Uma abordagem geográfica para a priorização de mensagens de mídias sociais para o gerenciamento de risco de inundação com base em dados de sensores
Resumo
O gerenciamento de riscos de enchentes requer informações atualizadas e precisas sobre a situação geral em áreas vulneráveis. As mensagens de mídias sociais são consideradas como uma fonte valiosa adicional de informações para complementar dados oficiais (por exemplo, dados de sensores no local). Em alguns casos, essas mensagens também podem ajudar a complementar dados de sensores inadequados ou incompletos e, assim, uma descrição mais completa de um fenômeno pode ser fornecida. No entanto, identificar informações significativas e confiáveis continua sendo uma atividade difícil. Isto ocorre devido ao enorme volume de mensagens que são produzidas, o que levanta questões relativas à sua autenticidade, confidencialidade, confiabilidade, propriedade e qualidade. Em vista disso, este artigo adota uma abordagem para priorização em tempo real de mensagens de mídias sociais que se baseiam em dados de sensores (especialmente medidores de água). Uma aplicação para a prova de conceito de nossa abordagem é delineada por meio de um cenário hipotético, que usa mensagens de mídia social do Twitter, bem como dados de sensores coletados por meio de redes de estações hidrológicas mantidas pela Pegelonline na Alemanha. Os resultados mostraram que nossa abordagem é capaz de priorizar as mensagens de mídia social e, assim, fornecer informações atualizadas e precisas para apoiar as tarefas executadas pelos tomadores de decisão no gerenciamento de riscos de inundação.
Palavras-chave:
Risco de Inundação, Mídias Sociais, Abordagem Geográfica
Referências
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Chae, J., Thom, D., Bosch, H., Jang, Y., Maciejewski, R., Ebert, D. S., and Ertl, T. (2012). Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In Proocedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 143–152, Seattle, USA.
Crooks, A., Croitoru, A., Stefanidis, A., and Radzikowski, J. (2013). # Earthquake: Twitter as a distributed sensor system. Transactions in GIS, 17(1):124–147.
Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. Geo-Journal, 69(4):211–221.
Gupta, A. and Kumaraguru, P. (2012). Credibility ranking of tweets during high impact events. In Proceedings of the 1st Workshop on Privacy and Security in Online Social Media (PSOSM), pages 1–8, Lyon, France.
Herfort, B., de Albuquerque, J., Schelhorn, S.-J., and Zipf, A. (2014). Exploring the Geographical Relations Between Social Media and Flood Phenomena to Improve Situational Awareness. In Huerta, J., Schade, S., and Granell, C., editors, Connecting a Digital Europe Through Location and Place, Lecture Notes in Geoinformation and Cartography, pages 55–71. Springer International Publishing.
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Horita, F. E. A., Degrossi, L. C., de Assis, L. F. G., Zipf, A., and de Albuquerque, J. P. (2013). The use of volunteered geographic information (vgi) and crowdsourcing in disaster management: a systematic literature review. AMCIS 2013 Proceedings.
Jha, A. K., B. R. and Lamond, J. (2012). Cities and flooding: A guide to integrated urban flood risk management for the 21st century. Technical Report, International Bank for Reconstruction and Development - The Work Bank.
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., and Silvestre, B. S. (2011). Social media? get serious! understanding the functional building blocks of social media. Business horizons, 54(3):241–251.
Kongthon, A., Haruechaiyasak, C., Pailai, J., and Kongyoung, S. (2012). The role of Twitter during a natural disaster: Case study of 2011 Thai Flood. In Proceedings of the 2012 Technology Management for Emerging Technologies (PICMET), pages 2227– 2232, Vancouver, Canada.
MacEachren, A. M., Jaiswal, A., Robinson, A. C., Pezanowski, S., Savelyev, A., Mitra, P., Zhang, X., and Blanford, J. (2011). Senseplace2: GeoTwitter analytics support for situational awareness. In Proocedings of the 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 181–190, Providence, USA.
Manca, S. and Ranieri, M. (2013). Identity, credibility, and trust in social networking sites. Social Network Engineering for Secure Web Data and Services, 5.
Mendoza, M., Poblete, B., and Castillo, C. (2010). Twitter under crisis: Can we trust what we RT? In Proceedings of the 1st Workshop on Social Media Analytics (SOMA), pages 71–79, Washington, USA.
Middleton, S. E., Middleton, L., and Modafferi, S. (2014). Real-time crisis mapping of natural disasters using social media. Intelligent Systems, IEEE, 29(2):9–17.
Mooney, P. and Corcoran, P. (2011). Can Volunteered Geographic Information Be a Participant in eEnvironment and SDI? In Hˇrebíˇcek, J., Schimak, G., and Denzer, R., editors, Environmental Software Systems. Frameworks of eEnvironment, volume 359 of IFIP Advances in Information and Communication Technology, pages 115–122.
Morris, M. R., Counts, S., Roseway, A., Hoff, A., and Schwarz, J. (2012). Tweeting is believing?:understanding microblog credibility perceptions. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (CSCW), pages 441–450, Seattle, USA.
Naaman, M. (2011). Geographic information from georeferenced social media data. SIGSPATIAL Special, 3(2):54–61.
Norris, F. H., Stevens, S. P., Pfefferbaum, B.,Wyche, K. F., and Pfefferbaum, R. L. (2008). Community resilience as a metaphor, theory, se of capacities and strategy for disaster. American Journal of Community Psychology.
Palen, L., Vieweg, S., and Anderson, K. M. (2010). Supporting “everyday analysts” in safety-and time-critical situations. The Information Society, 27(1):52–62.
Qu, Y., Huang, C., Zhang, P., and Zhang, J. (2011). Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work (CSCW), pages 25–34, Hangzhou, China.
Rogstadius, J., Kostakos, V., Laredo, J., and Vukovic, M. (2011). Towards real-time emergency response using crowd supported analysis of social media. In Proceedings of CHI 2011 Workshop on Crowdsourcing and Human Computation, Systems, Studies and Platforms, pages 1–3, Vancouver, Canada.
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes Twitter users: realtime event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web (WWW), pages 851–860, Raleigh, USA.
Schade, S., Díaz, L., Ostermann, F., Spinsanti, L., Luraschi, G., Cox, S., Nu˜nez, M., and De Longueville, B. (2013). Citizen-based sensing of crisis events: sensor web enablement for volunteered geographic information. Applied Geomatics, 5(1):3–18.
Schnebele, E. and Cervone, G. (2013). Improving remote sensing flood assessment using volunteered geographical data. Natural Hazards and Earth System Science, 13(3):669–677.
Schnebele, E., Cervone, G., Kumar, S., and Waters, N. (2014a). Real time estimation of the Calgary floods using limited remote sensing data. Water, 6(2):381–398.
Schnebele, E., Cervone, G., and Waters, N. (2014b). Road assessment after flood events using non-authoritative data. Natural Hazards and Earth System Science, 14(4):1007–1015.
Starbird, K., Palen, L., Hughes, A. L., and Vieweg, S. (2010). Chatter on the red: What hazards threat reveals about the social life of microblogged information. In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, pages 241–250.
Szomszor, M., Kostkova, P., and St Louis, C. (2011). Twitter informatics: Tracking and understanding public reaction during the 2009 swine flu pandemic. In Proocedings of the 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pages 320–323, Lyon, France.
Tapia, A. H., Bajpai, K., Jansen, B. J., Yen, J., and Giles, L. (2011). Seeking the trustworthy tweet: Can microblogged data fit the information needs of disaster response and humanitarian relief organizations. In Proceedings of the 8th International Conference on Information Systems for Crisis Response and Management (ISCRAM), pages 1–10.
Terpstra, T., de Vries, A., Stronkman, R., and Paradies, G. (2012). Towards a realtime Twitter analysis during crises for operational crisis management. In Proceedings of the 9th International Conference on Information Systems for Crisis Response and Management (ISCRAM), pages 1–9, Vancouver, Canada.
Triglav-Cˇ ekada,M. and Radovan, D. (2013). Using volunteered geographical information to map the November 2012 floods in Slovenia. Natural Hazards and Earth System Science, 13(11):2753–2762.
Vieweg, S., Hughes, A. L., Starbird, K., and Palen, L. (2010). Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In Proceedings of the 28th Conference on Human Factors in Computing Systems (SIGCHI), pages 1079–1088, Atlate, USA.
Yin, J., Lampert, A., Cameron, M., Robinson, B., and Power, R. (2012). Using social media to enhance emergency situation awareness. IEEE Intelligent Systems, 27(6):52–59.
Zielinski, A., Middleton, S. E., Tokarchuk, L. N., andWang, X. (2013). Social Media Text Mining and Network Analysis for Decision Support in Natural Crisis Management. In Proceedings of the 10th International Conference on Information Systems for Crisis Response and Management (ISCRAM), pages 840–845, Baden–Baden, Germany.
Zook, M., Graham, M., Shelton, T., and Gorman, S. (2010). Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Medical & Health Policy, 2(2):7–33.
Agichtein, E., Castillo, C., Donato, D., Gionis, A., and Mishne, G. (2008). Finding highquality content in social media. In Proceedings of the 2008 International Conference on Web Search and Data Mining, pages 183–194. ACM.
Albuquerque, J. P., Herfort, B., Brenning, A., and Zipf, A. (2015). A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. International Journal of Geographical Information Science, pages 1–23.
Chae, J., Thom, D., Bosch, H., Jang, Y., Maciejewski, R., Ebert, D. S., and Ertl, T. (2012). Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In Proocedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 143–152, Seattle, USA.
Crooks, A., Croitoru, A., Stefanidis, A., and Radzikowski, J. (2013). # Earthquake: Twitter as a distributed sensor system. Transactions in GIS, 17(1):124–147.
Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. Geo-Journal, 69(4):211–221.
Gupta, A. and Kumaraguru, P. (2012). Credibility ranking of tweets during high impact events. In Proceedings of the 1st Workshop on Privacy and Security in Online Social Media (PSOSM), pages 1–8, Lyon, France.
Herfort, B., de Albuquerque, J., Schelhorn, S.-J., and Zipf, A. (2014). Exploring the Geographical Relations Between Social Media and Flood Phenomena to Improve Situational Awareness. In Huerta, J., Schade, S., and Granell, C., editors, Connecting a Digital Europe Through Location and Place, Lecture Notes in Geoinformation and Cartography, pages 55–71. Springer International Publishing.
Horita, F. E., de Albuquerque, J. P., Degrossi, L. C., Mendiondo, E. M., and Ueyama, J. (2015). Development of a spatial decision support system for flood risk management in brazil that combines volunteered geographic information with wireless sensor networks. Computers & Geosciences, 80:84–94.
Horita, F. E. A., Degrossi, L. C., de Assis, L. F. G., Zipf, A., and de Albuquerque, J. P. (2013). The use of volunteered geographic information (vgi) and crowdsourcing in disaster management: a systematic literature review. AMCIS 2013 Proceedings.
Jha, A. K., B. R. and Lamond, J. (2012). Cities and flooding: A guide to integrated urban flood risk management for the 21st century. Technical Report, International Bank for Reconstruction and Development - The Work Bank.
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., and Silvestre, B. S. (2011). Social media? get serious! understanding the functional building blocks of social media. Business horizons, 54(3):241–251.
Kongthon, A., Haruechaiyasak, C., Pailai, J., and Kongyoung, S. (2012). The role of Twitter during a natural disaster: Case study of 2011 Thai Flood. In Proceedings of the 2012 Technology Management for Emerging Technologies (PICMET), pages 2227– 2232, Vancouver, Canada.
MacEachren, A. M., Jaiswal, A., Robinson, A. C., Pezanowski, S., Savelyev, A., Mitra, P., Zhang, X., and Blanford, J. (2011). Senseplace2: GeoTwitter analytics support for situational awareness. In Proocedings of the 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 181–190, Providence, USA.
Manca, S. and Ranieri, M. (2013). Identity, credibility, and trust in social networking sites. Social Network Engineering for Secure Web Data and Services, 5.
Mendoza, M., Poblete, B., and Castillo, C. (2010). Twitter under crisis: Can we trust what we RT? In Proceedings of the 1st Workshop on Social Media Analytics (SOMA), pages 71–79, Washington, USA.
Middleton, S. E., Middleton, L., and Modafferi, S. (2014). Real-time crisis mapping of natural disasters using social media. Intelligent Systems, IEEE, 29(2):9–17.
Mooney, P. and Corcoran, P. (2011). Can Volunteered Geographic Information Be a Participant in eEnvironment and SDI? In Hˇrebíˇcek, J., Schimak, G., and Denzer, R., editors, Environmental Software Systems. Frameworks of eEnvironment, volume 359 of IFIP Advances in Information and Communication Technology, pages 115–122.
Morris, M. R., Counts, S., Roseway, A., Hoff, A., and Schwarz, J. (2012). Tweeting is believing?:understanding microblog credibility perceptions. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (CSCW), pages 441–450, Seattle, USA.
Naaman, M. (2011). Geographic information from georeferenced social media data. SIGSPATIAL Special, 3(2):54–61.
Norris, F. H., Stevens, S. P., Pfefferbaum, B.,Wyche, K. F., and Pfefferbaum, R. L. (2008). Community resilience as a metaphor, theory, se of capacities and strategy for disaster. American Journal of Community Psychology.
Palen, L., Vieweg, S., and Anderson, K. M. (2010). Supporting “everyday analysts” in safety-and time-critical situations. The Information Society, 27(1):52–62.
Qu, Y., Huang, C., Zhang, P., and Zhang, J. (2011). Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work (CSCW), pages 25–34, Hangzhou, China.
Rogstadius, J., Kostakos, V., Laredo, J., and Vukovic, M. (2011). Towards real-time emergency response using crowd supported analysis of social media. In Proceedings of CHI 2011 Workshop on Crowdsourcing and Human Computation, Systems, Studies and Platforms, pages 1–3, Vancouver, Canada.
Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes Twitter users: realtime event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web (WWW), pages 851–860, Raleigh, USA.
Schade, S., Díaz, L., Ostermann, F., Spinsanti, L., Luraschi, G., Cox, S., Nu˜nez, M., and De Longueville, B. (2013). Citizen-based sensing of crisis events: sensor web enablement for volunteered geographic information. Applied Geomatics, 5(1):3–18.
Schnebele, E. and Cervone, G. (2013). Improving remote sensing flood assessment using volunteered geographical data. Natural Hazards and Earth System Science, 13(3):669–677.
Schnebele, E., Cervone, G., Kumar, S., and Waters, N. (2014a). Real time estimation of the Calgary floods using limited remote sensing data. Water, 6(2):381–398.
Schnebele, E., Cervone, G., and Waters, N. (2014b). Road assessment after flood events using non-authoritative data. Natural Hazards and Earth System Science, 14(4):1007–1015.
Starbird, K., Palen, L., Hughes, A. L., and Vieweg, S. (2010). Chatter on the red: What hazards threat reveals about the social life of microblogged information. In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, pages 241–250.
Szomszor, M., Kostkova, P., and St Louis, C. (2011). Twitter informatics: Tracking and understanding public reaction during the 2009 swine flu pandemic. In Proocedings of the 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pages 320–323, Lyon, France.
Tapia, A. H., Bajpai, K., Jansen, B. J., Yen, J., and Giles, L. (2011). Seeking the trustworthy tweet: Can microblogged data fit the information needs of disaster response and humanitarian relief organizations. In Proceedings of the 8th International Conference on Information Systems for Crisis Response and Management (ISCRAM), pages 1–10.
Terpstra, T., de Vries, A., Stronkman, R., and Paradies, G. (2012). Towards a realtime Twitter analysis during crises for operational crisis management. In Proceedings of the 9th International Conference on Information Systems for Crisis Response and Management (ISCRAM), pages 1–9, Vancouver, Canada.
Triglav-Cˇ ekada,M. and Radovan, D. (2013). Using volunteered geographical information to map the November 2012 floods in Slovenia. Natural Hazards and Earth System Science, 13(11):2753–2762.
Vieweg, S., Hughes, A. L., Starbird, K., and Palen, L. (2010). Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In Proceedings of the 28th Conference on Human Factors in Computing Systems (SIGCHI), pages 1079–1088, Atlate, USA.
Yin, J., Lampert, A., Cameron, M., Robinson, B., and Power, R. (2012). Using social media to enhance emergency situation awareness. IEEE Intelligent Systems, 27(6):52–59.
Zielinski, A., Middleton, S. E., Tokarchuk, L. N., andWang, X. (2013). Social Media Text Mining and Network Analysis for Decision Support in Natural Crisis Management. In Proceedings of the 10th International Conference on Information Systems for Crisis Response and Management (ISCRAM), pages 840–845, Baden–Baden, Germany.
Zook, M., Graham, M., Shelton, T., and Gorman, S. (2010). Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Medical & Health Policy, 2(2):7–33.
Publicado
01/08/2015
Como Citar
ASSIS, Luiz Fernando; HORITA, Flavio; HERFORT, Benjamin; STEIGER, Enrico; DE ALBUQUERQUE, João Porto.
Uma abordagem geográfica para a priorização de mensagens de mídias sociais para o gerenciamento de risco de inundação com base em dados de sensores. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 4. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. .
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2015.6768.
