Narratives at Conflict: Computational Analysis of News Framing in Multilingual Disinformation Campaigns

TL;DR

This study analyzes how multilingual disinformation campaigns systematically use different frames in news articles across languages and regions, revealing targeted framing strategies and limitations of current automatic analysis models.

Contribution

It provides a large-scale multilingual analysis of disinformation framing, highlighting systematic differences and the underperformance of existing automatic detection models.

Findings

Disinformation campaigns tailor frames based on target language and region.

Current automatic framing models underperform and disagree significantly.

Disinformation strategies are consistent and intentional across languages.

Abstract

Any report frames issues to favor a particular interpretation by highlighting or excluding certain aspects of a story. Despite the widespread use of framing in disinformation, framing properties and detection methods remain underexplored outside the English-speaking world. We explore how multilingual framing of the same issue differs systematically. We use eight years of Russia-backed disinformation campaigns, spanning 8k news articles in 4 languages targeting 15 countries. We find that disinformation campaigns consistently and intentionally favor specific framing, depending on the target language of the audience. We further discover how Russian-language articles consistently highlight selected frames depending on the region of the media coverage. We find that the two most prominent models for automatic frame analysis underperform and show high disagreement, highlighting the need for further research.