Abstract
Social media have evolved as an important source of information and situational awareness in crisis and emergency management. As the number of messages generated and diffused through social networks in time of crisis increase exponentially, locating reliable and critical information in a timely manner is crucial, especially for decision makers. In such scenarios, identifying influential users in social networks, detecting anomalous information diffusion patterns, and locating corresponding geographical coordinates are often instrumental in providing important information and helping analysts make decisions in a timely manner. We describe a visual analytics framework focusing on identifying influential users and anomalous information diffusion based on dynamic social networks using Twitter data. We also demonstrate a visual analytics approach that allows users to analyze a large volume of social media data to detect and examine abnormal events within Location-Based Social Network (LBSN). Our statistical models to extract user topics and evaluate their anomaly scores are applied to facilitate exploration and perception of Twitter semantics. The framework provides highly interactive filtering and geo-location mapping to help categorize different topics, detect influential users and anomalous information in specific events, and investigate the underlying spatiotemporal patterns.
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
References
Andrienko, G., Andrienko, N., Bosch, H., Ertl, T., Fuchs, G., Jankowski, P., et al. (2013). Thematic patterns in georeferenced tweets through space-time visual analytics. Computing in Science and Engineering, 15(3), 72–82.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Bosch, H., Thom, D., Worner, M., Koch, S., Puttmann, E., Jackle, D., et al. (2011). Scatterblogs: Geo-spatial document analysis. In IEEE Symposium on Visual Analytics Science and Technology (pp. 309–310).
Card, S. K., & Nation, D. (2002). Degree-of-interest trees: A component of an attention-reactive user interface. In Proceedings of the Working Conference on Advanced Visual Interfaces, AVI’02 (pp. 231–245). New York: ACM. doi:10.1145/1556262.1556300.
Chae, J., Thom, D., Bosch, H., Jang, Y., Maciejewski, R., Ebert, D., et al. (2012). Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In IEEE Symposium on Visual Analytics Science and Technology, October 14–19, 2012 (pp. 143–152).
Chae, J., Thom, D., Jang, Y., Kim, S., Ertl, T., & Ebert, D. S. (2014). Public behavior response analysis in disaster events utilizing visual analytics of microblog data. Computers & Graphics, 38, 51–60.
Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.
Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess (with discussion). Journal of Official Statistics, 6(1), 3–73.
Correa, C., Crnovrsanin, T., & Ma, K.-L. (2012). Visual reasoning about social networks using centrality sensitivity. IEEE Transactions on Visualization and Computer Graphics, 18(1), 106–120.
Crnovrsanin, T., Liao, I., Wu, Y., & Ma, K.-L. (2011). Visual recommendations for network navigation. Computer Graphics Forum, 30, 1081–1090.
Dork, M., Carpendale, S., Collins, C., & Williamson, C. (2008). VisGets: Coordinated visualizations for web-based information exploration and discovery. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1205–1212.
Fayyad, U. M., Piatetsky-Shapiro, G., & Smith, P. (1996). From data mining to knowledge discovery: An overview. In U. M. Fayyad, et al. (Eds.), Advances in knowledge discovery and data mining (chap. 1, pp. 1–34). Menlo Park, CA: AAAI Press/MIT Press.
Field, K., & O’Brien, J. (2010). Cartoblography: Experiments in using and organising the spatial context of micro-blogging. Transactions in GIS, 14, 5–23. doi:10.1111/j.1467-9671.2010.01210.x.
Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software Practice and Experience, 21(11), 1129–1164. doi:10.1002/spe.4380211102.
Ho, C.-T., Li, C.-T., & Lin, S.-D. (2011). Modeling and visualizing information propagation in a micro-blogging platform. In IEEE International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 328–335).
Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1), 1–8.
Krovetz R. (1993). Viewing morphology as an inference process. In Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’93 (pp. 191–202). New York: ACM. doi:10.1145/160688.160718.
Krueger, R., Thom, D., & Ertl, T. (2014). Visual analysis of movement behavior using web data for context enrichment. In Proceedings of the 2014 I.E. Pacific Visualization Symposium, PACIFICVIS’14 (pp. 193–200). Washington, DC: IEEE Computer Society.
MacEachren, A., Jaiswal, A., Robinson, A., Pezanowski, S., Savelyev, A., Mitra, P., et al. (2011). SensePlace2: GeoTwitter analytics support for situational awareness. In IEEE Symposium on Visual Analytics Science and Technology (pp. 181–190).
Macskassy, S. A., & Michelson, M. (2011). Why do people retweet? Anti-homophily wins the day!. In International AAAI Conference on Web and Social Media.
McCallum, A. K. (2002). Mallet: A machine learning for language toolkit. Retrieved from http://mallet.cs.umass.edu
Norman, D. A. (1993). Things that make us smart: Defending human attributes in the age of the machine. New York: Basic Books.
Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011). Influence and passivity in social media. In Machine learning and knowledge discovery in databases (pp. 18–33). Berlin, Germany: Springer.
Roth, E., & White, J. (2010) Twitterhitter: Geovisual analytics for harvesting insight from volunteered geographic information. In Proceedings of GIScience.
Sakaki, T., Okazaki, M., & 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’10 (pp. 851–860). New York: ACM.
Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings of the IEEE Symposium on Visual Languages (pp. 336–343).
Shneiderman, B. (2002). Inventing discovery tools: Combining information visualization with data mining. Information Visualization, 1(1), 5–12.
Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In Proceedings of the 2010 I.E. Second International Conference on Social Computing (SocialCom) (pp. 177–184).
Sun, J., Papadimitriou, S., Lin, C.-Y., Cao, N., Liu, S., & Qian, W. (2009) Multivis: Content-based social network exploration through multi-way visual analysis. In SDM (Vol. 9, pp. 1063–1074). SIAM.
Terpstra, T., Stronkman, R., de Vries, A., & Paradies, G. (2012). Towards a realtime twitter analysis during crises for operational crisis management. In Proceedings of the 9th International ISCRAM Conference, Vancouver.
Thom, D., Bosch, H., Koch, S., Woerner, M., & Ertl, T. (2012). Spatiotemporal anomaly detection through visual analysis of geolocated twitter messages. In IEEE Pacific Visualization Symposium (PacificVis) (pp. 41–48).
Wakamiya, S., Lee, R., & Sumiya, K. (2011). Crowd-based urban characterization: Extracting crowd behavioral patterns in urban areas from twitter. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN’11. New York: ACM.
Yang, X., Asur, S., Parthasarathy, S., & Mehta, S. (2008). A visual-analytic toolkit for dynamic interaction graphs. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1016–1024). New York: ACM.
Ying, J. J.-C., Lee, W.-C., Ye, M., Chen, C.-Y., & Tseng, V. S. (2011). User association analysis of locales on location based social networks. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN’11 (pp. 69–76). New York: ACM.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Zhang, J. et al. (2015). Visual Analytics of User Influence and Location-Based Social Networks. In: Matei, S., Russell, M., Bertino, E. (eds) Transparency in Social Media. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-18552-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-18552-1_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18551-4
Online ISBN: 978-3-319-18552-1
eBook Packages: Computer ScienceComputer Science (R0)