Still united in diversity? The longer a country is part of the EU, the stronger its citizens support liberal democratic values

What effect does EU membership have on the values of citizens? Drawing on recent research, Odelia Oshri, Tamir Sheafer and Shaul Shenhav assess the extent to which the EU has been successful in instilling the democratic values in its own citizens that it claims to promote externally. The research demonstrates a strong connection between a state’s duration of EU membership and the degree to which its citizens adhere to liberal democratic values, suggesting that while multiple national identities exist across the EU, it is nevertheless possible to unite citizens under the umbrella of democratic ideology

CompRes: A Dataset for Narrative Structure in News

This paper addresses the task of automatically detecting narrative structures in raw texts. 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 news articles, motivated by their growing social impact as well as their role in creating and shaping public opinion. We introduce CompRes -- the first dataset for narrative structure in news media. We describe the process in which the dataset was constructed: first, we designed a new narrative annotation scheme, better suited for 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); then, we used that scheme to annotate a set of 29 English news articles (containing 1,099 sentences) collected from news and partisan websites. We use the annotated dataset to train several supervised models to identify the different narrative elements, achieving an $F_1$ score of up to 0.7. We conclude by suggesting several promising directions for future work.Comment: Accpted to the First Joint Workshop on Narrative Understanding, Storylines, and Events, ACL 202

Navigating high-choice European political information environments : a comparative analysis of news user profiles and political knowledge

The transition from low- to high-choice media environments has had far-reaching implications for citizens’ media use and its relationship with political knowledge. However, there is still a lack of comparative research on how citizens combine the usage of different media and how that is related to political knowledge. To fill this void, we use a unique cross-national survey about the online and offline media use habits of more than 28,000 individuals in 17 European countries. Our aim is to (i) profile different types of news consumers and (ii) understand how each user profile is linked to political knowledge acquisition. Our results show that five user profiles – news minimalists, social media news users, traditionalists, online news seekers, and hyper news consumers – can be identified, although the prevalence of these profiles varies across countries. Findings further show that both traditional and online-based news diets are correlated with higher political knowledge. However, online-based news use is more widespread in Southern Europe, where it is associated with lower levels of political knowledge than in Northern Europe. By focusing on news audiences, this study provides a comprehensive and fine-grained analysis of how contemporary European political information environments perform and contribute to an informed citizenry

A Weakly Supervised and Deep Learning Method for an Additive Topic Analysis of Large Corpora

The collaborative effort of a 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. Additivity also enables the reuse of previous efforts or the merging of separate research projects, thereby increasing the accessibility of such methods and the ability of the discipline to create shareable content analysis capabilities. This paper proposes a weakly supervised topic analysis method, which combines 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 is a solid starting point for a low-cost and additive solution for a large-scale topic analysis

Role-based association of verbs, actions, and sentiments with entities in political discourse

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

Clause analysis:Using syntactic information to automatically extract source, subject, and predicate from texts with an application to the 2008-2009 Gaza War

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 showthat 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

Who is in the news? Personalized political news in comparative perspective

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