Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports (2022)

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.

## introduction and background the same event can be described in many different ways, according to who reports on it, and the choices they make. they can opt for some words rather than others, for example, or they can use a passive rather than an active construction, or more widely, they can -consciously or not -provide the reader with a specific perspective over what happened. such choices do not just pertain to the realm of stylistic subtleties; rather, they can have substantial consequences on how we think of -or frame -figure 1 : "cyclist slams into car door" figure 1 : "car driver opens door and hits cyclist" figure 1 : "cyclist injured in road accident on 5th street" figure 1 : "collision between bike and car" we use alternative captions to illustrate how the same event can be described from alternative perspectives, which can evoke different perceptions in the attribution of responsibility to the actors involved. violence against women is worryingly common and therefore often reported in the news. a report by the european parliament @xcite details an estimate of 87,000 women intentionally killed in 2017. while italy is listed in this report as one of the european countries with the lowest number of femicides, they are still too frequent and have been constant in the last 25 years (0.6 per 100,000 women in 1982 and 0.4 per 100,000 in 2017). most discouragingly, a report from november 2018 by two italian research institutes points out that the stereotype of a shared responsibility between the violence victim and its perpetrator is still widespread among young generations: "56.8% of boys and 38.8% of girls believe that the female is at least partly responsible for the violence she has suffered" (laboratorio adolescenza and istituto iard, 2018). working on italian news, @xcite observe that in descriptions of femicides, the use of syntactic constructions with varying levels of transitivity -from transitive active constructions on one side of the spectrum, via passives and anticausatives to nominalization constructions on the other side -corresponds to various degrees of responsibility attributed to the (male) perpetrator. for example, while "he killed her" (active/transitive) makes the involvement of an active agent fully explicit, with "she was killed (by him)" (passive) the event is accessed via the patient shifting attention away from the agent, and expressions such as "the murder" or even "the event" (nominal construction) moves both participants to the background. in a related contribution, @xcite investigate the impact of argument structure constructions on responsibility attributions by means of a survey on artificially-constructed gbv reports in italian. their results further confirm the findings of @xcite on the effects of readers' perception on the agentivity and responsibility of the perpetrators and the victims. the outcomes of both studies is in line with previous work in psycholinguistics showing that in events involving violence (at any level), the linguistic backgrounding of agents hinders their responsibility and promotes victim blaming @xcite @xcite @xcite . based on such framing choices, how will the general reader perceive the described event? can we model such perceptions automatically? in this paper we aim to answer these questions, still focusing on descriptions of femicides in italian news, and exploiting frame semantics @xcite as a theoretical and practical tool, as well as most recent nlp approaches. using specific pre-selected semantic frames, automatically extracted using a state-of-the-art semantic parser @xcite , we identify descriptions of gbv events from italian newspapers. on these descriptions we collect human judgements through a large-scale survey where we ask participants to read the texts and ascribe a degree of perceived responsibility to the perpetrator, the victim, or to some more abstract concept (e.g. "jealousy", "rage"). more details are provided in §2. next, we model perception of responsibility automatically by developing a battery of regression models (both from scratch as well as atop pretrained transformer models) exploiting a variety of linguistic cues which range from surface to framebased features. the training objective of such models is the prediction of the human perception scores. we achieve a strong correlation with a transformerbased model. the fine-grained character both of the survey and the result analysis that we conducted also allows us to observe differences in prediction complexity for the various aspects that we consider. modeling and evaluation are discussed in §3. the results we obtain show that different linguistic choices do indeed trigger different perceptions of responsibility, and that such perceptions can be modelled automatically. this finding not only confirms previous research which was conducted (manually) on a much smaller scale, but also opens up the possibility to conduct large-scale analyses of texts exposing to both producers and consumers of texts which perspectivization strategies are at play and their effects. foot_2 ## femicide perception dataset we designed an online questionnaire study in which participants were presented with sentences extracted from the rai femicides corpus @xcite , a collection of 2,734 news articles linked to 182 femicide cases perpetrated in italy between 2015-2017, and asked to rate the level of agentivity and responsibility expressed in each sentence. the results of the questionnaire demonstrate a clear effect of semantic frames and syntactic constructions on the perception of descriptions of femicides. ## perception score prediction in this section, we introduce models for automatically predicting femicide perception scores, as well as a suite of evaluation measures for evaluating these models. we model our task as a multi-output regression task: given a sentence s, we want to predict a perception vector ⃗ p, in which every entry p i represents the value of a particular likert dimension from the questionnaire (e.g. 'blame on the victim', 'focus on an object'). ## conclusion & future work this paper has presented a detailed analysis of human perceptions of responsibility in italian news reporting on gbv. the judgments we collected confirm the findings of previous work on the impact of specific grammatical constructions and semantic frames, and the perceptions they trigger in readers. on the basis of the results of our survey, we have investigated to what extent different nlp architectures can predict the human perception judgements. the results of our experiments indicate that finetuning monolingual transformers leads to the best results across multiple evaluation measures. this opens up the possibility of integrating systems able to identify potential perception effects as support tools for media professionals. in the future, we plan to run a more detailed analysis of the data considering differences along individual and demographic dimensions of the respondents. in addition to this, natural follow-up experiments will focus on the application of the approach to other languages and cultural contexts both targeting gbv as well as other socially relevant topics, e.g. car crashes @xcite .