The Moral Narrative Analyzer (MoNA; https://mnl.ucsb.edu/mona) is a hybrid content analytical platform developed by UCSB’s Media Neuroscience Lab. The primary task of MoNA is to extract latent moral content that permeate a wide range of narratives, spanning movie scripts, online news articles, song lyrics, books, and many more. Previous research suggests that narratives that are moralized and salient in moral conflict are more enjoyed by audiences and are of higher motivational relevance, for example, when predicting message sharing or online moral outrage. To classify the latent moral information contained in textual corpora, MoNA combines natural language processing algorithms with crowd-sourced human intelligence and thereby achieves greater reliability and validity compared to previous annotation procedures.
Via its seamless pipeline to the Global Database of Events, Language, and Tone (GDELT; https://www.gdeltproject.org/ ), MoNA automatically analyzes the moral profile of hundreds of thousands of news articles in near real-time to detect moralization and moral conflict in human communication. By drawing on computational models of human behavior, MoNA monitors when increased moral news frame densities heighten the likelihood of spatiotemporal conflicts in regions around the globe.
In addition to extracting the latent moral content patterns that permeate human communication, MoNA also examines how the particular moral profile of stories contribute to their performance. For example, MoNA currently analyses the dynamical trajectories of moral conflicts in movies to better understand their unique contribution to story liking among audiences
The GDELT interface for communication research (iCoRe, http://icore.mnl.ucsb.edu:5000/icore/ ) introduces a pipeline to access, explore, and analyze the Global Database of Events, Language and Tone (GDELT; Leetaru & Schrodt, 2013). GDELT provides a vast, open source, and constantly updated repository of online news and event metadata collected from tens of thousands of news outlets around the world. Despite GDELT’s promise for advancing communication science, its massive scale and complex data structures have hindered efforts of communication scholars aiming to access and analyze GDELT. We thus developed iCoRe, an easy-to-use web interface that (a) provides fast access to the data available in GDELT (b) shapes and processes GDELT for theory-driven applications within communication research and (c) enables replicability through transparent query and analysis protocols. In addition, iCoRe stores various regional metadata that are of relevance to news reporting, such as press freedom and political bias, historical pathogen prevalence and democracy indices, and many more. By incorporating these features in an open, accessible, and reproducible manner, iCoRe aims to revolutionize how communication scholars analyze and study the content, processing, and behavioral effects of our daily news.
When using iCoRe, please consider citing this article: Hopp, F. R., Schaffer, J., Fisher, J. T., & Weber, R. (2019). iCoRe: The GDELT interface for the advancement of communication research. Computational Communication Research.