| id |
caadria2020_276 |
| authors |
Chuang, I-Ting |
| year |
2020 |
| title |
Sensing the Diversity of Social Hubs through Social Media |
| source |
D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 61-70 |
| doi |
https://doi.org/10.52842/conf.caadria.2020.2.061
|
| summary |
As we continue to discover the potential of social media data as an insightful source for academic research, the majority of previous work tends to focus on the density of socio-spatial relations as the foundation for understanding urban phenomena. This paper extended those approaches by introducing the concepts of diversity and inclusiveness through an investigation of the 'differences' within the networks of relations that are inherent to social media data. The author constructs a diversity measure based on the variety of home locations of social media user visitors to each geographical location in the city. This home location, in its turn, is derived from each user's digital spatio-temporal footprint. This proposed method demonstrates that through the visualization of this diversity measure, 'social hubs' (which are frequently visited by different groups of people) were able to be located that would otherwise be overlooked in conventional data analyses that focus only on density. As such, this research expands the usefulness of social media as a practical tool to help understand urban processes by making the concept of diversity - a key consideration in many planning and design contexts - measurable and mappable. |
| keywords |
Social Media Data; Home Location Detection; Diversity Analysis |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (5,632,631 bytes) |
| references |
Content-type: text/plain
|
Campbell, AT, Eisenman, SB, Lane, ND, Miluzzo, E and Peterson, RA (2006)
 People-centric urban sensing
, Proceedings of the 2nd annual international workshop on Wireless internet - WICON '06, Boston, Massachusetts, pp. 18-es
|
|
|
|
|
Carmona, M (2010)
Contemporary Public Space: Critique and Classification, Part One: Critique
, Journal of Urban Design, 15(1), pp. 123-148
|
|
|
|
|
Carmona, M (2010)
Contemporary Public Space, Part Two: Classification
, Journal of Urban Design, 15(2), pp. 157-173
|
|
|
|
|
Carr, S, Stephen, C, Francis, M, Rivlin, LG and Stone, AM (1992)
Public space
, Cambridge University Press
|
|
|
|
|
Chen, Q (2019)
Identifying meaningful locations of social media users
, Geoinformatics week 2019 GSES & GeoAI-UC, Wuhan
|
|
|
|
|
Cho, E, Myers, SA and Leskovec, J (2011)
Friendship and Mobility: User Movement in Location-based Social Networks
, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 1082-1090
|
|
|
|
|
Crampton, JW, Graham, M, Poorthuis, A, Shelton, T, Stephens, M, Wilson, MW and Zook, M (2013)
Beyond the geotag: situating 'big data' and leveraging the potential of the geoweb
, Cartography and Geographic Information Science, 40(2), pp. 130-139
|
|
|
|
|
Goodchild, MF (2007)
Citizens as sensors: the world of volunteered geography
, GeoJournal, 69(4), pp. 211-221
|
|
|
|
|
Hossain, N, Hu, T, Feizi, R, White, AM, Luo, J and Kautz, H (2016)
Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities
, CoRR, abs/1603.03181, p. na
|
|
|
|
|
Humphreys, L (2010)
Mobile social networks and urban public space
, New Media & Society, 12(5), pp. 763-778
|
|
|
|
|
Jurdak, R, Zhao, K, Liu, J, AbouJaoude, M, Cameron, M and Newth, D (2015)
Understanding Human Mobility from Twitter
, PLOS ONE, 10(7), p. e0131469
|
|
|
|
|
Kouloumpis, E, Wilson, T and Moore, JD (2011)
Twitter Sentiment Analysis: The Good the Bad and the OMG!
, na, AAAI (eds), Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, July 17-21, 2011, AAAI Press, pp. 538-541
|
|
|
|
|
Mahmud, J, Nichols, J and Drews, C (2012)
Where is this tweet from? inferring home locations of twitter users
, Sixth International AAAI Conference on Weblogs and Social Media
|
|
|
|
|
Mahmud, J, Nichols, J and Drews, C (2014)
Home location identification of twitter users
, ACM Transactions on Intelligent Systems and Technology (TIST), 5(3), p. 47
|
|
|
|
|
McGee, J, Caverlee, J and Cheng, Z (2013)
Location Prediction in Social Media Based on Tie Strength
, Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management, New York, NY, USA, pp. 459-468
|
|
|
|
|
Mitchell, L, Frank, MR, Harris, KD, Dodds, PS and Danforth, CM (2013)
The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place
, PLoS One, 8(5), p. e64417
|
|
|
|
|
Nunes, AP, Silva, AC and Paiva, ACD (2010)
Detection of masses in mammographic images using geometry, Simpson's Diversity Index and SVM
, International Journal of Signal and Imaging Systems Engineering, 3(1), pp. 40-51
|
|
|
|
|
Oksanen, J, Kindt, R, Legendre, P, O'Hara, B, Stevens, MHH, Oksanen, MJ and Suggests, M (2007)
The vegan package
, Community ecology package, 10, pp. 631-637
|
|
|
|
|
Oldenburg, R (1999)
The great good place: Cafes, coffee shops, bookstores, bars, hair salons, and other hangouts at the heart of a community
, Da Capo Press
|
|
|
|
|
Pontes, T, Magno, G, Vasconcelos, M, Gupta, A, Almeida, J, Kumaraguru, P and Almeida, V (2012)
Beware of what you share: Inferring home location in social networks
, 2012 IEEE 12th International Conference on Data Mining Workshops, pp. 571-578
|
|
|
|
| last changed |
2022/06/07 07:56 |
|
