Topic Modeling in Feminist Debates on Instagram: A Generative AI Approach - Almeida, Thalia; Barbosa, Keila; Fernandes, Sheyla; Aquino, André (Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM); 2025: Anais do XIV Brazilian Workshop on Social Network Analysis and Mining, 2025)
This study investigates the identification of feminist themes addressed by Brazilian profiles on Instagram using Natural Language Processing (NLP) techniques and topic modeling. To analyze recurring discussions and their variations, we apply the BERTopic technique for topic modeling, while we use the large language model (LLM) LLaMA for labeling the identified themes. The modeling process resulted in 90 topics, highlighting issues such as domestic violence, reproductive rights, and mental health, reflecting current debates in the Brazilian context. Additionally, an experiment compared topic labeling performed by human participants and LLM, analyzing the similarity between both responses. The BERTScore metric, which assesses semantic similarity, yielded the highest results, with values between 0.68 and 0.79. This result indicates that the LLM produced semantically similar responses to human ones. The results emphasize the role of NLP techniques and language models in identifying complex social themes, providing a solid foundation for future studies on the impact of social media on awareness and social change promotion.