Abstract
The migrant caravan that recently came out from Central towards North America generated polarized opinions in online social networks. The objective of this paper is to explore the social spatial-temporal trends that emerge from this migrant caravan phenomenon, and based on a combination of social media and newspaper opinions and reports, together with additional socio-economic data. The framework combines text data mining, text clustering, sentiment analysis and spatiotemporal data exploration. The study reveals significant ethnic polarization and ideological patterns but noticeable regional differences in rural and urban areas. The experimental study shows that our approach provides a valuable experimental framework to study emerging regional phenomena as they appear from social media.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lee, S., Xenos, M.: Social distraction? Social media use and political knowledge in two US Presidential elections. Comput. Hum. Behav. 90, 18–25 (2019)
Bogolyubova, O., Panicheva, P., Tikhonov, R., Ivanov, V., Ledovaya, Y.: Dark personalities on Facebook: Harmful online behaviors and language. Comput. Hum. Behav. 78, 151–159 (2018). https://doi.org/10.1016/j.chb.2017.09.032
Bode, L.: Political news in the news feed: Learning politics from social media. Mass Commun. Soc. 19(1), 24–48 (2016)
Srivastava, A.N., Sahami, M.: Text Mining: Classification, Clustering, and Applications. CRC Press (2009). 328 pages
Witanto, J. N., Lim, H., Atiquzzaman, M.: Smart government framework with geo-crowdsourcing and social media analysis. Future Generation Computer Systems. Hruby, F.: From third-person to first-person cartographies with immersive virtual environments. Proc. Int. Cartogr. Assoc., 2, 44 (2019). https://doi.org/10.5194/ica-proc-2-44-2019
Aggarwal, C.C, Zhai, C.: Mining Text Data. Springer Editorial
Council of Europe (1997). https://rm.coe.int/CoERMPublicCommonSearchServices/DisplayDCTMContent?documentId=0900001680505d5b. Accessed 16 Feb 2017
Rouse, R.: Political Polarization in the US, 14 September 2017. https://public.tableau.com/en-us/s/gallery/political-polarization-us
Huang, Q., Zhang, L., Cheng, Y., Li, P., Li, W.: Detecting tweets against blacks. Adv. Synth. Catal. 360(17), 3266–3270 (2018). https://doi.org/10.1002/adsc.201800642
Miranda, C.A., Rodriguez, R.C., Zagal-Flores, R.: Arquitectura Web para análisis de sentimientos en Facebook con enfoque semántico. Res. Comput. Sci. 75, 59–69
Bi, T., Liang, P., Tang, A., Yang, C.: A systematic mapping study on text analysis techniques in software architecture. J. Syst. Softw. 144, 533–558 (2018). https://doi.org/10.1016/j.jss.2018.07.055
Cranley, E.: This is insider, news digital media, October 2018. https://www.thisisinsider.com/where-the-caravan-is-map-coming-to-us-border-mexico-2018-10
Chen, X., Zhou, X., Sellis, T., Li, X.: Social event detection with retweeting behavior correlation. Expert Syst. Appl. 114, 516–523 (2018). https://doi.org/10.1016/j.eswa.2018.08.022
Agarwal, A., Singh, R., Toshniwal, D.: Geospatial sentiment analysis using Twitter data for UK-EU referendum. J. Inf. Optim. Sci. 39(1), 303–317 (2018)
Srivastava, A.N., Sahami, M.: Text Mining: Classification, Clustering, and Applications. CRC Press (2009)
Barker, J.L.P., Macleod, C.J.A.: Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities. In: Environmental Modelling & Software, vol. 115, pp. 213–227 (2019). ISSN 1364-8152
Giuseppe, S., Leonardo, T.: Statistical dissemination systems and the web. In: Handbook of Research on Public Information Technology, pp. 578–591. IGI Global (2008)
Acknowledgments
The authors want to thank God, SIP (Secretaría de Investigación y Posgrado), IPN (Instituto Politécnico Nacional), COFAA (Comisión de Operación y Fomento a las Actividades Académicas del IPN), ESCOM and UPIITA IPN for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zagal-Flores, R., Mata, M.F., Claramunt, C. (2020). A Social-Spatial Data Approach for Analyzing the Migrant Caravan Phenomenon. In: Di Martino, S., Fang, Z., Li, KJ. (eds) Web and Wireless Geographical Information Systems. W2GIS 2020. Lecture Notes in Computer Science(), vol 12473. Springer, Cham. https://doi.org/10.1007/978-3-030-60952-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-60952-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60951-1
Online ISBN: 978-3-030-60952-8
eBook Packages: Computer ScienceComputer Science (R0)