Group leaders play a vital role in divided cities, particularly in local problem-solving and in everyday contestations. Their role as negotiators makes them perfectly positioned to promote urban processes for the group to which they belong but also raises questions regarding their loyalty. Seeking to understand these individuals’ thinking, this study asks how leaders from different groups in a divided city explain their development as leaders. Utilizing a life-story approach, we present a narrative analysis of 40 life-stories, as told by local leaders representing the main social groups in Jerusalem. Our findings suggest that leaders from different groups use distinctive narratives to ensure their relevancy: “The Homecomer,” used by Israeli-Jews; “The Middleman,” used by Palestinian-Arabs; and “The Pathfinder,” used by Israeli Ultraorthodox-Jews. More importantly, we found that all these leaders share a similar mind-set, what we call leadership development as discovery. Indeed, their development includes formative events that differentiate them from their community, helping them to see the divided city from a different perspective and positioning them as leaders. Understanding and acknowledging this spatial aspect in their narratives can be a first step in facilitating group collaborations, empowering local leaders, and even leading to the emergence of new ones. Our implications go beyond divided cities and can be further studied in ordinary cities.
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.
How does the European Union integrate new values into the text of its treaties? A growing body of literature indicates that, in the past three decades, new norms and values have entered the EU's discourse, resulting in what is usually termed ‘normative power Europe’. Yet the research and knowledge to-date about the EU's discursive assimilation of new values and norms is surprisingly poor. As any institutional change, such integration has the potential to undermine the coherence of the EU's identity and thus also its objective to ‘speak with one voice’. This article explores the EU's discursive management of the continuity-versus-change imperative by analysing the integration of new values into the text of its treaties. This issue is addressed based on a quantitative content analysis on the full texts of European founding treaties between the 1950s and 2009. Findings show that the distribution of the EU's values in the text is not uniform: while the language of market economy and democracy is pervasive, the values of peace, European identity, rights and social justice are mentioned less frequently and in restricted linguistic environments. To account for the differences in the integration of values into the EU's treaty discourse, the article develops the notion of a discursive mechanism of differentiated value integration (MDVI). This rationale echoes the logic of differentiation in policy implementation employed by the EU. It is claimed here that, applied in the European discursive arena, MDVI allows radically different readings of the same text. This helps the EU to maintain a coherent value identity while at the same time enabling change.
This article presents a new method and open source R package that uses syntactic information to automatically extract source–subject–predicate clauses. This improves on frequency-based text analysis methods by dividing text into predicates with an identified subject and optional source, extracting the statements and actions of (political) actors as mentioned in the text. The content of these predicates can be analyzed using existing frequency-based methods, allowing for the analysis of actions, issue positions and framing by different actors within a single text. We show that a small set of syntactic patterns can extract clauses and identify quotes with good accuracy, significantly outperforming a baseline system based on word order. Taking the 2008–2009 Gaza war as an example, we further show how corpus comparison and semantic network analysis applied to the results of the clause analysis can show differences in citation and framing patterns between U.S. and English-language Chinese coverage of this war.
Interpreting human stories, whether those told by individuals, groups, organizations, nations, or even civilizations, opens a wide scope of research options for understanding how people construct, shape, and reshape their perceptions, identities, and beliefs. Such narrative research is a rapidly growing field in the social sciences, as well as in the societally oriented humanities, such as cultural studies. This methodologically framed book offers conceptual directions for the study of social narrative, guiding readers through the means of narrative research and raising important ethical and value-related dilemmas.
Has the European Union (EU) succeeded in socializing citizens to support the democratic values it claims to promote? On the face of it, the prevailing skepticism precludes any expectation of a successful socialization of EU citizens to the EU values. Yet, according to the socialization hypothesis, citizens’ support for these values is expected to increase as countries accumulate more years of the EU membership. Using survey data to isolate distinct dimensions of democratic values, we examine differences among countries in this regard, as well as changes within countries over time. Results confirm the socialization hypothesis, showing that support for democratic values is generally higher in countries with more years of the EU membership, and that this support trends upwards over time.