations of atomic scientists don’t attract as much attention in the heartland.

Our findings parallel other divergences in American attitudes about climate change—between left and right, and between Washington and the rest of the country. While we focus on the incidence of coverage of climate change, not the content of the articles, there is a strong divergence over the period of study.

Finally, this study points to several areas for fruitful additional research. Here we focus on three.

First, if MediaCloud is to be a richer source of data for research on climate change then more research is needed to understand underlying shifts in coverage and coding within the dataset. There are significant changes in total coverage in the dataset that could affect trends and other empirical patterns—even when those trends are computed (as we do) in percentage of total coverage. Among the empirical patterns that need more explanation—and accounting for in statistical assessments—are bursts of new coverage notably in 2013 and 2018 (see Fig. 1). In addition, there are brief unexplained drops in the number of articles available for query in the database. In particular, there was a three-week period in January of 2022 where the number of U.S. articles available for query dropped to just 5% of the number of articles available in the months preceding and proceeding the drop (figure SI-1).

Despite the normalization of our data to the percentage of articles addressing climate change as the unit of our analysis we still find an underlying correlation between the total number of articles queried, per day, and the percentage of articles on that day related to climate change. Simple linear models using total articles per day as the independent variable and the percentage of articles related to climate change as the dependent variable, show that the number of article queried per day explains 20% of the variance in the dataset. The relationship between the two variables has a slight positive correlation. This relationship is only relevant for the heartland dataset, as the elite dataset had a roughly consistent number of articles queried per year (Table SI-1). Despite this growth in non-elite sources we still find that the elite and heartland data sets diverge over time. Nonetheless, this temporal trend in the heartland data set needs explanation.

There are clear structural questions that remain with the MediaCloud dataset. A lot of spadework is needed, and with such a huge dataset the spades will be hefty. Related, our search method—like nearly all other media pattern studies—is a dragnet approach with possibly lots of data bycatch. We are assuming that the errors are normally distributed and unbiased, but more work—with finer attention to search terms and their catch—is needed.

Second is the matter of explanation. We looked at fifteen spikes in coverage in each of the two datasets and then used brute force to identify the story that was driving the cycle. But spike analysis is quite limited in what it can say, especially in the early years of the data set where a small number of articles over a three-day period can exceed the threshold that qualifies as a “spike” in our formulation. With automated text analysis it should be possible to do more granular analysis of spikes in news coverage and also, perhaps, begin to explain larger patterns in the stories that catch the attention of editors and readers. (Empirical research on letters to the editor, where those exist, could also help round out the picture by showing what motivated readers do in response). Multivariate analysis of the entire data set—not just spikes—can add to explanation but will require particularly careful attention to the construction of dependent variables given the large changes in coverage included in the MediaCloud data set. Explanation should also look at geographical focus of non-elite news outlets, political leanings, and other factors that plausibly could explain patterns. Better explanation will require looking more closely at the content of coverage, not merely incidence.

Third is the need to disaggregate original stories from those that are simply reprinted. Wire services (like the Associated Press), in particular, generate a lot of reprinting through which the lines between elite and heartland might be blurred because news articles in multiple places have common origin. Reprinting should affect measures of incidence and may also be a fruitful way to explore where and how connections between elite and heartland news sources remain more durable. Such a reprinting effect would produce a tendency toward convergence between the elite and heartland datasets—suggesting that the divergence results we present here are robust.

The United States is early in a long process of developing and implementing a national policy strategy on climate change (American Academy of Arts and Sciences 2023). What it does at home will affect how it is seen abroad and its ability to get other nations to take a collective approach to climate change. The political viability of the American national policy depends, in large part, on what American think about climate change. For too long the efforts to understand what question have focused on elites and not the whole country.

Acknowledgements

We thank Kathleen Hall Jamieson and Max Boykoff for comments on a draft and the American Academy of Arts and Sciences Commission on Accelerating Action on Climate change for related discussions about the role of media in shaping public opinion. We are grateful to MediaCloud for accesss to their data set and guidance on how to use the data. DGV acknowledges support from the Brookings Institution and the Deep Decarbonization Initiative of UC San Diego.

Ethics approval and consent to participate

This article does not contain any studies with human or animal participants performed by any of the authors.

Consent for publication

The authors consent.

Competing Interests

Not applicable

Author contributions

Parker Bolstad and David G. Victor contributed equally.

Funding

Parker Bolstad Not applicable.

David G. Victor

The Brookings Institution Dr. David G. Victor

Electric Power Research Institute Dr. David G. Victor

School of Global Policy and Strategy,

University of California, San Diego

Dr. David G. Victor

Availability of data and materials

This article does not contain any studies with human or animal participants performed by any of the authors.

I confirm I have included a data availability statement in my main manuscript file

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Growing Deviations Between Elite and Non-Elite Media Coverage of Climate Change: The American Story

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