AFFILIATE RESEARCH
Prevalence and correlates of irritability among U.S. adults

By David Lazer | August 2024
This study aimed to characterize the prevalence of irritability among U.S. adults, and the extent to which it co-occurs with major depressive and anxious symptoms. A non-probability internet survey of individuals 18 and older in 50 U.S. states and the District of Columbia was conducted between November 2, 2023, and January 8, 2024. Regression models with survey weighting were used to examine associations between the Brief Irritability Test (BITe5) and sociodemographic and clinical features. The survey cohort included 42,739 individuals, mean age 46.0 (SD 17.0) years; 25,001 (58.5%) identified as women, 17,281 (40.4%) as men, and 457 (1.1%) as nonbinary. A total of 1218(2.8%) identified as Asian American, 5971 (14.0%) as Black, 5348 (12.5%) as Hispanic, 1775 (4.2%) as another race, and 28,427 (66.5%) as white. Mean irritability score was 13.6 (SD 5.6) on a scale from 5 to 30. In linear regression models, irritability was greater among respondents who were female, younger, had lower levels of education, and lower household income. Greater irritability was associated with likelihood of thoughts of suicide in logistic regression models adjusted for sociodemographic features (OR 1.23, 95% CI 1.22–1.24). Among 1979 individuals without thoughts of suicide on the initial survey assessed for such thoughts on a subsequent survey, greater irritability was also associated with greater likelihood of thoughts of suicide being present (adjusted OR 1.17, 95% CI 1.12–1.23). The prevalence of irritability and its association with thoughts of suicide suggests the need to better understand its implications among adults outside of acute mood episodes. Learn More >>
Other Affiliate Research

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This paper presents an experimental case study utilizing machine learning and generative AI to audit content diversity in a hyper- local news outlet, The Scope, based at a university and focused on underrepresented communities in Boston. Through computational text analysis, including entity extraction, topic labeling, and quote extraction and attribution, we evaluate the extent to which The Scope’s coverage aligns with its mission to amplify diverse voices.

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The commercial launch of ChatGPT in November 2022 and the fast development of Large Language Models catapulted the regulation of Artificial Intelligence to the forefront of policy debates One overlooked area is the political economy of these regulatory initiatives–or how countries and companies can behave strategically and use different regulatory levers to protect their interests in the international competition on how to regulate AI.
This Article helps fill this gap by shedding light on the tradeoffs involved in the design of AI regulatory regimes in a world where: (i) governments compete with other governments to use AI regulation, privacy, and intellectual property regimes to promote their national interests; and (ii) companies behave strategically in this competition, sometimes trying to capture the regulatory framework.

Multimodal Drivers of Attention Interruption to Baby Product Video Ads
Ad designers often use sequences of shots in video ads, where frames are similar within a shot but vary across shots. These visual variations, along with changes in auditory and narrative cues, can interrupt viewers’ attention. In this paper, we address the underexplored task of applying multimodal feature extraction techniques to marketing problems.
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