Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports

TL;DR

This study investigates how linguistic expressions in Italian news reports about femicide influence perceptions of responsibility, demonstrating that automatic models can predict perceived blame and focus based on language choices.

Contribution

It introduces a large-scale perception survey and trains regression models, including fine-tuned BERT, to predict responsibility perception from linguistic features in GBV news reports.

Findings

Salience of focus is more predictable than blame.

Perpetrators' salience is more predictable than victims'.

Linguistic features influence responsibility perception.

Abstract

Different linguistic expressions can conceptualize the same event from different viewpoints by emphasizing certain participants over others. Here, we investigate a case where this has social consequences: how do linguistic expressions of gender-based violence (GBV) influence who we perceive as responsible? We build on previous psycholinguistic research in this area and conduct a large-scale perception survey of GBV descriptions automatically extracted from a corpus of Italian newspapers. We then train regression models that predict the salience of GBV participants with respect to different dimensions of perceived responsibility. Our best model (fine-tuned BERT) shows solid overall performance, with large differences between dimensions and participants: salient _focus_ is more predictable than salient _blame_, and perpetrators' salience is more predictable than victims' salience. Experiments with ridge regression models using different representations show that features based on linguistic theory similarly to word-based features. Overall, we show that different linguistic choices do trigger different perceptions of responsibility, and that such perceptions can be modelled automatically. This work can be a core instrument to raise awareness of the consequences of different perspectivizations in the general public and in news producers alike.