Research suggests that politicians labeled as populists tend to use simpler language than their mainstream opponents. Yet, the metrics traditionally employed to assess the complexity of their language do not show consistent and generalizable results across different datasets and languages. This inconsistencies raise questions about the claimed simplicity of populist discourse, suggesting that the issue may be more nuanced than it initially seemed. To address this topic, we analyze the linguistic profile of IMPAQTS, a dataset of transcribed Italian political speeches, to identify linguistic features differentiating populist and non-populist parties. Our methodology ensures comparability of political texts and combines various statistical analyses to reliably identify key linguistic characteristics to test our case study. Results show that the “simplistic” language features previously described in the literature are not robust predictors of populism. This suggests that the characteristics defining populist statements are highly dependent on the specific dataset and the language being analysed, thus limiting the conclusions drawn in previous research. In our study, various linguistic features statistically differentiate between populist and mainstream parties, indicating that populists tend to employ specific well-known rhetorical strategies more frequently; however, none of them strongly indicate that populist parties use simpler language.

For the analysis of political discourse a reliable identification of group references, i.e., linguistic components that refer to individuals or groups of people, is useful. However, the task of automatically recognizing group references has not yet gained much attention within NLP. To address this gap, we introduce GRIT (Group Reference for Italian), a large-scale, multi-domain manually annotated dataset for group reference recognition in Italian. GRIT represents a new resource for automatic and generalizable recognition of group references. With this dataset, we aim to establish group reference recognition as a valid classification task, which extends the domain of Named Entity Recognition by expanding its focus to literal and figurative mentions of social groups. We verify the potential of achieving automated group reference recognition for Italian through an experiment employing a fine-tuned BERT model. Our experimental results substantiate the validity of the task, implying a huge potential for applying automated systems to multiple fields of analysis, such as political text or social media analysis.

Warning: This paper contains explicit statements of offensive stereotypes which may be upsetting The study of bias, fairness and social impact in Natural Language Processing (NLP) lacks resources in languages other than English. Our objective is to support the evaluation of bias in language models in a multilingual setting. We use stereotypes across nine types of biases to build a corpus containing contrasting sentence pairs, one sentence that presents a stereotype concerning an underadvantaged group and another minimally changed sentence, concerning a matching advantaged group. We build on the French CrowS-Pairs corpus and guidelines to provide translations of the existing material into seven additional languages. In total, we produce 11,139 new sentence pairs that cover stereotypes dealing with nine types of biases in seven cultural contexts. We use the final resource for the evaluation of relevant monolingual and multilingual masked language models. We find that language models in all languages favor sentences that express stereotypes in most bias categories. The process of creating a resource that covers a wide range of language types and cultural settings highlights the difficulty of bias evaluation, in particular comparability across languages and contexts.