| Texto completo | |
| Autor(es): |
Nascimento, Jose
;
Bestagini, Paolo
;
Rocha, Anderson
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE ACCESS; v. 13, p. 11-pg., 2025-01-01. |
| Resumo | |
Social media has emerged during the last decade as a potential information source in crisis scenarios, providing data in real-time or just after the occurrence of the event. Nevertheless, current social media data acquisition procedures result in datasets, presenting myriad non-informative content, which hinders posterior analyses. While previous studies have used machine learning to address this issue, they typically require many labeled examples, hardening their use in a real-world scenario. Moreover, social media posts tend to be multimodal, which adds complexity to how these data should be represented. This paper extends upon our previous work and presents a new method for identifying the most informative content related to an event in textual and visual data through few-shot learning. The results show that this method outperforms existing approaches in both performance and efficiency, offering a valuable solution for a timely analysis of crisis-related social media data and advancing research in this area. (AU) | |
| Processo FAPESP: | 23/12865-8 - Horus: técnicas de inteligência artificial para detecção e análise de realidades sintéticas |
| Beneficiário: | Anderson de Rezende Rocha |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |