Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports
This work addresses how media language shapes public perception of gender-based violence, offering tools to raise awareness among news producers and the general public.
The study investigated how linguistic expressions in gender-based violence news reports influence perceptions of responsibility, finding that fine-tuned BERT models could predict responsibility salience with solid performance, though predictability varied significantly across dimensions (focus more predictable than blame) and participants (perpetrators more predictable than victims).
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.