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Visualizing Urban vs. Rural Sentiments in Real-Time

  • Jackson Howell
  • Nathan Melenbrink
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Discrepancies in sentiment between urban and rural communities represent a divide which has garnered much media attention yet so far has yielded little research or analysis. In this research, we use sentiment analysis to parse tweets in order to reveal the mood of each demographic group when discussing specific topics. We expose this method through a publicly accessible web application for sentiment tracking. Users are able to track specific keywords on Twitter in order to collect data at different scales, filtering by country, state, or even neighborhood. Using this tool, we find that across a broad range of topics generally believed to be polarizing, urban and rural groups actually express very similar sentiment scores. These results suggest that even though two demographic groups might hold completely opposite views on an issue, there is usually a certain symmetry in the emotion that both groups bring to the discourse.

Keywords

Sentiment analysis Urban-rural divide NLP 

References

  1. 1.
  2. 2.
    The one million tweet map. https://onemilliontweetmap.com/
  3. 3.
    Avvenuti, M., Cresci, S., Marchetti, A., Meletti, C., Tesconi, M.: Ears (earthquake alert and report system): a real time decision support system for earthquake crisis management. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1749–1758. ACM (2014)Google Scholar
  4. 4.
    Berry, B.J., Okulicz-Kozaryn, A.: An urban-rural happiness gradient. Urban Geogr. 32(6), 871–883 (2011)CrossRefGoogle Scholar
  5. 5.
    Cao, X., MacNaughton, P., Deng, Z., Yin, J., Zhang, X., Allen, J.G.: Using twitter to better understand the spatiotemporal patterns of public sentiment: a case study in Massachusetts, USA. Int. J. Environ. Res. Public Health 15(2), 250 (2018)CrossRefGoogle Scholar
  6. 6.
    Cromartie, J., Bucholtz, S.: Defining the “rural” in rural america. Amber Waves 6(3), 28 (2008)Google Scholar
  7. 7.
    De Smedt, T., Daelemans, W.: pattern.en, April 2018. https://www.clips.uantwerpen.be/pages/pattern-en
  8. 8.
    França, U., Sayama, H., McSwiggen, C., Daneshvar, R., Bar-Yam, Y.: Visualizing the “heartbeat” of a city with tweets. Complexity 21(6), 280–287 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gamio, L.: Urban and rural america are becoming increasingly polarized. The Washington Post (2016)Google Scholar
  10. 10.
    Greenwood, S., Perrin, A., Duggan, M.: Social media update 2016. Pew Res. Cent. 11, 83 (2016)Google Scholar
  11. 11.
    Hamling, T., Agrawal, A.: Sentiment analysis of tweets to gain insights into the 2016 US election. Columbia Undergrad. Sci. J. 11, 34–42 (2017)Google Scholar
  12. 12.
    Hollander, J.B., Renski, H.: Measuring Urban Attitudes Using Twitter: An Exploratory Study. Lincoln Institute of Land Policy, Cambridge (2015)Google Scholar
  13. 13.
    Isella, A.: Critical values for the two-sample kolmogorov-smirnov test (2-sided). http://sparky.rice.edu/astr360/kstest.pdf
  14. 14.
    Jackson, J.E., Doescher, M.P., Jerant, A.F., Hart, L.G.: A national study of obesity prevalence and trends by type of rural county. J. Rural Health 21(2), 140–148 (2005)CrossRefGoogle Scholar
  15. 15.
    Koricich, A., Chen, X., Hughes, R.P.: Understanding the effects of rurality and socioeconomic status on college attendance and institutional choice in the united states. Rev. High. Educ. 41(2), 281–305 (2018)CrossRefGoogle Scholar
  16. 16.
    Lee, K., Agrawal, A., Choudhary, A.: Real-time disease surveillance using twitter data: demonstration on flu and cancer. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, pp. 1474–1477. ACM (2013)Google Scholar
  17. 17.
    Lynch, K.R., Logan, T., Jackson, D.B.: “People will bury their guns before they surrender them”: implementing domestic violence gun control in rural, appalachian versus urban communities. Rural Sociol. 83, 315–346 (2018)CrossRefGoogle Scholar
  18. 18.
    Mislove, A., Jørgensen, S., Ahn, Y.Y., Onnela, J.P., Rosenquist, J.: Understanding the demographics of twitter users, pp. 554–557. AAAI Press (2011)Google Scholar
  19. 19.
    Mitchell, L., Frank, M.R., Harris, K.D., Dodds, P.S., Danforth, C.M.: The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place. PloS One 8(5), e64417 (2013)ADSCrossRefGoogle Scholar
  20. 20.
    Novak, P.K., Smailović, J., Sluban, B., Mozetič, I.: Sentiment of emojis. PloS One 10(12), e0144296 (2015)CrossRefGoogle Scholar
  21. 21.
    Pang, B., Lee, L., et al.: Opinion mining and sentiment analysis. Found. Trends® Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  22. 22.
    Ratcliffe, M., Burd, C., Holder, K., Fields, A.: Defining rural at the us census bureau. United States Census Bureau (2016)Google Scholar
  23. 23.
    Roberts, H., Sadler, J., Chapman, L.: The value of twitter data for determining the emotional responses of people to urban green spaces: a case study and critical evaluation. Urban Stud. (2018).  https://doi.org/10.1177/0042098017748544
  24. 24.
    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, pp. 851–860. ACM (2010)Google Scholar
  25. 25.
    Sliwinski, A.: sentiment: Afinn-based sentiment analysis for node.js, April 2018. https://github.com/thisandagain/sentiment
  26. 26.
    Tsou, M.H., Jung, C.T., Allen, C., Yang, J.A., Han, S.Y., Spitzberg, B.H., Dozier, J.: Building a real-time geo-targeted event observation (geo) viewer for disaster management and situation awareness. In: International Cartographic Conference, pp. 85–98. Springer (2017)Google Scholar
  27. 27.
    Ulrich-Schad, J.D., Duncan, C.M.: People and places left behind: work, culture and politics in the rural united states. J. Peasant Stud. 45(1), 59–79 (2018)CrossRefGoogle Scholar
  28. 28.
    Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 US presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, pp. 115–120. Association for Computational Linguistics (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Harvard University Graduate School of DesignCambridgeUSA
  2. 2.Harvard University Wyss Institute for Biologically Inspired EngineeringCambridgeUSA

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