While there is substantial research on COVID-19’s general framing in the news, little is known about the antecedents and moderators of using moral language in communicating the disease to audiences. In this study, we rely on the Model of Intuitive Morality and Exemplars to explore how news media’s attention on COVID-19 and moralizing language in COVID-19 news vary with respect to ultimate (historical pathogen prevalence) and proximate (spread of COVID-19) socio-psychological factors. Specifically, we analyzed 1,024,800 news headlines from 28 countries published throughout 2020 and applied automated content analysis for moral language extraction. Our results provide support for increased media attention and higher levels of moralizing language in COVID-19 news for regions with high historical pathogen prevalence and COVID-19 spread. We discuss the theoretical impact of these findings in view of the socio-psychological relevance of moralizing language for disease-related news and point towards future research directions.
The “replication crisis” in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different software packages, and even different versions of the same package can lead to variable results. Nipype, an open-source Python project, integrates different neuroimaging software packages uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) providing comprehensive software development frameworks and usage information, (2) improving computational efficiency, (3) facilitating reproducibility through sufficient details, and (4) easing the steep learning curve. Despite the rich tutorials it has provided, the Nipype community lacks a standard three-level GLM tutorial for FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level GLM analysis with FSL via Nipype. Next, we point out some undocumented discrepancies between Nipype and FSL functions that led to substantial differences in results. Finally, we provide revised Nipype code in re-executable notebooks that assure result invariability between FSL and Nipype. Our analyses, notebooks, and operating software specifications (e.g., docker build files) are available on the Open Science Framework platform.
In the Hollywood film industry, racial minorities remain underrepresented. Characters from racially underrepresented groups receive less screen time, fewer central story positions, and frequently inherit plotlines, motivations, and actions that are primarily driven by White characters. Currently, there are no clearly defined, standardized, and scalable metrics for taking stock of racial minorities’ cinematographic representation. In this paper, we combine methodological tools from computer vision and network science to develop a content analytic framework for identifying visual and structural racial biases in film productions. We apply our approach on a set of 89 popular, full-length movies, demonstrating that this method provides a scalable examination of racial inclusion in film production and predicts movie performance. We integrate our method into larger theoretical discussions on audiences’ perception of racial minorities and illuminate future research trajectories towards the computational assessment of racial biases in audiovisual narratives.
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