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.

What role does news content play in explaining inter-party hostility? We argue that affective polarization is influenced by exposure to one of the most dominant ways to cover politics: strategy coverage. While previous studies have pointed to the negative consequences of covering politicians’ strategies and campaign tactics, we find that this reporting style decreases out-party hostility. Our findings are based on two separate studies: (1) a survey experiment and (2) a cross-sectional analysis that increases external validity by combining survey data with computational content analysis of the articles respondents were exposed to by their primary news sources throughout the 2016 US presidential campaign (415,604 articles from 157 American news outlets). The results demonstrate that despite the wide criticism of the tendency of journalists to focus on political strategies, such coverage may ease inter-party tensions in American politics.

When the past is contested by political actors, it can play a notable role both in present and in future politics. This is especially true when it comes to the memory of dominant parties, which are part and parcel of political and national history. Focusing on dominant parties in parliamentary democracies, this article examines the memory dynamics of a dominant party after its demise and highlights the importance of memory modes in understanding these dynamics. Using theories of collective memory, it identifies four possible modes of memory in a post-dominance era, suggesting discursive and power-related indications for each mode. The article then utilizes this framework to examine the memory of Mapai, the once-dominant party in Israel. On the basis of this analysis, the authors propose hypotheses concerning the comparative cases of Sweden, Italy, and Japan.

Many researchers consider the presentation of
diverse content as a prerequisite for the news
media to fully exercise their democratic mandate.
While prior news diversity studies have contributed
important theoretical insights, we argue here that
scholarly knowledge of this concept can be significantly
advanced by employing computational methods
for text analysis. Using automated methods,
researchers can increase both the scope of data
being analyzed and the resolution of the analysis.
This article presents a novel framework for analyzing
news diversity consisting of two distinct stages. In
the first stage, a computational text classification
method is used to analyze, at a high resolution, the
attention given in news texts to a broad range of
political and social issues. In the second stage, the
text classifications are aggregated, and the distributions
of media attention to those issues (i.e., news
diversity) are assessed on a large scale. After presenting
the novel approach, we illustrate its usefulness
for testing theoretical hypotheses about news diversity.
We compare the diversity of economic coverage
in three elite and three popular US newspapers
(N = 252,807 articles) and find that a fine-grained
analysis relaxes concerns raised in previous studies
about low content diversity in the popular press.

One may plausibly assume that the current academic interest in narrative research stems from a growing awareness that human beings are by their very nature storytellers, and that the stories we make become part of who we are, be it as individuals or groups. Indeed, narrative analysis has gained wide ground in many fields of the humanities and social sciences. This bibliography article is intended primarily for students and scholars of politics, but it can be of use for readers and researchers from other disciplinary backgrounds in the social sciences. While political scholars may not be among the pioneers that embraced “the narrative turn,” the connection between politics and narratives is of very long standing.

A crucial challenge in measuring how text represents an entity is the need to associate each representative expression with a relevant entity to generate meaningful results. Common solutions to this problem are usually based on proximity methods that require a large corpus to reach reasonable levels of accuracy. We show how such methods for the association between an entity and a representation yield a high percentage of false positives at the expression level and low validity at the document level. We introduce a solution that combines syntactic parsing, semantic role labeling logic, and a machine learning approach—the role-based association method. To test our method, we compared it with prevalent methods of association on the news coverage of two entities of interest—the State of Israel and the Palestinian Authority. We found that the role-based association method is more accurate at the expression and the document levels.