Abstract
Using evolving information within rapid mapping activities in the response phase of emergency situations poses a number of questions related to the quality of information being provided. In this paper, we focus on image extraction from social networks, in particular Twitter, in case of emergencies. In this case issues arise about the temporal and spatial location of images, which can be refined over time as information about the event is being collected and (automatically) analyzed. The paper describes a scenario for rapid mapping in an emergency event and how information quality can evolve over time. A model for managing and analyzing the evolving information is proposed to be used as a basis for analyzing the images quality for mapping purposes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
Notice that they are not the complete set of posted tweets with that image. Some tweets could not be retrieved and \(\approx \)2.7% of the retrieved tweets are not available anymore. Moreover, to find also the same image at different resolutions or with slight modifications a hashing algorithm has been used and it has false negatives.
- 5.
This is true if it would be possible to confirm that the image comes from the target event; in general the starting point of the interval is unknown or, equivalently, the precision associated to the interval of two hours is low.
- 6.
- 7.
References
Al-Rfou, R., Kulkarni, V., Perozzi, B., Skiena, S.: Polyglot-NER: massive multilingual named entity recognition. In: Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, British Columbia, Canada, 30 April – 2 May 2015, April 2015
Andrienko, G.L., Andrienko, N.V., Fuchs, G.: Understanding movement data quality. J. Locat. Based Serv. 10(1), 31–46 (2016). https://doi.org/10.1080/17489725.2016.1169322
Andrienko, N., Andrienko, G., Fuchs, G., Rinzivillo, S., Betz, H.D.: Detection, tracking, and visualization of spatial event clusters for real time monitoring. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015. 36678 2015, pp. 1–10. IEEE (2015)
Batini, C., Scannapieco, M.: Data and Information Quality - Dimensions, Principles and Techniques. DSA. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-24106-7
Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on Twitter. In: ICWSM, vol. 11, pp. 438–441 (2011)
Brusoni, V., Console, L., Terenziani, P., Pernici, B.: Qualitative and quantitative temporal constraints and relational databases: theory, architecture, and applications. IEEE Trans. Knowl. Data Eng. 11(6), 948–968 (1999). https://doi.org/10.1109/69.824613
Castillo, C.: Big crisis data: social media in disasters and time-critical situations (2016)
Davis Jr., C.A., Pappa, G.L., de Oliveira, D.R.R., de L Arcanjo, F.: Inferring the location of Twitter messages based on user relationships. Trans. GIS 15(6), 735–751 (2011)
Di Blas, N., Mazuran, M., Paolini, P., Quintarelli, E., Tanca, L.: Exploratory computing: a comprehensive approach to data sensemaking. Int. J. Data Sci. Anal. 3(1), 61–77 (2017). https://doi.org/10.1007/s41060-016-0039-5
Dyreson, C., Grandi, F., Käfer, W., Kline, N., Lorentzos, N., Mitsopoulos, Y., Montanari, A., Nonen, D., Peressi, E., Pernici, B., et al.: A consensus glossary of temporal database concepts. ACM Sigmod Rec. 23(1), 52–64 (1994)
E2mC Team: Analysis of Copernicus Witness integration issues, E2mC deliverable D1.3, April 2017
Francalanci, C., Guglielmino, P., Montalcini, M., Scalia, G., Pernici, B.: Imext: a method and system to extract geolocated images from tweets analysis of a case study. In: Research Challenges in Information Science (RCIS), 2017 IEEE Eleventh International Conference on Research Challenges in Information Science, Brighton, UK, May 2017. IEEE (2017)
Francalanci, C., Pernici, B.: Data integration and quality requirements in emergency services. In: Advances in Service-Oriented and Cloud Computing. Springer (in press)
Gelernter, J., Mushegian, N.: Geo-parsing messages from microtext. Trans. GIS 15(6), 753–773 (2011)
Ghufran, M., Quercini, G., Bennacer, N.: Toponym disambiguation in online social network profiles. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 6. ACM (2015)
Guglielmino, P., Montalcini, M.: Extracting relevant content from social media for emergency management contexts. Master thesis, Politecnico di Milano (2016)
Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing social media messages in mass emergency: a survey. ACM Comput. Surv. (CSUR) 47(4), 67 (2015)
Imran, M., Elbassuoni, S.M., Castillo, C., Diaz, F., Meier, P.: Extracting information nuggets from disaster-related messages in social media. In: Proceedings of ISCRAM, Baden-Baden, Germany (2013)
Imran, M., Mitra, P., Castillo, C.: Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages. arXiv preprint arXiv:1605.05894 (2016)
Inkpen, D., Liu, J., Farzindar, A., Kazemi, F., Ghazi, D.: Detecting and disambiguating locations mentioned in Twitter messages. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 321–332. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_24
Liu, F., Vasardani, M., Baldwin, T.: Automatic identification of locative expressions from social media text: a comparative analysis. In: Proceedings of the 4th International Workshop on Location and the Web, pp. 9–16. ACM (2014)
Nugroho, R., Yang, J., Zhao, W., Paris, C., Nepal, S.: What and with whom? Identifying topics in Twitter through both interactions and text. IEEE Trans. Serv. Comput. PP(99), 1 (2017)
Paradesi, S.M.: Geotagging tweets using their content. In: FLAIRS Conference (2011)
Reuter, T., Cimiano, P.: Event-based classification of social media streams. In: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, p. 22. ACM (2012)
Ritter, A., Clark, S., Etzioni, O., et al.: Named entity recognition in tweets: an experimental study. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534. Association for Computational Linguistics (2011)
Sakaki, T., Okazaki, M., Matsuo, Y.: Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans. Knowl. Data Eng. 25(4), 919–931 (2013). https://doi.org/10.1109/TKDE.2012.29
Scalia, G.: Network-based content geolocation on social media for emergency management. Master thesis, Politecnico di Milano, April 2017
Tamura, K., Ichimura, T.: Density-based spatiotemporal clustering algorithm for extracting bursty areas from georeferenced documents. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2079–2084. IEEE (2013)
Wasay, A., Athanassoulis, M., Idreos, S.: Queriosity: automated data exploration. In: Carminati, B., Khan, L. (eds.) 2015 IEEE International Congress on Big Data, New York City, NY, USA, 27 June – 2 July 2015, pp. 716–719. IEEE (2015). https://doi.org/10.1109/BigDataCongress.2015.116
Zhang, W., Gelernter, J.: Geocoding location expressions in Twitter messages: a preference learning method. J. Spat. Inf. Sci. 2014(9), 37–70 (2014)
Acknowledgments
This work has been partially funded by the European Commission H2020 project E\(^{2}\)mC “Evolution of Emergency Copernicus services” under project No. 730082. This work expresses the opinions of the authors and not necessarily those of the European Commission. The European Commission is not liable for any use that may be made of the information contained in this work. The authors thank Paolo Ravanelli for his support in creating event-specific crawlers, and Paolo Gugliemino and Matteo Montalcini for their work on the case study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Francalanci, C., Pernici, B., Scalia, G. (2018). Exploratory Spatio-Temporal Queries in Evolving Information. In: Doulkeridis, C., Vouros, G., Qu, Q., Wang, S. (eds) Mobility Analytics for Spatio-Temporal and Social Data. MATES 2017. Lecture Notes in Computer Science(), vol 10731. Springer, Cham. https://doi.org/10.1007/978-3-319-73521-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-73521-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73520-7
Online ISBN: 978-3-319-73521-4
eBook Packages: Computer ScienceComputer Science (R0)