Do the leaders of minority communities in divided cities influence group members’ expressed willingness to engage politically with rival groups? Studies typically link group members’ willingness to engage with rival groups to direct contact between individuals from opposing groups. However, such contact is problematic in divided cities, wherein opportunities to interact are scarce and frowned upon. Focusing on the contested urban space of Jerusalem, we find indications that the diverse nature of community leadership in East Jerusalem can influence Palestinian residents’ attitudes towards political engagement with Israeli authorities via municipal elections. The ‘middlemen’ role can explain community leaders’ influence in divided cities. They facilitate indirect contact between their constituents and the other group’s members or institutions. Our analysis employs original data from a public opinion survey conducted among Palestinian residents of East Jerusalem immediately prior to the Jerusalem 2018 municipal elections. It has ramifications regarding urban governance for other divided cities.

Many 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.

Automatic 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.

The collaborative effort of theory-driven content analysis can benefit
significantly from the use of topic analysis methods, which allow researchers
to add more categories while developing or testing a theory. This additive
approach enables the reuse of previous efforts of analysis or even the
merging of separate research projects, thereby making these methods
more accessible and increasing the discipline’s ability to create and share
content analysis capabilities. This paper proposes a weakly supervised topic
analysis method that uses both a low-cost unsupervised method to compile
a training set and supervised deep learning as an additive and accurate
text classification method. We test the validity of the method, specifically
its additivity, by comparing the results of the method after adding 200
categories to an initial number of 450. We show that the suggested method
provides a foundation for a low-cost solution for large-scale topic analysis.