REAL TIME EVENT LOCATION DETECTION BASED MOBILITY PATTERN MODELLING FOR SOCIAL MEDIA USER MOBILITY ANALYSIS

TITLE

Real Time Event Location Detection Based Mobility Pattern Modelling For Social Media User Mobility Analysis

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ABSTRACT

Detection of important events in a region through social media has been a recent development with scope for multiple applications. One of the applications is the analysis of the mobility of the users in the region to adapt the energy and resources as per their movement. For this purpose, the travel or movement behaviour of the social media users is analysed through their posts and related messages. An efficient method of mobility pattern modelling named as Event Location based Mobility Pattern Modelling WebClickviz (ELMPM-WebClickviz) is proposed in this paper based on the event location detection. Initially the social media data are collected and preprocessed. Then the geographical as well as temporal information are extracted along with the time and distance parameters. Then the pattern modelling is initiated using Sequential Hierarchical pattern clustering which detects the continuous events from the user data along with the location of occurrence. Based on these results, the mobility behaviour can be modelled with higher accuracy. The evaluation results prove that the proposed model provides efficient mobility patter modelling to be utilized for the organizations and official concerns in fulfilling the resources and needs of the users of that location.

KEYWORDS

Social media, Travel behaviour, Event location detection, Mobility pattern modelling, WebClickviz, Sequential Hierarchical pattern clustering

REFERENCES

[1] Yan, Z., & Chakraborty, D. (2014). Semantics in mobile sensing. Synthesis Lectures on the Semantic Web: Theory and Technology, 4(1), 1- 143.

[2] Wu, F., Li, Z., Lee, W. C., Wang, H., & Huang, Z. (2015). Semantic annotation of mobility data using social media. In Proceedings of the 24th International Conference on World Wide Web (pp. 1253-1263). International World Wide Web Conferences Steering Committee.

[3] Gabrielli, L., Rinzivillo, S., Ronzano, F., & Villatoro, D. (2014). From tweets to semantic trajectories: mining anomalous urban mobility patterns. In Citizen in Sensor Networks (pp. 26- 35). Springer, Cham.

[4] Hasan, S., Zhan, X., & Ukkusuri, S. V. (2013). Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In Proceedings of the 2nd ACM SIGKDD international workshop on urban computing (p. 6). ACM.

[5] Thilakarathna, K., Seneviratne, S., Gupta, K., Kaafar, M. A., & Seneviratne, A. (2017). A deep dive into location-based communities in social discovery networks. Computer Communications, 100, 78-90.

[6] Frhan, A. J. (2017). Website Clickstream Data Visualization Using Improved Markov Chain Modelling In Apache Flume. In MATEC Web of Conferences (Vol. 125, p. 04025). EDP Sciences.

[7] Al-Ariki, H. D. E., & Swamy, M. S. (2017). A survey and analysis of multipath routing protocols in wireless multimedia sensor networks. Wireless Networks, 23(6), 1823-1835.

[8] Cheng, Z., Caverlee, J., Lee, K., & Sui, D. Z. (2011). Exploring millions of footprints in location sharing services. ICWSM, 2011, 81-88.

[9] Rashidi, T. H., Abbasi, A., Maghrebi, M., Hasan, S., & Waller, T. S. (2017). Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges. Transportation Research Part C: Emerging Technologies, 75, 197-211.

[10] Zhu, Z., Blanke, U., & Tröster, G. (2014). Inferring travel purpose from crowd-augmented human mobility data. In Proceedings of the First International Conference on IoT in Urban Space(pp. 44-49). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).

[11] Zheng, X., Chen, W., Wang, P., Shen, D., Chen, S., Wang, X., & Yang, L. (2016). Big data for social transportation. IEEE Transactions on Intelligent Transportation Systems, 17(3), 620- 630.

[12] Brockmann, D., Hufnagel, L., & Geisel, T. (2006). The scaling laws of human travel. Nature, 439(7075), 462-465.

[13] Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A. L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779-782.

[14] Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1082-1090). ACM.

[15] Tarasov, A., Kling, F., & Pozdnoukhov, A. (2013). Prediction of user location using the radiation model and social check-ins. In Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing (p. 8). ACM.

[16] Simini, F., González, M. C., Maritan, A., & Barabási, A. L. (2012). A universal model for mobility and migration patterns. Nature, 484(7392), 96-100.

[17] Isaacman, S., Becker, R., Cáceres, R., Martonosi, M., Rowland, J., Varshavsky, A., & Willinger, W. (2012). Human mobility modeling at metropolitan scales. In Proceedings of the 10th international conference on Mobile systems, applications, and services (pp. 239-252).

[18] Deb, B., & Basu, P. (2015). Discovering latent semantic structure in human mobility traces. In European Conference on Wireless Sensor Networks (pp. 84-103). Springer, Cham.

[19] Zhang, C., Zhang, K., Yuan, Q., Zhang, L., Hanratty, T., & Han, J. (2016). Gmove: Grouplevel mobility modeling using geo-tagged social media. In KDD: proceedings. International Conference on Knowledge Discovery & Data Mining (Vol. 2016, p. 1305). NIH Public Access.

[20] Wang, Y., Yuan, N. J., Lian, D., Xu, L., Xie, X., Chen, E., & Rui, Y. (2015). Regularity and conformity: Location prediction using heterogeneous mobility data. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1275-1284). ACM.

[21] Jiang, M., Cui, P., Wang, F., Xu, X., Zhu, W., & Yang, S. (2014). Fema: flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1186- 1195). ACM.

[22] Jiang, M., Faloutsos, C., & Han, J. (2016). Catchtartan: Representing and summarizing dynamic multicontextual behaviors. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 945-954). ACM.

[23] Yuan, Q., Zhang, W., Zhang, C., Geng, X., Cong, G., & Han, J. (2017, February). Pred: Periodic region detection for mobility modeling of social media users. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (pp. 263-272). ACM.

[24] Zhang, Z., He, Q., & Zhu, S. (2017). Potentials of using social media to infer the longitudinal travel behavior: A sequential modelbased clustering method. Transportation Research Part C: Emerging Technologies, 85, 396-414.

[25] Cuenca-Jara, J., Terroso-Saenz, F., Valdes- Vela, M., Gonzalez-Vidal, A., & Skarmeta, A. F. (2017). Human mobility analysis based on social media and fuzzy clustering. In Global Internet of Things Summit (GIoTS), 2017 (pp. 1-6). IEEE.

[26] Li, Z., Ding, B., Han, J., Kays, R., & Nye, P. (2010). Mining periodic behaviors for moving objects. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1099-1108). ACM.

[27] Loewenstein, Y., Portugaly, E., Fromer, M., & Linial, M. (2008). Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space. Bioinformatics, 24(13), i41-i49.

Cite this paper

Amjad Jumaah Frhan. (2017) Real Time Event Location Detection Based Mobility Pattern Modelling For Social Media User Mobility Analysis. International Journal of Communications, 2, 109-117