We describe our participation in the Multilingual Counterspeech Generation shared task, which aims to generate a counternarrative to counteract hate speech, given a hateful sentence and relevant background knowledge. Our team tested two different aspects: translating outputs from English vs generating outputs in the original languages and filtering pieces of the background knowledge provided vs including all the background knowledge. Our experiments show that filtering the background knowledge in the same prompt and leaving data in the original languages leads to more adherent counternarrative generations, except for Basque, where translating the output from English and filtering the background knowledge in a separate prompt yields better results. Our system ranked first in English, Italian, and Spanish and fourth in Basque.

Understanding how words shift in meaning is crucial for analyzing societal attitudes.In this study, we investigate the contextual variations of the terms feminist, feminists along three axes: time, language, and domain.To this aim, we collect and release FEMME, a dataset comprising the occurrences of such terms from 2014 to 2023 in English, Italian and Swedish in Twitter, Reddit and Incel domains.Our methodology leverages frame analysis, as well as fine-tuning and LLMs. We find that the connotation of the plural form feminists is consistently more negative than feminist, indicating more hostility towards feminists as a collective, which often triggers greater societal pushback, reflecting broader patterns of group-based hostility and stigma. Across languages, we observe similar stereotypes towards feminists that often include body shaming, as well as accusations of hypocrisy and irrational behavior. In terms of time, we identify events that trigger a peak in terms of negative or positive connotation.As expected, the Incel spheres show predominantly negative connotations, while the general domains show mixed connotations.