Speaker: Federico Nanni, University of Mannheim
Title: Topic-based and Cross-lingual Scaling of Political Text
Abstract: Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models, such as Wordscores and Wordfish, scale texts based on relative word usage; by doing so, they do not take into consideration topical information and cannot be used for cross-lingual analyses. In our talk, we present our efforts toward developing a topic-based and cross-lingual political text scaling approach. First we introduce our initial work, TopFish, a multilevel computational method that integrates topic detection and political scaling and shows its applicability for temporal aspect analyses of political campaigns (pre-primary elections, primary elections, and general elections). Next, we present a new text scaling approach that leverages semantic representations of text and is suitable for cross-lingual political text scaling. We also propose a simple and straightforward setting for quantitative evaluation of political text scaling.
Short bio: Federico Nanni is a Post-Doctoral researcher at the University of Mannheim, affiliated with the Data and Web Science Group and the Political Science Department. His research deals with adopting (and adapting) Natural Language Processing methods for supporting studies in the Digital Humanities and Computational Social Sciences.
Sala Gianni Lazzari, Edificio Ovest