ABSTRACT
The recent popularity of location-based social (LBS) networking services has resulted in huge volumes of geo-tagged data from social media, allowing us to monitor massive lifelogs from a real-world space. Also, the characteristics of urban areas, placeness, were identified from the lifelogs attained.
Based on this concern, in this paper, we propose a new approach of placeness extraction with an ontology-based urban area placeness identification system. The suggested technique uses the textual, temporal, and spatial information of a LBS post from a specific area, and combines this information with the help of ontology. This combination measures the areas occasion-oriented placeness, which can be subdivided into time or companions. Our work focuses on a case study of Twitter data from the city of Seoul. The results show that our system is able to extract subdividable placeness and suitable correspondences when compared to real world socio-geographic features.
- S. Harrison and P. Dourish, "Re-place-ing space: The roles of place and space in collaborative systems," in Proceedings of the 1996 ACM Conference on Computer Supported Cooperative Work, ser. CSCW '96. New York, NY, USA: ACM, 1996, pp. 67--76. [Online]. Available: http://doi.acm.org/10.1145/240080.240193 Google ScholarDigital Library
- "Mann S. wearcam.org," http://wearcam.org/eastcampusfire.htm, accessed: 1995--02.Google Scholar
- C. G. Bell and J. Gemmell, Total recall: How the e-memory revolution will change everything. Dutton, 2009.Google Scholar
- A. Noulas, S. Scellato, C. Mascolo, and M. Pontil, "Exploiting semantic annotations for clustering geographic areas and users in location-based social networks." The Social Mobile Web, vol. 11, no. 2, 2011.Google Scholar
- R. Lee, S. Wakamiya, and K. Sumiya, "Urban area characterization based on crowd behavioral lifelogs over twitter," Personal and Ubiquitous Computing, vol. 17, no. 4, pp. 605--620, 2013. [Online]. Available: http://dx.doi.org/10.1007/s00779-012-0510-9 Google ScholarDigital Library
- K. Dave, S. Lawrence, and D. M. Pennock, "Mining the peanut gallery: Opinion extraction and semantic classification of product reviews," in Proceedings of the 12th International Conference on World Wide Web, ser. WWW '03. New York, NY, USA: ACM, 2003, pp. 519--528. [Online]. Available: http://doi.acm.org/10.1145/775152.775226 Google ScholarDigital Library
- B. Pang and L. Lee, "A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts," in Proceedings of the 42Nd Annual Meeting on Association for Computational Linguistics, ser. ACL '04. Stroudsburg, PA, USA: Association for Computational Linguistics, 2004. [Online]. Available: http://dx.doi.org/10.3115/1218955.1218990 Google ScholarDigital Library
- X. Liu, S. Zhang, F. Wei, and M. Zhou, "Recognizing named entities in tweets," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies -Volume 1, ser. HLT '11. Stroudsburg, PA, USA: Association for Computational Linguistics, 2011, pp. 359--367. [Online]. Available: http://dl.acm.org/citation.cfm?id=2002472.2002519Google Scholar
- M. B. Salem and S. J. Stolfo, "Detecting masqueraders: A comparison of one-class bag-of-words user behavior modeling techniques." JoWUA, vol. 1, no. 1, pp. 3--13, 2010.Google Scholar
- N. Grinberg, M. Naaman, B. Shaw, and G. Lotan, "Extracting diurnal patterns of real world activity from social media." in ICWSM, 2013.Google Scholar
- T. Kawamura, A. Ohsuga et al., "Extraction and estimation of human activity from twitter for information sharing in disaster," Journal of Convergence Information Technology, vol. 8, no. 11, p. 707, 2013. Google ScholarCross Ref
- D. T. Nguyen and J. E. Jung, "Privacy-preserving discovery of topic-based events from social sensor signals: An experimental study on twitter," The Scientific World Journal, vol. 2014, 2014.Google ScholarCross Ref
- R. Dueas-Fernndez, J. D. Velsquez, and G. LHuillier, "Detecting trends on the web: A multidisciplinary approach," Information Fusion, vol. 20, pp. 129--135, 2014. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1566253514000116Google ScholarCross Ref
- M. Mathioudakis and N. Koudas, "Twittermonitor: Trend detection over the twitter stream," in Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD '10. New York, NY, USA: ACM, 2010, pp. 1155--1158. [Online]. Available: http://doi.acm.org/10.1145/1807167.1807306 Google ScholarDigital Library
- J.-Y. Kim, T.-H. Moon, and J.-H. Cho, "Evaluation of the urban regeneration project: Land use transformation and sns big data analysis," Evaluation, vol. 1, p. 56582, 2016.Google Scholar
- C. Andris, "Integrating social network data into gisystems," International Journal of Geographical Information Science, vol. 30, no. 10, pp. 2009--2031, 2016. [Online]. Available: http://dx.doi.org/10.1080/13658816.2016.1153103 Google ScholarDigital Library
- I. Hong, "Spatial analysis of location-based social networks in seoul, korea," Journal of Geographic Information System, vol. 7, no. 3, p. 259, 2015. Google ScholarCross Ref
- Extracting Placeness from Social Media: an Ontology-Based System
Recommendations
Social Media for Social Good: A Study of Experiences and Opportunities in Rural Australia
#SMSociety17: Proceedings of the 8th International Conference on Social Media & SocietySocial media platforms are espoused as helpful for overcoming geographic isolation for rural people and businesses; connecting them with local, national and global communities. Yet, little research has been conducted to document social media use by ...
Citywide management of media facades: case study of Seoul City
MAB '16: Proceedings of the 3rd Media Architecture Biennale ConferenceDue to the evolution of LED lighting and information technology, the application of media facades has expanded rapidly. Despite the positive aspects of media facades, the growth of them can cause light pollution and add to the confusion of the city. This ...
Comments