Large-scale Cross-lingual Language Resources for Referencing and Framing

In this article, we lay out the basic ideas and principles of the project Framing Situations in the Dutch Language. We provide our first results of data acquisition, together with the first data release. We introduce the notion of cross-lingual referential corpora. These corpora consist of texts that make reference to exactly the same incidents. The referential grounding allows us to analyze the framing of these incidents in different languages and across different texts. During the project, we will use the automatically generated data to study linguistic framing as a phenomenon, build framing resources such as lexicons and corpora. We expect to capture larger variation in framing compared to traditional approaches for building such resources. Our first data release, which contains structured data about a large number of incidents and reference texts, can be found at http://dutchframenet. nl/data-releases/

Frame Semantics for Social NLP in Italian:Analyzing Responsibility Framing in Femicide News Reports

We propose using a FrameNet-based ap- proach for analyzing how socially relevant events are framed in media discourses. Taking femicides as an example, we per- form a preliminary investigation on a large dataset of news reports and event data cov- ering recent femicides in Italy. First, we revisit the EVALITA 2011 shared task on Italian frame labeling, and test a recent multilingual frame semantic parser against this benchmark. Then, we experiment with specializing this model for Italian and perform a human evaluation to test our model’s real-world applicability. We show how FrameNet-based analyses can help to identify linguistic constructions that back- ground the agentivity and responsibility of femicide perpetrators in Italian news

Explaining the distribution of implicit means of misrepresentation:A case study on Italian immigration discourse

This study analyzes Fillmore's frames in a large corpus of Italian news headlines concerning migrations, dating from 2013 to 2021 and taken from newspapers of diverse ideological stances. Our goal is to assess whether, how, and why migrants' representation varies over time and across ideological stances. Our approach combines corpus-assisted critical discourse analysis with cognitive linguistics. We present a new methodology that exploits SOCIOFILLMORE, a tool integrating a novel Natural Language Processing model for automatic frame annotation into a web-based user interface for exploring frame-annotated corpora. In our corpus, the frequency distribution of frames varies over time according to detectable contextual factors. Across political stances, instead, the most frequent frames remain more constant: both right-winged and left-winged news providers contribute to reifying migrants into non-agentive entities. Further, in religious (Christian) press migrants are given a more humanizing depiction, but they still often appear in non-agentive roles. The distributions of frames can be explained by the fact that the latter act as indirect, routinized, and implicit means of (mis)representation. We suggest that framing entails inferential operations that take place unconsciously and can therefore escape the cognitive screening not only of those who receive discourse, but also of those who (re)produce it.</p