Abstract
Twitter messages can be located in a city and take the pulse of the citizens’ activity. The temporal and spatial location of spots of high activity, the mobility patterns and the existence of unforeseen bursts constitute a certain Urban Chronotype, which is altered when a city-wide event happens, such as a world-class Congress. This paper proposes a Social Sensing Platform to track the Urban Chronotype, able to collect the Tweets, categorize their provenance and extract knowledge about them. The clustering algorithm DBScan is proposed to detect the hot spots, and a day to day analysis reveals the movement patterns. Having analyzed the Tweetbeat of Barcelona during the 2012 Mobile World Congress, results show that a easy-to-deploy social sensor based on Twitter is capable of representing the presence and interests of the attendees in the city and enables future practical applications. Initial empirical results haven shown a significant alteration in the behavioural patterns of users and clusters of activity within the city.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 851–860. ACM, New York (2010)
Takahashi, T., Abe, S., Igata, N.: Can Twitter Be an Alternative of Real-World Sensors? In: Jacko, J.A. (ed.) HCI International 2011, Part III. LNCS, vol. 6763, pp. 240–249. Springer, Heidelberg (2011)
Jeff Cox, B.P.: Improving automatic weather observations with the public twitter stream. Technical report, Indiana University Computer Science Program (February 2011)
Li, R., Lei, K.H., Khadiwala, R., Chang, K.C.-C.: Tedas: a twitter based event detection and analysis system. In: Proceedings of the IEEE International Conference on Data Engineering, ICDE (April 2012)
Fujisaka, T., Lee, R., Sumiya, K.: Detection of unusually crowded places through micro-blogging sites. In: 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 467–472 (April 2010)
Weng, J., Yao, Y., Leonardi, E., Lee, F.: Event Detection in Twitter. Technical report, HP Labs (2011)
Asur, S., Huberman, B.A.: Predicting the future with social media. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2010, vol. 01, pp. 492–499. IEEE Computer Society, Washington, DC (2010)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)
Girardin, F., Fiore, F.D., Ratti, C., Blat, J.: Leveraging explicitly disclosed location information to understand tourist dynamics: a case study. J. Locat. Based Serv. 2(1), 41–56 (2008)
Schedl, M.: Analyzing the potential of microblogs for spatio-temporal popularity estimation of music artists. In: Proceedings of the IJCAI 2011: International Workshop on Social Web Mining (2011)
Conover, M., Gonçalves, B., Ratkiewicz, J., Flammini, A., Menczer, F.: Predicting the political alignment of twitter users. In: Proceedings of 3rd IEEE Conference on Social Computing, SocialCom (2011)
Nagarajan, M., Gomadam, K., Sheth, A.P., Ranabahu, A., Mutharaju, R., Jadhav, A.: Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 539–553. Springer, Heidelberg (2009)
Martino, M., Vaccari, A., Ratti, C.: Pulse of the city: Visualizing urban dynamics of special events. In: GraphiCon - Internation Conference on Computer Graphics and Vision (2010)
Ferrari, L., Rosi, A., Mamei, M., Zambonelli, F.: Extracting urban patterns from location-based social networks. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2011, pp. 9–16. ACM, New York (2011)
Fujisaka, T., Lee, R., Sumiya, K.: Discovery of user behavior patterns from geo-tagged micro-blogs. In: Proceedings of the 4th International Conference on Uniquitous Information Management and Communication, ICUIMC 2010, pp. 36:1–36:10. ACM, New York (2010)
Du, Y., Fan, J., Chen, J.: Experimental analysis of user mobility pattern in mobile social networks. In: 2011 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1086–1090 (March 2011)
Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 759–768. ACM, New York (2010)
Lehmann, J., Gonçalves, B., Ramasco, J.J., Cattuto, C.: Dynamical classes of collective attention in twitter. CoRR abs/1111.1896 (2011)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Villatoro, D., Serna, J., Rodríguez, V., Torrent-Moreno, M. (2013). The TweetBeat of the City: Microblogging Used for Discovering Behavioural Patterns during the MWC2012. In: Nin, J., Villatoro, D. (eds) Citizen in Sensor Networks. CitiSens 2012. Lecture Notes in Computer Science(), vol 7685. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36074-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-36074-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36073-2
Online ISBN: 978-3-642-36074-9
eBook Packages: Computer ScienceComputer Science (R0)