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The Fear of Ebola: A Tale of Two Cities in China

  • Xinyue Ye
  • Shengwen Li
  • Xining Yang
  • Jay Lee
  • Ling Wu
Chapter
Part of the Advances in Geographic Information Science book series (AGIS)

Abstract

Emerging social issues have often led to rumors breeding and propagation in social media in China. Public health-related rumors will harm social stability, and such noise negatively affects the quality of disease outbreak detection and prediction. In this chapter, we use the diffusion of Ebola rumors in social media networks as a case study. The topic of rumors is identified based on latent Dirichlet allocation method, and the diffusion process is explored using the space-time methods. By comparing Ebola rumors in the two cities, the chapter explores the relationship between the spread of rumors, user factors, and contents. The results show that: (1) rumors have a self-verification process; (2) rumors have strong aggregation characteristics, and similar rumors in different regions at the same period of time will lead to a synergistic effect; (3) non-authenticated users are more inclined to believe the rumors, while the official users play a major role in stopping rumors as they pay more attention to the fact; (4) the spread and elimination of rumors largely depend on the users who have more followers and friends; and (5) the topics of rumors are closely related to the local event.

Keywords

Ebola Rumor LDA Social media China 

Abbreviations

ESDA

Exploratory spatial data analysis

LDA

Latent Dirichlet allocation

Notes

Acknowledgments

This material is based upon the work supported by the National Science Foundation under Grant No. 1416509, with the project titled “Spatiotemporal Modeling of Human Dynamics Across Social Media and Social Networks.” Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

References

  1. Achrekar, H., Gandhe, A., Lazarus, R., Yu, S. H., & Liu, B. (2011, April). Predicting flu trends using twitter data. In Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on (pp. 702–707). IEEE.Google Scholar
  2. Anselin, L. (2004). Exploring spatial data with GeoDaTM: A workbook. Urbana, 51, 61801.Google Scholar
  3. Aslam, A. A., Tsou, M., Spitzberg, H. B., An, L., Gawron, M. J., Gupta, K. D., Peddecord, M. K., Nagel, C. A., Allen, C., Yang, J., & Lindsay, S. (2014). The reliability of tweets as a supplementary method of seasonal influenza surveillance. Journal of medical Internet research, 16, e250.CrossRefGoogle Scholar
  4. Bai, S., Yuan, S., Hao, B., & Zhu, T. (2014). Predicting personality traits of microblog users. Web Intelligence and Agent Systems: An International Journal, 12(3), 249–265.Google Scholar
  5. Benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2012). GenBank. Nucleic Acids Research, 41, s1195.CrossRefGoogle Scholar
  6. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.zbMATHGoogle Scholar
  7. Brady, O. J., Gething, P. W., Bhatt, S., Messina, J. P., Brownstein, J. S., Hoen, A. G., Moyes, C. L., Farlow, A. W., Scott, T. W., & Hay, S. I. (2012). Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Neglected Tropical Diseases, 6, e1760.CrossRefGoogle Scholar
  8. Brownstein, J. S., Freifeld, C. C., Reis, B. Y., & Mandl, K. D. (2008). Surveillance sans Frontieres: Internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Medicine, 5, e151.CrossRefGoogle Scholar
  9. Buckner, H. T. (1965). A theory of rumor transmission. Public Opinion Quarterly, 29, 54–70.CrossRefGoogle Scholar
  10. Chan, M. (2014). Ebola virus disease in West Africa--no early end to the outbreak. The New England Journal of Medicine, 371, 1183–1185.CrossRefGoogle Scholar
  11. Coelho, L. P., Peng, T., & Robert. (2010). Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing. Bioinformatics, 26, i7–i12.CrossRefGoogle Scholar
  12. Epstein, H. (2014). Ebola in Liberia: An epidemic of rumors. New York Review of Books, 61, 91.Google Scholar
  13. Floris, R., & Zoppi, C. (2015). Social media-related geographic information in the context of strategic environmental assessment of municipal Masterplans: A case study concerning Sardinia (Italy). Future Internet, 7, 276–293.CrossRefGoogle Scholar
  14. Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69(4), 211–221.CrossRefGoogle Scholar
  15. Grein, T. W., Kamara, K. B., Rodier, G., Plant, A. J., Bovier, P., Ryan, M. J., Ohyama, T., & Heymann, D. L. (2000). Rumors of disease in the global village: Outbreak verification. Emerging Infectious Diseases, 6, 97–102.CrossRefGoogle Scholar
  16. Grubesic, T. H., & Mack, E. A. (2008). Spatio-temporal interaction of urban crime. Journal of Quantitative Criminology, 24, 285–306.CrossRefGoogle Scholar
  17. Hay, S. I., George, D. B., Moyes, C. L., & Brownstein, J. S. (2013). Big data opportunities for global infectious disease surveillance. PLoS Medicine, 10, e1001413.CrossRefGoogle Scholar
  18. Hecht, B., Hong, L., Suh, B., & Chi, E.H. (2011). Tweets from Justin Bieber’s heart: The dynamics of the location field in user profiles. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 237–246). Vancouver: ACM.Google Scholar
  19. Huang, Q., & Xiao, Y. (2015). Geographic situational awareness: Mining tweets for disaster preparedness, emergency response, impact, and recovery. ISPRS International Journal of Geo-Information, 4(3), 1549–1568.CrossRefGoogle Scholar
  20. Indrawan-Santiago, M., Miyabe, M., Nadamoto, A., & Aramaki, E. (2014). How do rumors spread during a crisis? Analysis of rumor expansion and disaffirmation on twitter after 3.11 in Japan. International Journal of Web Information Systems, 10, 394–412.CrossRefGoogle Scholar
  21. Jin, F., Wang, W., Zhao, L., Dougherty, E., Cao, Y., Lu, C., & Ramakrishnan, N. (2014). Misinformation propagation in the age of twitter. Computer, 47, 90–94.CrossRefGoogle Scholar
  22. Kay, S., Zhao, B., & Sui, D. (2014). Can social media clear the air? A case study of the air pollution problem in Chinese cities. The Professional Geographer, 67, 1–13.Google Scholar
  23. King, D., Ramirez-Cano, D., Greaves, F., Vlaev, I., Beales, S., & Darzi, A. (2013). Twitter and the health reforms in the English National Health Service. Health Policy, 110, 291–297.CrossRefGoogle Scholar
  24. Liao, Q., & Shi, L. (2013). She gets a sports car from our donation: Rumor transmission in a Chinese microblogging community. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work (pp. 587–598). San Antonio: ACM.Google Scholar
  25. Lienou, M., Maitre, H., & Datcu, M. (2010). Semantic annotation of satellite images using latent Dirichlet allocation. Geoscience and Remote Sensing Letters, IEEE, 7, 28–32.CrossRefGoogle Scholar
  26. Liu, X., Wang, G. A., Johri, A., Zhou, M., & Fan, W. (2014). Harnessing global expertise: A comparative study of expertise profiling methods for online communities. Information Systems Frontiers, 16, 715–727.CrossRefGoogle Scholar
  27. Luckerson, V. (2014, October 7). Watch how word of Ebola exploded in America. Time.Google Scholar
  28. MacEachren, A.M., Robinson, A.C., Jaiswal, A., Pezanowski, S., Savelyev, A., Blanford, J., & Mitra, P. (2011). Geo-twitter analytics: Applications in crisis management. In 25th International Cartographic Conference (pp. 3–8).Google Scholar
  29. Maynard, D., Gossen, G., Funk, A., & Fisichella, M. (2014). Should I care about your opinion? Detection of opinion interestingness and dynamics in social media. Future Internet, 6, 457–481.CrossRefGoogle Scholar
  30. Mehrotra, R., Sanner, S., Buntine, W., & Xie, L. (2013). Improving LDA topic models for microblogs via tweet pooling and automatic labeling. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 889–892). Dublin: ACM.Google Scholar
  31. Mendoza, M., Poblete, B., & Castillo, C. (2010). Twitter under crisis: Can we trust what we RT? In Proceedings of the First Workshop on Social Media Analytics (pp. 71–79). Washington DC: ACM.Google Scholar
  32. Messina, J.P., Brady, O.J., Pigott, D.M., Brownstein, J.S., Hoen, A.G., & Hay, S.I. (2014). A global compendium of human dengue virus occurrence. EP.Google Scholar
  33. Milinovich, G. J., Williams, G. M., Clements, A. C. A., & Hu, W. (2014). Internet-based surveillance systems for monitoring emerging infectious diseases. The Lancet Infectious Diseases, 14, 160–168.CrossRefGoogle Scholar
  34. Mullen, P. B. (1972). Modern legend and rumor theory. Journal of the Folklore Institute, 95–109.Google Scholar
  35. Oh, O., Kwon, K. H., & Rao, H. R. (2010). An exploration of social media in extreme events: Rumor theory and twitter during the Haiti earthquake 2010. 1–14. ICIS, 231.Google Scholar
  36. Okada, Y., Ikeda, K., Shinoda, K., Toriumi, F., Sakaki, T., Kazama, K., Numao, M., Noda, I., & Kurihara, S. (2014). SIR-extended information diffusion model of false rumor and its prevention strategy for twitter. Journal Ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, 18, 598–607.Google Scholar
  37. Padmanabhan, A., Wang, S., Cao, G., Hwang, M., Zhang, Z., Gao, Y., Soltani, K., & Liu, Y. (2014). FluMapper: A cyberGIS application for interactive analysis of massive location-based social media. Concurrency and Computation: Practice and Experience, 26, 2253–2265.CrossRefGoogle Scholar
  38. Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2, 1–135.CrossRefGoogle Scholar
  39. Peterson, W. A., & Gist, N. P. (1951). Rumor and public opinion. American Journal of Sociology, 159–167.Google Scholar
  40. Plachouras, V., Carpentier, F., Faheem, M., Masanès, J., Risse, T., Senellart, P., Siehndel, P., & Stavrakas, Y. (2014). ARCOMEM crawling architecture. Future Internet, 6, 518–541.CrossRefGoogle Scholar
  41. Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P. & Welling, M. (2008). Fast collapsed gibbs sampling for latent dirichlet allocation. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 569–577). ACM.Google Scholar
  42. Rapoport, A., & Rebhun, L. I. (1952). On the mathematical theory of rumor spread. The Bulletin of Mathematical Biophysics, 14, 375–383.CrossRefMathSciNetGoogle Scholar
  43. Rogers, D. J., Wilson, A. J., Hay, S. I., & Graham, A. J. (2006). The global distribution of yellow fever and dengue. Advances in Parasitology, 62, 181–220.CrossRefGoogle Scholar
  44. Rosnow, R. L. (1988). Rumor as communication: A contextualist approach. Journal of Communication, 38, 12–28.CrossRefGoogle Scholar
  45. Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346, 1063–1064.CrossRefGoogle Scholar
  46. Sakaki, T., Okazaki, M., & Matsuo, Y. (2013). Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Transactions on Knowledge and Data Engineering, 25, 919–931.CrossRefGoogle Scholar
  47. Samaan, G., Patel, M., Olowokure, B., Roces, M. C., & Oshitani, H. (2005). Rumor surveillance and avian influenza H5N1. Emerging Infectious Diseases, 11, 463–466.CrossRefGoogle Scholar
  48. Sohu. (2014). China’s first Ebola-infected persons appeared Ningbo? Rumors!Google Scholar
  49. Spiro, E.S., Fitzhugh, S., Sutton, J., Pierski, N., Greczek, M., & Butts, C.T. (2012). Rumoring during extreme events: a case study of deepwater horizon 2010. In Proceedings of the 4th Annual ACM Web Science Conference (pp. 275–283). Evanston: ACM.Google Scholar
  50. Starbird, K., Maddock, J., Orand, M., Achterman, P., & Mason, R.M. (2014). Rumors, false flags, and digital vigilantes: Misinformation on twitter after the 2013 Boston marathon bombing. In iConference 2014 Proceedings (pp. 654–662). Boston.Google Scholar
  51. Sui, D. Z. (2008). The wikification of GIS and its consequences: Or Angelina Jolie’s new tattoo and the future of GIS. Computers, Environment and Urban Systems, 32(1), 1–5.CrossRefGoogle Scholar
  52. Velardi, P., Stilo, G., Tozzi, A. E., & Gesualdo, F. (2014). Twitter mining for fine-grained syndromic surveillance. Artificial Intelligence in Medicine, 153–163.Google Scholar
  53. Whalen, K. E., Páez, A., & Carrasco, J. A. (2013). Mode choice of university students commuting to school and the role of active travel. Journal of Transport Geography, 31, 132–142.CrossRefGoogle Scholar
  54. Widener, M. J., & Li, W. (2014). Using geolocated twitter data to monitor the prevalence of healthy and unhealthy food references across the US. Applied Geography, 54, 189–197.CrossRefGoogle Scholar
  55. Wong, W., & Lee, J. (2005). Statistical analysis of geographic information with ArcView GIS and ArcGIS. Wiley: New Jersey.Google Scholar
  56. Xie, H., Kung, C., & Zhao, Y. (2012). Spatial disparities of regional forest land change based on ESDA and GIS at the county level in Beijing-Tianjin-Hebei area. Frontiers of Earth Science, 6, 445–452.CrossRefGoogle Scholar
  57. Xie, H., Liu, Z., Wang, P., Liu, G., & Lu, F. (2013). Exploring the mechanisms of ecological land change based on the spatial autoregressive model: A case study of the Poyang lake eco-economic zone, China. International Journal of Environmental Research and Public Health, 11, 583–599.CrossRefGoogle Scholar
  58. Xinhuanet. (2014). Guangzhou now Ebola? Rumors! Guangdong Province Write appeared Ebola cases.Google Scholar
  59. Yang, X., Ye, X., & Sui, D. Z. (2016). We know where you are: In space and place-enriching the geographical context through social media. International Journal of Applied Geospatial Research (IJAGR), 7(2), 61–75.CrossRefGoogle Scholar
  60. Yap, L. F., Bessho, M., Koshizuka, N., & Sakamura, K. (2012). User-generated content for location-based services: A review. In Virtual communities, social networks and collaboration (pp. 163–179). New York: Springer.CrossRefGoogle Scholar
  61. Ye, X., & Carroll, M. C. (2011). Exploratory space-time analysis of local economic development. Applied Geography, 31, 1049–1058.CrossRefGoogle Scholar
  62. Ye, X., & Rey, S. (2013). A framework for exploratory space-time analysis of economic data. The Annals of Regional Science, 50, 315–339.CrossRefGoogle Scholar
  63. Zook, M., Graham, M., Shelton, T., & Gorman, S. (2010). Volunteered geographic information and crowdsourcing disaster relief: A case study of the Haitian earthquake. World Medical & Health Policy, 2(2), 7–33.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of GeographyKent Sate UniversityKentUSA
  2. 2.Department of Information EngineeringChina University of GeosciencesWuhanChina
  3. 3.Department of Geography and GeologyEastern Michigan UniversityYpsilantiUSA
  4. 4.College of Environment and Planning, Henan UniversityKaifengChina
  5. 5.Department of SociologyKent State UniversityKentUSA

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