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

HOW TO DO THINGS WITH “VALUES”: A CROSS-LINGUISTIC ANALYSIS OF THE MEANINGS AND FUNCTIONS OF A CORE CONCEPT ON TWITTER

The term _values_ is prominent in political discourse. Yet its ubiquity is matched by its vagueness, especially when considering usage among different cultures. Twitter provides a promising venue for exploring the contested political discourse surrounding this term. In this paper, we study the use of the term values on Twitter across different language communities. We ask: _what meanings do users ascribe to the term “values”?_ and _what are the social and political discursive functions of the term?_ For both questions, we will explore cross-cultural similarities and differences. We compiled a corpus of around 15 million tweets containing the term _values_ from 2019-2021 in English, German, Italian, Japanese, and Korean. We use two complementary methods for analyzing the data: 1) a big-data computational analysis, finding the most prominent terms co-occurring with and phrases containing the term _values_; 2) a systematic content analysis of a smaller subset of the corpus. *Preliminary Results of English and Japanese tweets suggest* fundamental differences in how the two languages frame _values_: while the term in English appears mainly in a partisan political context, Japanese usage is far more personal and apolitical. In the English corpus, the term is often invoked in politically motivated rhetorical attacks, often alleging hypocrisy. In the Japanese corpus, tweeters use the term to promote a societal norm of respecting others’ values and to discuss generational gaps. Our analysis demonstrates the potential of Twitter-based research for unpacking the meanings and functions of this core concept, across cultures