| Texto completo | |
| Autor(es): |
Theophilo, Antonio
;
Pereira, Luis A. M.
;
Rocha, Anderson
;
IEEE
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP); v. N/A, p. 5-pg., 2019-01-01. |
| Resumo | |
The world is facing a new era in which social media communication plays a fundamental role in people's lives. Along with irrefutable benefits, several collateral drawbacks have risen, one being the wide spread of false information with malicious intents, what is now commonly called "Fake News". The fight against this problem is not easy, especially when taking into account the nature of text messages involved on social media platforms (a sea of small messages and myriad users). In this work, we cope with the challenging problem of authorship attribution of small text messages posted on social media platforms. Differently from what has been done with longer texts, we rely upon data-driven approaches, exploiting recent advances of deep neural networks in the field of pattern recognition. By viewing small texts usually employed in social media as unidimensional signals, we devise modern deep-learning techniques tailored for this kind of data to find the author of these posts with promising results. (AU) | |
| Processo FAPESP: | 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade |
| Beneficiário: | Anderson de Rezende Rocha |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 18/10204-6 - Combatendo Notícias Falsas Através da Atribuição de Autoria e da Análise de Filogenia |
| Beneficiário: | Antônio Carlos Theóphilo Costa Júnior |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |