Publications

In this introductory chapter, we sketch out our own subjective appraisal of the field of computational social science (CSS) in three parts. First, we trace the field’s history from its origins in agent-based modeling in the late 1990s, through the “Web 2.0” revolution and then to the present day. Second, we offer our perspective on the current state of CSS, summarizing the many ways in which exciting progress has been made, as well as questioning what we have accomplished. And third, we identify five challenges and potential opportunities for the future development of CSS: industry-academic partnerships, research infrastructure, integrative thinking, open science, and digital ethics. We conclude that CSS has a bright future, fueled in part by the large and growing array of compelling problems in the world that blend society and computation, and in part by the diverse, youthful, and energetic makeup of the global CSS community.

Scientists provide important information to the public. Whether that information influences decision-making depends on trust. In the USA, gaps in trust in scientists have been stable for 50 years: women, Black people, rural residents, religious people, less educated people and people with lower economic status express less trust than their counterparts (who are more represented among scientists). Here we probe the factors that influence trust. We find that members of the less trusting groups exhibit greater trust in scientists who share their characteristics (for example, women trust women scientists more than men scientists). They view such scientists as having more benevolence and, in most cases, more integrity. In contrast, those from high-trusting groups appear mostly indifferent about scientists’ characteristics. Our results highlight how increasing the presence of underrepresented groups among scientists can increase trust. This means expanding representation across several divides—not just gender and race/ethnicity but also rurality and economic status.

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Conspiratorial thoughts as a cognitive aspect are understudied outside small clinical cohorts. We conducted a 50-state non-probability internet survey of respondents age 18 and older, who completed the American Conspiratorial Thinking Scale (ACTS) and the 9-item Patient Health Questionnaire (PHQ-9). Across the 6 survey waves, there were 123,781 unique individuals. After reweighting, a total of 78.6 % somewhat or strongly agreed with at least one conspiratorial idea; 19.0 % agreed with all four of them. More conspiratorial thoughts were reported among those age 25–54, males, individuals who finished high school but did not start or complete college, and those with greater levels of depressive symptoms. Endorsing more conspiratorial thoughts was associated with a significantly lower likelihood of being vaccinated against COVID-19. The extent of correlation with non-vaccination suggests the importance of considering such thinking in designing public health strategies.

How does Google Search direct people to information about their elected officials?

To answer this, we conducted daily searches for members of the US House of

Representatives from all 435 US congressional districts and DC between September

1 and December 31, 2020, resulting in 20.1 million search engine results pages

(SERPs) and 302 million search results. We find that these search results are

dominated by a small number of mainstream sources (eg. Twitter, Wikipedia),

with the top seven domains accounting for 64.2% of all results. There was no

significant difference in the partisanship of search results depending on whether the

member whose name was searched was a Democrat or Republican. Additionally,

we found a clear prioritization of politician-controlled social media, government,

and personal websites over news media, local news outlets over national ones, and

reliable news over unreliable news. We observed a lack of sensitivity to search

location, where searching for a given member’s name on the same day but from

different locations yielded similar results.

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The production–consumption gap on social media is a consistent finding across time, platforms,

and cultural contexts: A small minority of highly active users produce the majority

of online political content, while the majority of users consume content passively and remain

largely silent. Online content thus reveals only the tip of an iceberg, from which citizens and

scholars alike are apt to draw incorrect inferences regarding the submerged mass of public

opinion. This has substantive as well as methodological consequences for social media research,

which must be taken into account when designing studies to describe and understand

how social media use relates to content exposure, public opinion, and political behavior, and

when designing and testing pro-democratic interventions.

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Complex contagion rests on the idea that individuals are more likely to adopt a behavior if they experience social reinforcement from multiple sources. We develop a test for complex contagion, conceptualized as social reinforcement, and then use it to examine whether empirical data from a country-scale randomized controlled viral marketing field experiment show evidence of complex contagion. The experiment uses a peer encouragement design in which individuals were randomly exposed to either one or two friends who were encouraged to share a coupon for a mobile data product. Using three different analytical methods to address the empirical challenges of causal identification, we provide strong support for complex contagion: the contagion process cannot be understood as independent cascades but rather as a process in which signals from multiple sources amplify each other through synergistic interdependence. We also find social network embeddedness is an important structural moderator that shapes the effectiveness of social reinforcement.

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Is there a relationship between depression and political evaluations? Building on existing work, we argue that experiencing depressive symptoms will positively correlate with supporting a populist politician and negatively correlate with supporting a nonpopulist officeholder. We evaluate these predictions with data from the United States, focusing on Donald Trump and Joe Biden. Our data are consistent with our hypotheses, and, as expected, we find particularly strong relationships for Democratic respondents. The results highlight the importance of considering mental health when studying the approval of politicians both in and out of office. We conclude with a discussion of next steps for a research agenda on depression and political evaluations.

Complex contagion rests on the idea that individuals are more likely to adopt a behavior if they experience social reinforcement from multiple sources. We develop a test for complex contagion, conceptualized as social reinforcement, and then use it to examine whether empirical data from a country-scale randomized controlled viral marketing field experiment show evidence of complex contagion. The experiment uses a peer encouragement design in which individuals were randomly exposed to either one or two friends who were encouraged to share a coupon for a mobile data product. Using three different analytical methods to address the empirical challenges of causal identification, we provide strong support for complex contagion: the contagion process cannot be understood as independent cascades, but rather as a process in which signals from multiple sources amplify each other through synergistic interdependence. We also find social network embeddedness is an important structural moderator that shapes the effectiveness of social reinforcement.

The transmission of communicable diseases in human populations is known to be modulated by behavioral patterns. However, detailed characterizations of how population-level behaviors change over time during multiple disease outbreaks and spatial resolutions are still not widely available. We used data from 431,211 survey responses collected in the United States, between April 2020 and June 2022, to provide a description of how human behaviors fluctuated during the first 2 y of the COVID-19 pandemic. Our analysis suggests that at the national and state levels, people’s adherence to recommendations to avoid contact with others (a preventive behavior) was highest early in the pandemic but gradually—and linearly—decreased over time. Importantly, during periods of intense COVID-19 mortality, adaption to preventive behaviors increased—despite the overall temporal decrease. These spatial-temporal characterizations help improve our understanding of the bidirectional feedback loop between outbreak severity and human behavior. Our findings should benefit both computational modeling teams developing methodologies to predict the dynamics of future epidemics and policymakers designing strategies to mitigate the effects of future disease outbreaks.

Importance  Screening measures of depressive symptoms (eg, 9-item Patient Health Questionnaire [PHQ-9]) are increasingly used in surveys and remote applications, where shorter versions would be valuable.

Objective  To derive shorter versions of the PHQ-9 that maximize the variability in total depressive symptom severity captured.

Design, Setting, and Participants  This survey study used data from 4 waves of a 50-state nonprobability web-based survey conducted between November 2, 2023, and July 21, 2024. Survey respondents were aged 18 years or older. The first survey wave data were used to identify shortened question subsets capturing variance in the PHQ-9 and estimating a PHQ-9 score of 10 or higher. Resulting models (eg, 3-item version of the PHQ [PHQ-3]) were validated in subsequent survey waves.

Main Outcome and Measure  Performance of PHQ-3 in the full sample and across subgroups of age, gender, race and ethnicity, and educational levels. Depressive symptom severity was measured with the PHQ-9 (total score range: 0-27, with a score ≥10 indicating moderate or greater depressive symptoms).

Results  In the 4 survey waves, there were 96 234 total participants (mean [SD] age, 47.3 [17.1] years; 55 245 [57.4%] identifying as women). In the full sample, 4401 participants (4.6%) identified as Asian American, 12 699 (13.2%) as Black or African American, 9776 (10.2%) as Hispanic or Latino, and 65 309 (67.9%) as White individuals, with 4049 (4.2%) who identified as having other race or ethnicity. Among these participants, the mean (SD) PHQ-9 score was 6.5 (6.6), and 25 411 (26.4%) met the criteria for moderate or greater depressive symptoms (PHQ-9 score ≥10). The optimal 3-item version, PHQ-3, used items 2 (subject: depressed mood), 6 (self-esteem or failure), and 1 (interest), yielding a Cronbach α of 0.88 (95% CI, 0.88-0.88) and Pearson correlation with the PHQ-9 total score of 0.93 (95% CI, 0.93-0.94). At a threshold of 3 or greater, the PHQ-3 sensitivity was 0.98 (95% CI, 0.97-0.98) and specificity was 0.76 (95% CI, 0.75-0.76) for moderate or greater depressive symptoms. Performance was consistent across sociodemographic subgroups and survey waves.

Conclusions and Relevance  This survey study of US adults identified a 3-item scale that remained highly correlated with the full PHQ-9 instrument. The reduced set of questions could enable more widespread and efficient incorporation of depressive symptom measurement in general population samples.

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