key: cord-1012487-8cxgd7l7
authors: Evers, Noah F.G.; Greenfield, Patricia M.; Evers, Gabriel W.
title: COVID‐19 shifts mortality salience, activities, and values in the United States: Big data analysis of online adaptation
date: 2021-02-09
journal: Hum Behav Emerg Technol
DOI: 10.1002/hbe2.251
sha: 1038c76f93edd91b5d4336a09ff810764b1beac1
doc_id: 1012487
cord_uid: 8cxgd7l7

What is the effect of a life‐threatening pandemic at the societal level? An expanded Theory of Social Change, Cultural Evolution, and Human Development predicts that, during a period of increasing survival threat and decreasing prosperity, humans will shift toward the psychology and behavior typical of the small‐scale, collectivistic, and rural subsistence ecologies in which we evolved. In particular, subjective mortality salience, engagement in subsistence activities, and collectivism will all increase, while the aspiration to be wealthy will decrease. Because coronavirus has forced unprecedented proportions of human activity online, we tested hypotheses derived from the theory by analyzing big data samples for 70 days before and 70 days after the coronavirus pandemic stimulated President Trump to declare a national emergency. Google searches were used for an exploratory study; the exploratory study was followed by three independent replications on Twitter, internet forums, and blogs. Across all four internet platforms, terms related to subjective mortality salience, engagement in subsistence activities, and collectivism showed massive increases. These findings, coupled with prior research testing this theory, indicate that humans may have an evolutionarily conditioned response to the level of death and availability of material resources in society. More specifically, humans may shift their behavior and psychology toward that found in subsistence ecologies under conditions of high mortality and low prosperity or, conversely, toward behavior and psychology found in modern commercial ecologies under conditions of low mortality and high prosperity.

What is the effect of a life-threatening pandemic on human behavior and cultural values? We have expanded the Theory of Social Change, Cultural Evolution, and Human Development (Greenfield, 2009) to predict that, during a period of increasing survival threat and decreasing prosperity, humans will shift toward the psychology and behavior typical of the smallscale, rural subsistence ecologies in which human beings evolved (Greenfield & Brown, under revision; Park, Twenge, & Greenfield, 2014) .

Our rationale for these predictions is the following: during a period of increasing survival threat and decreasing material resources, an evolutionary instinct causes a psychological shift toward the mindset and behavior typical of an earlier form of society, composed of small-scale, rural environments. In such environments, the volatility of day-to-day existence renders scarce resources a considerable concern and mortality highly salient. (We borrow the term mortality salience from Terror Management Theory [Greenberg et al., 1992] .) Therefore, if social conditions suddenly shift toward features of those environments-decreasing material resources and increasing survival threat -it follows that thoughts, behaviors, and values will adjust accordingly. We posited that shifts would be particularly evident in four features that define lifestyles in those ecologies: amplified subjective salience of death, increased engagement in subsistence activities, greater identification with collectivistic values, and reduced aspiration for wealth. Tragically, the societal conditions of the COVID-19 pandemic created a natural experiment in which this theoretical analysis and the empirical hypotheses derived from it could be tested.

Based on the idea that language provides a window into concerns, values, and behavior, we used linguistic terms from Google searches and social media posts in the United States to index the predicted psychological and cultural shifts in the time of coronavirus.

An underlying assumption was that the use of linguistic representations on a national level would provide insight into national culture and culture change brought about by the pandemic.

Ironically, because the pandemic transposed much of human activity online, the Internet became a valuable platform for exploring the hypothesis that, in the time of the novel coronavirus, mortality would be more salient, values more collectivistic, subsistence activities more frequent, and wealth less important. Hence, we predicted that, during the pandemic, the frequency of mortality-related terms such as "death" and "cemetery" would increase in Google searches and on social media throughout the United States. We predicted similar increases in terms representing various subsistence activities (e.g., "grow vegetables," "baking bread") and collectivistic values (e.g., "sacrifice," "share"). Because people living in subsistence ecologies have to deal with having enough resources to survive, rather than seeking to accumulate wealth, we also predicted that the use of terms representing materialistic values (e.g., "Rolls Royce") would decline during the pandemic.

Two big data studies evaluated these predictions by comparing the national prevalence of selected words and phrases on the internet before and after President Trump's declaration that COVID-19 was a national emergency. The first exploratory study used Google searches as the data source. The second confirmatory study used social media.

Analyzing data from Twitter, blogs, and internet forums, Study 2 provided three independent replications of Study 1. Our contribution to the literature is to demonstrate the paradox that, on the one hand, adaptation to the pandemic involves concerns, activities, and values associated with a historically earlier way of life, and, on the other hand, that these shifts are expressed on the canvas of the newest emerging technologies.

In the next section we provide background on the theory and empirical findings that led to our predictions.

1.1 | Theory of Social Change, Cultural Evolution, and Human Development

In the Theory of Social Change, Cultural Evolution, and Human Development, the fundamental determinant of cultural values and behavior consists of sociodemographic variables (Greenfield, 2009 (Greenfield, , 2016 (Greenfield, , 2018 . The most basic distinction at the sociodemographic level represents subsistence versus commercial ecologies. Subsistence ecologies are characterized by small villages, short life expectancy (including high infant mortality rate), low material resources, collectivism, and basic survival activities-people produce their own food, shelter, and clothing. These are the environments in which human beings evolved.

In commercial ecologies-a product of cultural evolution-most people live in urban environments; people have substantially longer life expectancies, access greater material resources, and purchase rather than produce their own food, shelter, and clothing.

Low life expectancy, high mortality rate, and scarce material resources create high survival threat and high mortality salience; these are central features of a subsistence ecology. In contrast, low survival threat and low mortality salience are central to commercial ecologies.

However, the commercial ecology of the United States has been impacted by the coronavirus pandemic creating high survival threat.

We, therefore, tested the hypothesis that the pandemic's survival threat has made mortality more salient and that online behavior would reflect this increased salience and its downstream effects on activities and values.

The municipio (county) of Zinacantán in the Mexican state of Chiapas exemplifies community response to high mortality and low life expectancy in a subsistence environment. In Zinacantán in the 1960s, families grew their own food, wove their clothing, and constructed their housing. At that time, about 35% of children died before age 4 (Brazelton, Robey, & Collier, 1969) . Zinacantecs created elaborate and expensive burial sites and regularly provided food on graves to feed dead family members (Greenfield, 2004) . This elaborate cultural structuring of death exemplifies our proposal that high mortality rates and low life expectancy produce mortality salience, a psychological product of intense survival concerns.

In ecologies with low mortality rates and high life expectancy (i.e., low survival threat), we propose that mortality salience is reduced and, correlatively, persistent fear of death and preparations for death are also reduced. For example, in the United States, a highly commercial ecology, focus on a dead body is often minimized through cremation rather than burial, and memorial services, which, by definition, do not include the body, are often structured as celebrations of life rather than opportunities to grieve over death.

In a subsistence ecology, activities center around subsistence needs, such as food, shelter, and clothing. Many ethnographic field studies evince these priorities (e.g., Bolin, 2006; Bowser & Patton, 2008; Greenfield, 2004; Hewlett, Lamb, Shannon, Leyendecker, & Schölmerich, 1998; Vogt, 1969) . In a commercial, high-tech ecology, subsistence needs are most often purchased.

For instance, during coronavirus stay-at-home orders, food markets and home construction were considered "essential businesses." In a commercial ecology, subsistence activities, like food production, are monetized and expanded beyond serving a solely survival purpose.

In subsistence ecologies, the collectivistic values of helping, sharing, and giving are central, and behavior enacts these values. For example, in learning how to weave clothing, the learner relies on help from an older family member (Childs & Greenfield, 1980) ; in hunting for food, prestige comes from sharing game with other members of the community (Dowling, 1968) ; in celebrating religious holidays, giving away resources to the whole community, rather than accumulating wealth, is an important source of cachet (Vogt, 1969) . Conversely, in a commercial ecology, individual accomplishment, personal property, and accumulation of wealth, rather than helpfulness, sharing, and giving, are dominant values (Raeff, Greenfield, & Quiroz, 2000; Uhls & Greenfield, 2012) .

Increasing life expectancy, decreasing mortality rates In subsistence ecologies, life expectancy is short, and survival threats are common. Global life expectancy was age 27 in 1800 and remained at that level for the next hundred years. However, as medicine and science advanced, life expectancies increased (Roser, Ortiz-Espina, & Ritchie, 2013 (Lee, 2003) .

In the United States (the geographical area of our study), as a result of advancements in scientific and technological developments in public health and medicine, life expectancy increased, and mortality decreased from 1900 to 2018, with a notable reversal of those trends in 1917 during the Spanish Flu pandemic (National Center for Health Statistics, n.d.) .

The dominant direction of social change in our globalized world has been towards ever greater urbanization, commercialization, wealth, and monetization of activity (Greenfield, 2009 (Greenfield, , 2016 . The movement towards a more commercial and wealthy ecology brings with it reduced mortality (Hajat, Kaufman, Rose, Siddiqi, & Thomas, 2011) ; reduced survival concerns (LeVine, Dixon, Leiderman, Keefer, & Brazelton, 1994) ; fewer subsistence activities (Greenfield, 2004) ; more individualistic values (Santos, Varnum, & Grossmann, 2017) ; and increased importance of becoming rich (Park et al., 2014) .

Even so, social change can go in the opposite direction, as it did in the Great Recession. As wealth decreased, adolescents became more communitarian in their values and less materialistic (Park et al., 2014) .

Until the coronavirus pandemic, the United States had not, since the 1917 flu pandemic, known survival threat on a mass scale. COVID-19's palpable danger to survival has led to significantly increased mortality salience and fear of death (Cable & Gino, 2020; Menzies & Menzies, 2020) . Survival concerns have been caused not only by the coronavirus itself but also by economic retraction. In April, the official unemployment rate reached 14.7%, the highest it has been since the Great Depression, putting 23 million Americans out of jobs and slashing the pay of twice as many workers as the Great Depression (Iacurci, 2020; Long & Van Dam, 2020) . This economic devastation left almost one-third of the country unable to pay for subsistence basics like adequate food, medical care, or utility bills (Karpman, Zuckerman, Gonzalez, & Kenney, 2020) . Aside from potentiating the increasing survival threat, a hallmark condition of subsistence ecologies, this economic retraction provides the other essential feature of a subsistence ecology: scarcity of material resources.

These shifts in the direction of a subsistence ecology led us to hypothesize that response to the pandemic would entail (1) increased mortality salience, (2) more engagement in subsistence activities,

(3) increased collectivism, and (4) decreased materialism. Study 1 was an exploration; it compared the frequency of relevant terms used in Google searches in the United States before and during the pandemic to produce a list of terms that were representative of the predicted conceptual shifts. Study 2 aimed to replicate the findings of Study 1, drawing data from social media sites in the United States before and during the pandemic. Study 2 provided three independent replications of Study 1 by utilizing national data from three different social media: Twitter, internet forums, and blogs. Approximate estimates indicate 87.3% of Americans use Google (2020), 22% use Twitter (2019), 20% use internet forums (2015), and 10% create blog content (based on 2016 forecast) (Pendry & Salvatore, 2015; Statcounter, 2020; Statista Research Department, 2016; Wojcik & Hughes, 2019) . Because of our method of examining whether the predicted trends were consistent across Google, Twitter, internet forums, and blogs, and because such high proportions of the American population use one or more of the analyzed internet platforms, the study was essentially a comprehensive population-level analysis of the United States.

All four hypotheses had been confirmed by survey research carried out during stay-at-home orders in California and Rhode Island (Greenfield & Brown, under revision) . With the current research, we test the generality of these findings and explore the extent to which the internet is a reflection of changing psychological dynamics on a societal level-in this case, psychological reactions to the conditions created by the coronavirus pandemic.

Language provides a window into the thoughts, feelings, and actions of individuals. Because society is composed of individuals interacting and communicating through language, the study of language on a mass scale provides, in principle, a window into psychological processes on a societal level. So, why has the collection and analysis of massive language datasets in order to develop understandings about human societies emerged only recently? The main reason is that, before the end of the 20th century, existing technology made largescale word analyses impossible (Pennebaker & Chung, 2014) . With the advent of desktop computing and the internet, the linguistic behavior of whole societies could, for the first time, be studied on an unprecedented scale. However, because communication technologies have expanded exponentially, society-wide studies using linguistic data became possible only quite recently as the quantity of data in existence exploded. At the time of our study (2020), 2018-2020 had produced the majority of the world's data (Holst, 2020) . Over the same time period, social media users went from being a minority of the world's population to the majority, moving considerable proportions of their social interactions online and recordable for potential analysis (Clement, 2020; Datareportal, 2020) . Big data studies on the Precedents for studying historical, cultural change through big data sources Google's digitized collection of roughly 8 million books, 6% of the total books ever published, has been analyzed by numerous scholars to measure historical and cultural changes around the world (e.g., Greenfield, 2013; Michel et al., 2011; Skrebyte, Garnett, & Kendal, 2016; Twenge, Campbell, & Gentile, 2012; Zeng & Greenfield, 2015; Younes & Reips, 2019) .

Scholars also avail themselves of newspapers for big data. Using a database with millions of newspapers, researchers analyzed U.S. newspapers from 1900 to 2000; they measured historical frequency changes in variables composed of terms associated with specific value categories, a methodology we use in the current research.

The researchers established the predictive validity of this methodology in two ways. First, they found that the value-representing variables had a significantly stronger correlation with objective indicators of corresponding value-expressive behavior versus noncorresponding value-expressive behavior. For example, the terms representing the value of stimulation ("excitement," "novelty," and "thrill") highly correlated over time with the number of films released, and the terms representing the value of self-direction ("independence," "freedom,"

and "liberty") highly correlated over time with voting participation. Second, they found that the terms correlated with expected behaviors exhibited meaningful variations at expected major time points. For example, the stimulation terms dramatically increased during the roaring twenties and decreased when the Great Depression began. Similarly, the terms representing the value of security ("security," "safety,"

and "protection") dramatically increased at the start of World War II, dramatically decreased at the end of World War II, and then increased up until the conclusion of the Cold War (Bardi & Calogero, 2008) .

Many studies have used Google Trends to measure change over time (Jun, Yoo, & Choi, 2018) . More specific to our research, Google Trends data have been used to assess the psychological and behavioral effects of COVID-19 in the U.S. population (Goldman, 2020) . This research has revealed several COVID-19-induced phenomena, including changes in food-related activities. Evidence of the shift toward a subsistence ecology, search terms related to cooking increased, while searches for restaurants decreased.

"Smaller" studies extract big data from social media in order to measure the effects of COVID-19

As coronavirus forced the greatest proportion of interpersonal relationships in human history from offline interactions to online, several researchers have seen COVID-19 and its effects as a unique opportunity to study psychological changes through big data on social media.

These studies used much smaller quantities of data than the present research, but they are still extremely informative. One particularly relevant study analyzed the language used by 17,865 active Chinese users on Weibo, one of the biggest Chinese social media platforms, before and after the Chinese declaration of outbreak of COVID-19, in order to assess several psychological metrics (Li, Wang, Xue, Zhao, & Zhu, 2020) . In line with our hypotheses, their results indicate that, based on word frequencies, individuals shifted toward adaptation to a subsistence ecology. For example, Weibo users showed more concern with death after COVID-19 was declared than before the declaration.

They also showed more interest in religion, another value that is stronger in rural ecologies than urban societies (Greenfield, 2013) . 

Google Trends transforms its search frequency for each term in a given region and time period into a metric referred to as "search interest" or just "interest." The interest metric is a percentage based on a set time frame. Each day's search interest is calculated as a percentage of peak interest during whatever time frame has been selected. For example, an interest value of 100 means that on that date, the search term reached its peak popularity for the time frame, and a value of 50 means that on that date, the term was half as popular as its peak popularity in the same time frame (Google Trends, 2020). Our time frame was the period of January 3, 2020-May 22, 2020. Within this time frame, we compared the 70-day period before President Trump declared COVID-19 an emergency (January 3-March 12) with the 70-day period after the emergency declaration (March 14-May 22).

March 13, the day of the emergency declaration, was considered a transitional day and was excluded from the analysis.

Our dependent variables were, for every day of the time frame and for every term searched, the interest value for that term. The independent variable was before versus after Trump's emergency declaration. Our unit of analysis was the day, so that, by means of t-tests, 70 daily measurements of search interest before the emergency declaration were compared for every term with 70 daily measurements of search interest after the emergency declaration for the same term.

We considered using both paired-samples and independentsamples t-tests and compared them for every dependent variable. All statistically significant shifts were maintained in both the independent-samples and paired-samples t-tests. However, we decided to use the independent-samples t-tests in reporting results because we had no basis for thinking that Day 1, 70 days before the beginning of the pandemic, would be more similar to Day 1 of the pandemic than it was to other days during the pandemic; no basis for thinking that Day 2, occurring 69 days before the pandemic, would be more similar to Day 2 of the pandemic than to other days during the pandemic, etc. Therefore, we concluded that the two sets of days-70 days before the pandemic and 70 days during the pandemic-were more independent than correlated. Where matching does not produce a substantial correlation, independent-samples t-tests are preferred (Zimmerman, 1997) .

With t-tests performed on time series, the issue of independence of adjacent days arises, as this independence is a required condition for a t-test. Because day, rather than person, served as our unit of analysis and because data from adjacent days consisted of input from large but constantly varying samples of internet users all over the United States (rather than data from the same people over a series of days), we considered that our dependent variables met the criterion for the independence of data from adjacent days.

For this exploratory study, we estimated that the time frames of 70 days before and 70 days after President Trump's emergency declaration would be long enough to ensure that the behaviors were stable for both periods, despite day-to-day variability. Because the n in our statistical analyses is based on number of days sampled, we estimated that a total n of 140 days would provide a reasonable sample for statistical reliability. Based on our exploratory findings in Study 1, we did a power analysis that confirmed this sample size of 140 days to be appropriate for Study 2. Details will be reported in Section 3.1.3.

While it was important to achieve sufficient statistical power by having a large enough sample of days, it was also important to avoid a time sample that was too long. Here the considerations were substantive rather than statistical. Because we conceptualized mortality salience as a key variable, we did not want the time period to be so long that individuals' subjective mortality salience would decrease through factors such as greater understanding of preventative practices, reduced hospital crowding, more widespread testing availability, or lifting of stay-at-home orders. In fact, initial stay-at-home orders were still in full or partial effect in 18 states on the last day of our study period (National Academy for State Health Policy, n.d.); the continuation of stay-at-home orders provided evidence that our pandemic time period had not gone on too long.

For the Google Trends exploration, words and phrases were selected based on three criteria; all of the criteria had been used in prior research (Greenfield, 2013; Zeng & Greenfield, 2015) . The first criterion was theory relevance. This criterion meant that the guiding principle for selecting particular words was that they represented concepts central to the theoretically-based predictions summarized in the introduction.

The second criterion was that only high-frequency terms were selected. Methodologically, this was a necessary criterion because "[Google] Trends only show data for popular terms" by filtering out data on terms with low search volumes (FAQ about Google Trends data, n.d.). Conceptually, selecting for popular terms meant that the terms would be significantly characteristic of U.S. culture as a whole and not subject to minor influences that would bias the results (Greenfield, 2013) .

The third criterion was that the words and phrases had to have a relatively narrow range of meanings, so that they were not ambiguous. Narrowing the semantic interpretations of the words and phrases helped ensure that a particular word or phrase did not have many uses in contexts irrelevant to the psychological or behavioral element that the word or phrase was representing. For example, the term "will" was excluded from the analysis of mortality-related terms because of ambiguous meaning.

Before carrying out t-tests, we compared the variances of each word and phrase in the 70 days before Trump's emergency declaration with the variances of the same words and phrases in the 70 days after his emergency declaration. The Levene test for Equality of Variances revealed that 21 out of 30 terms had significantly different variances in the two time periods. If one compares the standard deviations (SD column) for the two time periods for each term in Tables 1-4, the unequal variances are apparent. In line with the recommendation of Ruxton (2006) , we used Welch's t-test, also known as the unequal variance t-test, for comparing word and phrase frequencies before and during the pandemic for both the unequal and equal variance cases. Because the unequal variance/Welch's t-test factors in the specific standard deviations of the samples being compared, the effective degrees of freedom associated with the Welch's t-test can deviate from that of the equal variance t-test where the df is always n − 2; in this case, n − 2 would be 138 (140 days minus 2) (Sawilowsky, 2002) .

Also, when unequal variances are taken into account, dfs have noninteger rather than whole-number values. Thus, in all cases in Tables 1-4, dfs show deviations from 138 to varying degrees and have non-integer values. All t-tests were two-tailed. The reader may also notice that degrees of freedom differ noticeably from term to term. In the unequal variance t-test, degrees of freedom depend on standard deviations and because standard deviations varied dramatically from term to term, degrees of freedom varied from term to term.

We would also like to make two points relating to p-values.

First, we believe that it is informative to present our p-values at the .0000000001 level, rather than cutting them off at the more usual p < .001, in order to more accurately demonstrate the unusually high level of statistical significance of our findings. Second, there is a growing body of literature indicating that p-values should not carry as much weight with big data studies because, as the sample size grows, the effects become more likely to reach statistical significance (Lin, Lucas Jr, & Shmueli, 2013; Demidenko, 2016) . However, in our design, the sample size is relatively small because our unit of analysis is the day. Hence, we compare the 70 days before President Trump's emergency declaration with the 70 days after it. This comparison yields a total sample size of 140. Thus, our study's big data component is the large frequency that each data point is based on, rather than the number of data points. Because of this sample size control, our extraordinarily small p-values provide essential information on the results' strength. In order to further demonstrate the strength of the observed effects, we also calculated Cohen's d for each before-after comparison in order to provide an estimate of the magnitude of each difference.

The expanded Theory of Social Change, Cultural Evolution, and Human Development found support in the Google Trends results for each of the four hypotheses: 2.2.1 | Hypothesis 1: Mortality salience, represented by specific Google search terms, will increase during the pandemic Compared with before the COVID-19 pandemic, mortality salience has significantly increased during the pandemic; this finding is indicated by a rise in Google searches for many terms related to contemplating and preparing for one's own death or the deaths of others:

"cemetery"/"cemeteries," "survive," "fear of death," "death," and "bury" all rose significantly after President Trump's emergency declaration. Effect sizes range from medium to large. After the inception of the pandemic, Google searches significantly increased for many words associated with collectivism: "sacrifice,"

"share," "help," and "give." Table 3 presents t-values, degrees of freedom, p-values, and effect sizes associated with each of these comparisons. Again, all effect sizes were in the medium to large range.

In testing the hypothesis of increasing collectivism in the pandemic, we also tried using family terms such as "mother," "father,"

and "grandparents," given that family values are central to collectivism in subsistence ecologies. However, family terms did not show significant trends during the pandemic, so those data are not shown here.

2.2.4 | Hypothesis 4: Materialism, as represented by specific Google search terms, will decline during the pandemic Google searches for many words associated with being rich and the aspiration to wealth significantly decreased: "spend," "Lamborghini,"

"Rolls Royce," and "Ferrari." For each of these search terms, Table 4 shows 

After we identified the words that emerged with significant predicted shifts from the Google Trends exploration, data on each word's daily mentions on each of three social media types for the specified time frame were collected using the Talkwalker tool. (In one case where two forms of the same root word ["cemetery" and "cemeteries"]

showed the same statistically significant directional change in Google searches before and after COVID began, we selected the singular form for the Talkwalker analysis.)

We then grouped the terms into a composite variable for each hypothesis. We averaged the daily mentions of each word comprising the composite variable for each day of the "before" time frame and each day of the "after" time frame. We then averaged the daily averages of each composite variable over the 70 days before Trump's emergency declaration to find the mean daily mentions for that composite variable before the pandemic. In similar fashion, we averaged the daily averages of each composite variable over the 70 days after

Trump's emergency declaration to find the mean daily mentions for that composite variable during the pandemic. These scores were then compared statistically as described in Section 3.1.4.

The same comparison periods were used in the Talkwalker study as the Google Trends study. In addition to the substantive considerations identified in Study 1, we used the results of our exploratory study of Google searches to confirm that 70 days before and 70 days after President Trump's emergency declaration provided sufficient statistical power. Our calculations indicated that, to achieve a power of .8, the maximum time sample needed to detect an effect at the .05 level of significance for any of the words used in both studies would be 62 days before and 62 days after Trump's emergency declaration (Brant, n.d.) . Hence, a sample of 70 days before and 70 days after the declaration provided more than sufficient statistical power.

As in the Google Trends study and for the same reasons, we analyzed the Talkwalker data with two-tailed independent-samples t- pandemic.

In the main analysis, the dependent variables were composites Again, we followed the suggestion of Ruxton (2006) and used the unequal variance t-test for both the equal and unequal variances cases.

As noted in Study 1, the unequal variance t-test is based on effec- Again, similar to Study 1, because of the unusually high levels of statistical significance and our sample size control, we report p-values out to 10 decimal places. We have also calculated Cohen's d for each

Talkwalker before-after comparison in order to provide an estimate of the magnitude of each shift.

3.2.1 | Hypothesis 1: The representation of mortality will become more salient on social media during the pandemic

The results confirmed this hypothesis. The terms making up the composite mortality salience variable ("survive," "cemetery," "fear of death," "death," and "bury") showed highly significant increases with large effect sizes on all three types of social media: Twitter, internet forums, and blogs. This hypothesis was confirmed, as the terms making up the composite subsistence activity variable ("farmland," "farm," "grow vegetables,"

"seeds," "shovel," "garden," "grow plants," "recipes," "baking bread,"

"sourdough," "cooking directions," "cook," "sewing machine," "sew," "tools," and "Home Depot") had significant increases with large effect sizes across Twitter, internet forums, and blogs. (See Table B5 Table B6 for statistical analyses of the individual terms on blogs.)

The data confirmed this hypothesis, with the terms making up the composite collectivism variable ("sacrifice," "share," "help," and 

The data did not confirm this hypothesis as no social medium showed a significant decrease for the terms composing the aspiration to be rich ("spend," "Lamborghini," "Rolls Royce," and "Ferrari").

Twitter had 34,074.4 mean daily mentions (SD = 6,949.1) for the composite variable representing the aspiration to be rich terms before the emergency declaration. This number increased to 35,233.7 (SD = 6,481.9) afterwards. This effect was not significant, t(137.34) = 1.02, ns. Nor was there a significant change for any of the individual terms tested. (See Table B10 Table B11 Table B12 for statistical analyses of the individual terms on blogs.)

The table shows that mentions of "spend" increased significantly, while mentions of "Ferrari" decreased significantly, both with medium effect sizes.

In national samples from Google searches, Twitter, internet forums, and blogs, we confirmed theoretically derived predictions that COVID-19 would increase mortality salience, engagement in subsistence activities, and collectivistic values. Given concerns about replicability in psychological science (e.g., Makel, Plucker, & Hegerty, 2012) , it is notable that we first carried out an exploratory study and then replicated findings with three independent big data samples. Another notable feature of the results are the many large effect sizes. In fact, many effect sizes are not just unusually large, but huge. These effect sizes testify to the power of the theory to make scientifically important predictions, as well as to the power of the pandemic to lead to socially significant change. At the outset of coronavirus, the dramatic shift toward collectivism would have been quite challenging to predict, and there were actually substantial behavioral indications that the pandemic would make Americans more individualistic. Hoarding, a distinctly individualistic behavior, taking more than necessary from the common good and keeping it for oneself, was predominant. Even before Trump declared COVID-19 a national emergency, the nation was in a "grocery-hoarding frenzy (D'Innocenzio & The Associated Press, 2020)."

The news was full of iconic images of shoppers in long lines at supermarkets waiting to get in right when they opened, and supermarket shelves were wiped out of essentials (Manning-Schaffel, 2020;

Zagorsky, 2020). On the flip side, sellers were using it as a time to engage in the most brute capitalism they could: In response to the increased demand, N95 mask listings went from $18.20 in mid-January to $199.99 at the end of February; a dozen Purell bottles selling at $30 in January skyrocketed to $159.99 by March 3; and shoppers were being quoted astronomical shipping fees up to $5,000 for next-day air (Tyko, 2020) .

What transformed Americans from the hyper-individualists of the onset of the pandemic to valuing collectivism as documented in our study? We would argue that they adapted to living in a society with a high mortality rate. At the time of Trump's announcement, a little over 50 people in the United States had died from coronavirus, 10 times less than would have died by the next week, and nothing compared to the 2,770 that became America's peak death rate in the 70 days following the emergency declaration (Centers for Disease Control and Prevention, 2020; The COVID Tracking Project, 2020). This massive increase in societal mortality appears to have caused death to become significantly more salient in the American mind, as indicated by this study. Our theory predicted that this salience would cause an adaptive shift resulting in a traditionally individualistic society shifting significantly toward collectivistic values.

Development is also unique in being able to predict the significant increase in subsistence activities. Aside from the initial panic and hoarding of toilet paper and water, there was never any time during the pandemic when there might be a real need to become self-sufficient. Food delivery never stopped; no shortage of water ever occurred; supermarkets and home repairs were deemed essential services; almost everything was available online. Taking a big-picture approach, we conclude that the only major difference in our society's commercial functions was that an unprecedented number of transactions moved online, and goods came as deliveries. Nonetheless, the American people engaged in significantly increased subsistence activities across various domains. Because these shifts happened so quickly-within the span of a couple of months-we conclude that these responses reflect evolutionarily conserved instinctual adaptations to changing environmental conditions. 4.1 | "The Largest Movement in U.S. History" in light of this study's findings During the pandemic, America witnessed the "largest movement in U.S. History" (Buchanan, Bui, & Patel, 2020 (Buchanan et al., 2020) . This turnout for a single continuous protest on a particular issue far surpassed the Women's March of 2017, which had been the largest protest movement prior with three to five million participants (Buchanan et al., 2020; Waddell, 2017 (Greenfield, 2013) . The most obvious difference between these two situations was the rate of ecological How lasting are these changes? Residents of the United States adapted quickly to the sudden COVID-19-induced change in ecological conditions. Therefore, when these conditions reverse, they will likely adapt in the opposite direction, at a rate dependent on the speed of the ecological reversal. In order to test this prediction, this study should be replicated after the United States recovers from the pandemic. The study design will have to be a bit different as the country will likely have a slow recovery, so that researchers would not measure whether there were significant changes in a short 10-week period as we did, but would, instead, examine the rate of psychological and behavioral change as the United States recovers over a longer period of time. However, despite our prediction that the population as a whole will readapt to the commercial ecology as it previously existed, prior research has shown that economic conditions present in one's adolescence and emerging adulthood have a lifelong impact on people's values (Bianchi, 2016) . Hence, we predict that the ecological conditions created by the COVID-19 pandemic will leave an enduring effect on American adolescents and young adults.

4.3 | The prediction that the aspiration to be rich would decline did not replicate on social media It surprised us that the aspiration to be wealthy did not replicate on social media because, in Greenfield and Brown's surveys in Rhode

Island and California, participants reported that their interest in becoming rich declined after the pandemic began (Greenfield & Brown, under revision) ; and, in our first study, aspiring to be rich seemed to decline so significantly in Google searches. The most likely explanation for these seemingly paradoxical findings is that the search terms representing the aspiration to be rich were not a valid representation of the concept as a whole. They were "spend," "Lamborghini,"

"Rolls Royce," and "Ferrari." "Spend" does not necessarily represent being rich, but rather the purchasing of goods, which Americans still had to do at a generally unchanged level as the subsistence activities they were participating in would not provide sufficient goods for their family.

As for "Lamborghini," "Rolls Royce," and "Ferrari," these do represent the aspiration to be rich as they are blatant status symbols.

However, buying luxury cars only represents one specific way to aspire to be rich, and if the majority of the population does not care much about cars, then the behavior of those words would not reflect on the aspiration to be rich as a concept. This American lack of interest in luxury cars may be the case, as "Lamborghini," "Rolls Royce," and "Ferrari" were substantially less used terms even before the pandemic compared with other variables. For example, before 

We have no known conflict of interest to disclose.

The peer review history for this article is available at https://publons. 

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Note: The above table and all the following tables present each term's mean daily mentions for the period before President Trump's emergency declaration (70 days before March 13, 2020) and the period after President Trump's emergency declaration (70 days after