Micro Quality of Life: Assessing Health and Well-Being in and around Public Facilities in New York City
Microblogs and other social media platforms are increasingly used as sources of data for analyzing social issues and problems, and for determining appropriate public policy. Our research investigates the utility of an urban social listening approach in considering quality of life around public facilities in New York City, and the possibility of combining conventional public health data and microblogging data from Twitter to render an instructive sketch of urban neighborhoods. We demonstrate that this approach shows promise, with significant relationships between tweet scores, unemployment rates, and incidences of diabetes in the localized geographies we analyzed. While limitations exist, we provide a roadmap for future research as scholars seek to understand the health and well-being of urban populations.
KeywordsSocial media New York City Twitter Content analysis Social listening
We would like to acknowledge research assistance provided by the following: Alphonsus Adu-Bredu, Owen Searls, Stephanie Savir, Tatiana Marzan, Divya Gandhi, Rachel Lai, Judy Fung, Allison Curtis, Caroline Antonelli, Claudia Aliff, Rebekah Abioye, and John VanderHeide. Chris Gallegos and Noam Saragosti assisted with graphics and mapping. Special thanks go to Terri Matthews, James Russell, Margaret O’Donoghue Castillo, Frederic Bell, Ifeoma Ebo, and Allison Brown, all of the NYC DDC.
This study was funded by The New York City Department of Design and Construction (grant number 20167204516).
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
- Antonelli, F., Azzi, M., Balduini, M., Ciuccarelli, P., Valle, E. D., and Larcher, R. 2014. City sensing: visualising mobile and social data about a city scale event. In Proceedings of the 2014 International Working Conference on Advanced Visual Interfaces. May 2014: 337–338. ACM.Google Scholar
- Balduini, M., Della Valle, E., Dell’Aglio, D., Tsytsarau, M., Palpanas, T., and Confalonieri, C. 2013. Social listening of city scale events using the streaming linked data framework. The Semantic Web–ISWC 2013. 1–16. Springer Berlin Heidelberg.Google Scholar
- Bertrand, K. Z., Bialik, M., Virdee, K., Gros, a., and Bar-yam, Y. 2013. Sentiment in new york city: A high resolution spatial and temporal view. arXiv preprint arXiv:1308.5010.Google Scholar
- Bollen, J., Mao, H., and Pepe, A. 2011. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In ICWSM. July 2011.Google Scholar
- Boniwell, I. 2008. Positive psychology in a nutshell: A balanced introduction to the science of optimal functioning. Personal Well-Being Centre.Google Scholar
- Brooks B (2014). Using Twitter data to identify geographic clustering of anti-vaccination sentiments. Master's thesis. digital.lib.washington.edu.Google Scholar
- Diener, E., Suh, E. M., Lucas, R. E., and Smith, H. E. 1999. Subjective well-being: Three decades of progress. psychological bulletin, 125: 276-302. Duggan, M. 2015. “Mobile messaging and social media – 2015.” pew research center. August 2015. Available at: http://www.pewinternet.org/2015/08/19/mobile-messaging-and-social-media-2015/
- Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A. and Danforth, C. M. (2011). Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter. PLoS ONE, 6, e26752. https://doi.org/10.1371/journal.pone.0026752.
- Frias-Martinez, V., Soto, V., Hohwald, H., and Frias-Martinez, E. 2013. Sensing urban land use with twitter activity.Google Scholar
- Fujisaka, T., Lee, R. and Sumiya, K. (2010). Exploring urban characteristics using movement history of mass mobile microbloggers. Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications. ACM.Google Scholar
- Gordon, J. 2013. Comparative geospatial analysis of twitter sentiment data during the 2008 and 2012 US presidential elections.Google Scholar
- Greenberg, M. R., and Schneider, D. 1996. Environmentally devastated neighborhoods: Perceptions, realities and policies. New Brunswick, NJ: Rutgers University Press.Google Scholar
- Hollander, J. B., Pallagst, K., Schwarz, T., & Popper, F. (2009). Planning shrinking cities. Progress in Planning, 72(4), 223–232.Google Scholar
- Hollander, Justin B., Erin Graves, Henry Renski, Cara Foster-Karim, Andrew Wiley, and Dibyendu Das. 2016. Urban social listening: Potential and pitfalls of using social media data in studying cities. New York: Palgrave Macmillan.Google Scholar
- Kramer, A. D. I. (2010). ‘An unobtrusive behavioral model of “gross national happiness”’. Proceedings of the 28th International Conference on Human factors in Computing Systems – CHI 10, 287–90. https://doi.org/10.1145/1753326.1753369.
- Lovelace, R., Malleson, N., Harland, K., and Birkin, M. 2014. Geotagged tweets to inform a spatial interaction model: a case study of museums. arXiv preprint arXiv:1403.5118.Google Scholar
- MacEachren, A.M., Robinson, A., Jaiswal1, A., Pezanowski, S., Savelyev, A., Blanford, J., and Mitra, P. 2011. Geo-twitter analytics: Applications in crisis management. 25th international cartographic conference. Google Scholar
- Mislove A., Lehmann, S., Ahn, Y. Y., Onnela, J. P. and Rosenquist, J. N. (2011). Understanding the Demographics of Twitter Users. The International AAAI Conference on Web and Social Media (ICWSM). July 17.Google Scholar
- O'Connor, B., Balasubramanyan, R., Routledge, B. R., & Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. ICWSM, 11, 122–129.Google Scholar
- Pennebaker, J.W., Boyd, R.L., Jordan, K., and Blackburn, K. 2015. The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin. https://doi.org/10.15781/T29G6Z.
- Poorthuis, A., & Zook, M. (2014). Artists and bankers and hipsters, oh my! Mapping Tweets in the New York Metropolitan Region. Cityscape: A Journal of Policy Development and Research, 16(2), 169–172.Google Scholar
- Quercia, D., Ellis, J., Capra, L., and Crowcroft, J. 2012. Tracking gross community happiness from tweets. Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. February 2012: 965–968. ACM.Google Scholar
- Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Lucas, R. E., Agrawal, M., Park, G., Lakshmikanth S.J., Seligmann M., and Ungar, L.H. (2013). Characterizing geographic variation in well-being using tweets. ICWSM.Google Scholar
- Schwarz, T. and Rugare, S. (ed.) 2008. Cities growing smaller. Vol. 1.” Urban-Infill. Cleveland: Kent State University Cleveland Urban Design Collaborative.Google Scholar
- Smith, A. & Brenner, J. (2012). ‘Twitter use 2012’, Pew Internet & American Life Project 4, http://www.pewinternet.org/2012/05/31/twitter-use-2012/. (accessed 1 March 2017).
- Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, 10, 178–185.Google Scholar