There is very little other scholarship that applies language processing methods to large corpora of articles from The New York Times or other leading papers. Atalay et al. We explore the impact of The New York Times on its readers by examining the general relationship between The Times and public opinion. Though some might contend that only elites read NYT, we have adopted this research strategy for two reasons. Additionally, it is a widely held belief that NYT serves as a general barometer of an agenda-setting agent for American culture Schwarz, Because of these two reasons, we interpolate the relationship between NYT and public opinion from the relationship between NYT and its readers, and we extrapolate that the views of NYT are broadly representative of American media.
Our paper aims to advance understanding of how Americans form their attitudes on China with a case study of how The New York Times may shape public opinion.
We hypothesize that media coverage of foreign nations affects how Americans view the rest of the world. This reduced-form model deliberately simplifies the interactions between audience and media and sidesteps many active debates in political psychology and political communication.
Analyzing a corpus of , articles on China from The New York Times, we quantify media sentiment with BERT, a state-of-the-art natural language processing model with deep neural networks, and segment sentiment into eight domain topics.
We find strong correlations between how The New York Times reports on China in one year and the views of the public on China in the next. The correlations agree with our hypothesis and imply a strong connection between media sentiment and public opinion. We quantify media sentiment with a natural language model on a large-scale corpus of , articles on China from The New York Times published between and To explore sentiment from this corpus in greater detail, we map every article to a sentiment category positive, negative, or neutral in eight topics: ideology, government and administration, democracy, economic development, marketization, welfare and well-being, globalization, and culture.
We do this with a three-stage modeling procedure. First, two human coders annotate randomly selected articles with a total of 18, paragraphs expressing either positive, negative, or neutral sentiment in each topic.
We treat irrelevant articles as neutral sentiments. The model uses a deep neural network with 12 layers. It accepts paragraphs i. We end up with two binary classifiers for each topic for a grand total of 16 classifiers: an assignment classifier that determines whether a paragraph expresses sentiment in a given topic domain and a sentiment classifier that then distinguishes positive and negative sentiments in a paragraph classified as belonging to a given topic domain.
Thirdly, we run the 16 trained classifiers on each paragraph in our corpus and assign category probabilities to every paragraph. As demonstrated in Table 1 , the two classifiers are accurate at both the paragraph and article levels. American public opinion towards China is a composite measure drawn from national surveys that ask respondents for their opinions on China. We collect cross-sectional surveys from to that asked relevant questions about attitudes toward China and incorporate a probabilistic model to harmonize different survey series with different scales e.
For every year, there is a single real value representing American sentiment on China relative to the level in Put another way, we use sentiment in as a baseline measure to normalize the rest of the time series. A positive value shows a more favorable attitude than that in , and a negative value represents a less favorable attitude than that in Because of this, the trends in sentiment changes year-over-year are of interest, but the absolute values of sentiment in a given year are not. As shown in Fig.
This time series is aggregated from cross-sectional surveys from to that asked relevant questions about attitudes toward China with the year of as baseline.
We begin with a demonstration of how the reporting of The New York Times on China changes over time, and we follow this with an analysis of how coverage of China might influence public opinion toward China. The New York Times has maintained a steady interest in China over the years and has published at least 3, articles on China in every year of our corpus.
Figure 2 displays the yearly volume of China-related articles from The New York Times on each of the eight topics since Articles on China increased sharply after and eventually reached a peak around , almost doubling their volume from the s. As the number of articles on China increased, the amount of attention paid to each of the eight topics diverged. Articles on government, democracy, globalization, and culture were consistently common while articles on ideology were consistently rare.
Note that the sum of the stacks does not equal to the total volume of articles about China, because each article may express sentiment in none or multiple topics. While the proportion of articles in each given topic change over time, the sentiment of articles in each topic is remarkably consistent.
Ignoring neutral articles, Figure 3 illustrates the yearly fractions of positive and negative articles about each of the eight topics. The media attitude is measured as the percentages of positive articles and negative articles, respectively.
US—China relation milestones are marked as gray dots. The New York Times express diverging but consistent attitudes in the eight domains, with negative articles consistently common in ideology, government, democracy, and welfare, and positive sentiments common in economic, globalization, and culture. Standard errors are too small to be visible below 1. Similarly, economics, marketization, and culture are covered most commonly in positive tones that have only grown more glowing over time.
This agrees with the intuition that most Americans like Chinese culture. The New York Times has been deeply enamored with Chinese cultural products ranging from Chinese art to Chinese food since the very beginning of our sample. In contrast, welfare and well-being are covered in an almost exclusively negative light.
Topics regarding politics are covered very negatively. Negative articles on ideology, government and administration, and democracy outnumber positive articles on these topics for all of the years in our sample. Though small fluctuations that coincided with ebbs in US—China relations are observed for those three topics, coverage has only grown more negative over time.
Government and administration is the only negatively covered topic that does feature some positive articles. This reflects the qualitative understanding that The New York Times thinks that the Chinese state is an unpleasant but capable actor. Despite the remarkable diversity of sentiment toward China across the eight topics, sentiment within each of the topics is startlingly consistent over time. This consistency attests to the incredible stability of American stereotypes towards China.
If there is any trend to be found here, it is that the main direction of sentiment in each topic, positive or negative, has grown more prevalent since the s. This is to say that reporting on China has become more polarized, which is reflective of broader trends of media polarization Jacobs and Shapiro, ; Mullainathan and Shleifer, To reveal the connection between media sentiment and public opinion, we run a linear regression model Eq.
There is inertia to public opinion. A broadly held opinion is hard to change in the short term, and it may require a while for media sentiment to affect how the public views a given issue. In other words, we inspect lagged values of media sentiment as candidate predictors for public attitudes towards China.
We seek an optimal solution of media sentiment predictors to explain the largest fraction of variance r 2 of public opinion. To reduce the risk of overfitting, we first constrain the coefficients to be non-negative after reverse-coding negative sentiment variables, which means we assume that positive articles have either no impact or positive impact and that negative articles have either zero or negative impact on public opinion.
Secondly, we require that the solution be sparse and contain no more than one non-zero coefficient in each topic:. The solution varies with the number of topics included in the fitting model. As shown in Table 2 , if we allow fitting with only one topic, we find that sentiment on Chinese culture has the most explanatory power, accounting for We run a greedy strategy to add additional topics that yield the greatest increase in explanatory power, resulting in eight nested models Table 2.
The explanatory power of our models increases monotonically with the number of allowed topics but reaches a saturation point at which the marginal increase in variance explained per topics decreases after only two topics are introduced see Table 2.
To strike a balance between simplicity and explanatory power, we use the top two predictors, which are the positive sentiment of culture and the negative sentiment of democracy in the previous year, to build a linear predictor of public opinion that can be written as.
This formula explains For example, in Therefore, overlap between public and individual agendas was greatest in rich countries with little press freedom, such as Israel, and the least in rich countries with high press freedom, such as Switzerland.
The findings show that media can still influence what people think about, but several national and individual factors greatly influence how it happens. Effects are not the same from one country to the next or even from one person to the next. The University of Kansas is a major comprehensive research and teaching university. The university's mission is to lift students and society by educating leaders, building healthy communities and making discoveries that change the world.
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