Identificação de Ocorrências do Cnidário Physalia physalis em Dados Extraídos de Mídias Sociais
Resumo
As necessidades de conhecimento da biodiversidade são constantes, enquanto recursos para pesquisa, sejam financeiros, de tempo e humanos são escassos. Por outro lado, a Internet oferece um enorme volume de dados que podem ser explorados em favor da ciência da conservação. As caravelas-portuguesas (Physalia physalis) oferecem risco à população, e dados sobre sua ocorrência nem sempre estão disponíveis para estudo da espécie. Neste trabalho é proposto o treinamento de modelos de aprendizagem de máquina como ferramenta para classificar dados extraídos de uma mídia social e assim possibilitar a geração de uma base de dados sobre ocorrências de caravelas-portuguesas no litoral brasileiro.
Palavras-chave:
processamento de linguagem natural, aprendizagem de máquina multimodal, midias sociais
Referências
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Cavalcante, M. M. E., Rodrigues, Z. M. R., Hauser-Davis, R. A., Siciliano, S., Haddad Júnior, V., and Nunes, J. L. S. (2020). Health-risk assessment of portuguese man-ofwar (physalia physalis) envenomations on urban beaches in são luís city, in the state of maranhão, brazil. Revista da Sociedade Brasileira de Medicina Tropical, 53.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.
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Foglio, M. (2019). Animal Wildlife Population Estimation Using Social Media Images Collections. Master’s thesis, University of Illinois, Chicago, Illinois, USA.
Giachanou, A., Zhang, G., and Rosso, P. (2020). Multimodal multi-image fake news detection. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pages 647-654.
Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Rodrigues, J., and Aluisio, S. (2017). Portuguese word embeddings: Evaluating on word analogies and natural language tasks. arXiv preprint arXiv:1708.06025.
Hu, C., Yin, M., Liu, B., Li, X., and Ye, Y. (2021). Detection of Illicit Drug Trafficking Events on Instagram. In Proceedings of the 30th ACM International Conference on Information Knowledge Management, pages 3838-3846, New York, NY, USA. ACM.
Kulkarni, R. and Di Minin, E. (2021). Automated retrieval of information on threatened species from online sources using machine learning. Methods in Ecology and Evolution, 12(7):1226-1239.
Lu, J., Batra, D., Parikh, D., and Lee, S. (2019). Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc.
Mazars-Simon, A. E. (2019). The Wild in Live Project: A Human/Algorithm learning network to help citizen science in wildlife conservation. Master’s thesis, Universidade de Coimbra.
Morais, P., Afonso, L., and Dias, E. (2021). Harnessing the Power of Social Media to Obtain Biodiversity Data About Cetaceans in a Poorly Monitored Area. Frontiers in Marine Science, 8.
Nascimento, L. (2020). Monitoring jellyfish population by social media. Technical report, Pós-Graduação em Sistemas Costeiros e Oceânicos, Universidade Federal do Paraná.
Ofli, F., Alam, F., and Imran, M. (2020). Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response. arXiv preprint arXiv:2004.11838.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In Cerri, R. and Prati, R. C., editors, Intelligent Systems, pages 403-417, Cham. Springer International Publishing.
Taklis, C., Giovos, I., and Karamanlidis, A. A. (2020). Social media: a valuable tool to inform shark conservation in Greece. Mediterranean Marine Science, 21(3):493-498.
Cavalcante, M. M. E., Rodrigues, Z. M. R., Hauser-Davis, R. A., Siciliano, S., Haddad Júnior, V., and Nunes, J. L. S. (2020). Health-risk assessment of portuguese man-ofwar (physalia physalis) envenomations on urban beaches in são luís city, in the state of maranhão, brazil. Revista da Sociedade Brasileira de Medicina Tropical, 53.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Edwards, T., Jones, C. B., and Corcoran, P. (2022). Identifying wildlife observations on twitter. Ecological Informatics, 67:101500.
Edwards, T., Jones, C. B., Perkins, S. E., and Corcoran, P. (2021). Passive citizen science: The role of social media in wildlife observations. PLOS ONE, 16(8):e0255416.
Foglio, M. (2019). Animal Wildlife Population Estimation Using Social Media Images Collections. Master’s thesis, University of Illinois, Chicago, Illinois, USA.
Giachanou, A., Zhang, G., and Rosso, P. (2020). Multimodal multi-image fake news detection. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pages 647-654.
Hartmann, N., Fonseca, E., Shulby, C., Treviso, M., Rodrigues, J., and Aluisio, S. (2017). Portuguese word embeddings: Evaluating on word analogies and natural language tasks. arXiv preprint arXiv:1708.06025.
Hu, C., Yin, M., Liu, B., Li, X., and Ye, Y. (2021). Detection of Illicit Drug Trafficking Events on Instagram. In Proceedings of the 30th ACM International Conference on Information Knowledge Management, pages 3838-3846, New York, NY, USA. ACM.
Kulkarni, R. and Di Minin, E. (2021). Automated retrieval of information on threatened species from online sources using machine learning. Methods in Ecology and Evolution, 12(7):1226-1239.
Lu, J., Batra, D., Parikh, D., and Lee, S. (2019). Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc.
Mazars-Simon, A. E. (2019). The Wild in Live Project: A Human/Algorithm learning network to help citizen science in wildlife conservation. Master’s thesis, Universidade de Coimbra.
Morais, P., Afonso, L., and Dias, E. (2021). Harnessing the Power of Social Media to Obtain Biodiversity Data About Cetaceans in a Poorly Monitored Area. Frontiers in Marine Science, 8.
Nascimento, L. (2020). Monitoring jellyfish population by social media. Technical report, Pós-Graduação em Sistemas Costeiros e Oceânicos, Universidade Federal do Paraná.
Ofli, F., Alam, F., and Imran, M. (2020). Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response. arXiv preprint arXiv:2004.11838.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In Cerri, R. and Prati, R. C., editors, Intelligent Systems, pages 403-417, Cham. Springer International Publishing.
Taklis, C., Giovos, I., and Karamanlidis, A. A. (2020). Social media: a valuable tool to inform shark conservation in Greece. Mediterranean Marine Science, 21(3):493-498.
Publicado
19/09/2022
Como Citar
ROCHA, Heloisa F.; HARA, Carmem S..
Identificação de Ocorrências do Cnidário Physalia physalis em Dados Extraídos de Mídias Sociais. In: WORKSHOP DE TESES E DISSERTAÇÕES (WTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 119-125.
DOI: https://doi.org/10.5753/sbbd_estendido.2022.21853.
