skip to main content
10.1145/2964284.2984063acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

Contextual Enrichment of Remote-Sensed Events with Social Media Streams

Authors Info & Claims
Published:01 October 2016Publication History

ABSTRACT

The availability of satellite images for academic or commercial purpose is increasing rapidly due to efforts made by governmental agencies (NASA, ESA) to publish such data openly or commercial startups (PlanetLabs) to provide real-time satellite data. Beyond many commercial application, satellite data is helpful to create situation awareness in disaster recovery and emergency situations such as wildfires, earthquakes, or flooding. To fully utilize such data sources, we present a scalable system for the contextual enrichment of satellite images by crawling and analyzing multimedia content from social media. This information stream can provide vital information from the ground and help to complement remote sensing in situations. We use Twitter as main data source and analyze its textual, visual, temporal, geographical and social dimensions. Visualizations show different aspects of the event allowing high-level comprehension and provide deeper insights into the event as complemented by social media.

References

  1. H. Becker, D. Iter, M. Naaman, and L. Gravano. Identifying content for planned events across social media sites. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM '12, pages 533--542, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang. Large-scale visual sentiment ontology and detectors using adjective noun pairs. In Proceedings of the 21st ACM international conference on Multimedia, pages 223--232. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. T. Chen, D. Borth, T. Darrell, and S. Chang. Deepsentibank: Visual sentiment concept classification with deep convolutional neural networks. CoRR, abs/1410.8586, 2014.Google ScholarGoogle Scholar
  4. M. Douze, H. Jégou, H. Sandhawalia, L. Amsaleg, and C. Schmid. Evaluation of gist descriptors for web-scale image search. In Proceedings of the ACM International Conference on Image and Video Retrieval, page 19. ACM, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.Google ScholarGoogle Scholar
  6. W. He, S. Zha, and L. Li. Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3):464--472, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  7. M. E. Keim and E. Noji. Emergent use of social media: a new age of opportunity for disaster resilience. American journal of disaster medicine, 6(1):47--54, 2010.Google ScholarGoogle Scholar
  8. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097--1105. Curran Associates, Inc., 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. D. Lazer, A. S. Pentland, L. Adamic, S. Aral, A. L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, et al. Life in the network: the coming age of computational social science. Science (New York, NY), 323(5915):721, 2009.Google ScholarGoogle Scholar
  10. B. R. Lindsay. Social media and disasters: Current uses, future options, and policy considerations, 2011.Google ScholarGoogle Scholar
  11. D. Maynard, K. Bontcheva, and D. Rout. Challenges in developing opinion mining tools for social media. Proceedings of the@ NLP can u tag #usergeneratedcontent, pages 15--22, 2012.Google ScholarGoogle Scholar
  12. P. J. McParlane, A. J. McMinn, and J. M. Jose. Picture the scene...;: Visually summarising social media events. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 1459--1468. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. L. Palen. Online social media in crisis events. Educause Quarterly, 31(3):76--78, 2008.Google ScholarGoogle Scholar
  14. T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web, WWW '10, pages 851--860, New York, NY, USA, 2010. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. Sarasohn-Kahn. The wisdom of patients: Health care meets online social media, 2008.Google ScholarGoogle Scholar
  16. C. W. Schmidt. Using social media to predict and track disease outbreaks. Environmental health perspectives, 120(1):A31, 2012.Google ScholarGoogle Scholar
  17. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.Google ScholarGoogle Scholar
  18. P. Sobkowicz, M. Kaschesky, and G. Bouchard. Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the web. Government Information Quarterly, 29(4):470--479, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  19. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1--9, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  20. M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12):2544--2558, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li. Yfcc100m: The new data in multimedia research. Commun. ACM, 59(2):64--73, Jan. 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. A. Ulges, D. Borth, and T. M. Breuel. Visual concept learning from weakly labeled web videos. In Video Search and Mining, pages 203--232. Springer, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  23. A. Ulges, C. Schulze, D. Borth, and A. Stahl. Pornography detection in video benefits (a lot) from a multi-modal approach. In Proceedings of the 2012 ACM international workshop on Audio and multimedia methods for large-scale video analysis, pages 21--26. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. J. Yin, A. Lampert, M. Cameron, B. Robinson, and R. Power. Using social media to enhance emergency situation awareness. IEEE Intelligent Systems, 27(6):52--59, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Contextual Enrichment of Remote-Sensed Events with Social Media Streams

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      MM '16: Proceedings of the 24th ACM international conference on Multimedia
      October 2016
      1542 pages
      ISBN:9781450336031
      DOI:10.1145/2964284

      Copyright © 2016 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 October 2016

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      MM '16 Paper Acceptance Rate52of237submissions,22%Overall Acceptance Rate995of4,171submissions,24%

      Upcoming Conference

      MM '24
      MM '24: The 32nd ACM International Conference on Multimedia
      October 28 - November 1, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader