Dvir-Gvirsman, S., Waismel-Manor, I., Tsuriel, K., Sheafer, T., Shenhav, S., Zoizner, A., Lavi, L., et al. (2022).
Mediated Representation in the Age of Social Media: How Connection with Politicians Contributes to Citizens’ Feelings of Representation. Evidence from a Longitudinal Study.
Political Communication ,
39 (6), 779-800 . Routledge.
Publisher's Version Kaplan, Y. R., Sheafer, T., & Shenhav, S. R. (2022).
Do we have something in common?Understanding national identities through ametanarrative analysis.
Nations and Nationalism.
Publisher's VersionAbstractMany scholars stress the role national identities play as an essential element that shapes interests and explains political behaviours. Others, however, contend that national identities are too amorphic and highlight the analytical challenge of employing them as a research variable. We propose the use of metanarratives as a theoretical framework that captures the essence of national identities and allows the comparative study of their similarities and differences. Metanarratives are shared dominant stories that guide values, beliefs and behaviours and help communities understand who they are. We develop a new systematic method for measuring their content and present a three-step process for gauging metanarratives. We demonstrate this method on 159 countries, analysing constitution preambles to assess each nation's metanarrative and create a global identity orientation map. We show how this approach enables the classification and comparison of national identities and discuss its potential contribution to further empiric study of national identities.
Levi, E., Mor, G., Sheafer, T., & Shenhav, S. R. (2022).
Detecting narrative elements in informational text.
Findings of the Association for Computational Linguistics: NAACL 2022. Association for Computational Linguistics.
AbstractAutomatic extraction of narrative elements from text, combining narrative theories with computational models, has been receiving increasing attention over the last few years. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to informational texts, specifically news stories. We introduce NEAT (Narrative Elements AnnoTation) – a novel NLP task for detecting narrative elements in raw text. For this purpose, we designed a new multi-label narrative annotation scheme, better suited for informational text (e.g. news media), by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success). We then used this scheme to annotate a new dataset of 2,209 sentences, compiled from 46 news articles from various category domains. We trained a number of supervised models in several different setups over the annotated dataset to identify the different narrative elements, achieving an average F1 score of up to 0.77. The results demonstrate the holistic nature of our annotation scheme as well as its robustness to domain category.