Skip to main content

Spatiotemporal Analysis on Sentiments and Retweet Patterns of Tweets for Disasters

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11420))

Abstract

Twitter provides an important channel for public to share feelings, attitudes and concerns about disasters. In this study, we aim to explore how spatiotemporal factors affect people’s sentiment in disaster situations and how the area type, time stage and sentiment of the tweets affect the extent and speed of tweets’ diffusion. After analyzing 531,912 geo-tagged tweets about Hurricane Harvey, we found that on-site tweets are more positive than off-site tweets across the time; neutral tweets spread broader and faster than tweets with sentiment propensity; on-site tweets and tweets posted at early stages tend to be more popular. These findings could enable authorities and response organizations to better comprehend people’s feelings and behaviors in social media and their changes over time and space. In future, we will analyze the influence of the interactions among sentiment, location and time to retweet patterns.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Imran, M., Castillo, C., Diaz, F., et al.: Processing social media messages in mass emergency: a survey. ACM Comput. Surv. (CSUR) 47(4), 67 (2015)

    Article  Google Scholar 

  2. Cobo, A., Parra, D., Navón, J.: Identifying relevant messages in a twitter-based citizen channel for natural disaster situations. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1189–1194 (2015)

    Google Scholar 

  3. Vieweg, S.E.: Situational awareness in mass emergency: a behavioral and linguistic analysis of microblogged communications. University of Colorado at Boulder (2012)

    Google Scholar 

  4. Boyd D., Golder S., Lotan G.: Tweet, tweet, retweet: conversational aspects of retweeting on twitter. In: Proceedings of the 43rd Hawaii International Conference on System Sciences, pp. 1–10 (2010)

    Google Scholar 

  5. Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In: Proceedings of IEEE Second International Conference on Social Computing, pp. 177–184 (2010)

    Google Scholar 

  6. Pervin, N., Takeda, H., Toriumi, F.: Factors affecting retweetability: an event-centric analysis on Twitter. In: Proceedings of Thirty Fifth International Conference on Information Systems, pp. 1–10 (2014)

    Google Scholar 

  7. Zhang, L., Xu, L., Zhang, W.: Social media as amplification station: factors that influence the speed of online public response to health emergencies. Asian J. Commun. 27(3), 322–338 (2017)

    Article  Google Scholar 

  8. Neppalli, V.K., Caragea, C., Squicciarini, A., et al.: Sentiment analysis during Hurricane Sandy in emergency response. Int. J. Disaster Risk Reduct. 21, 213–222 (2017)

    Article  Google Scholar 

  9. Phillips, M.E.: Hurricane Harvey Twitter Dataset. https://digital.library.unt.edu/ark:/67531/metadc993940/. Accessed 22 Nov 2017

  10. Kryvasheyeu, Y., Chen, H., Moro, E., et al.: Performance of social network sensors during Hurricane Sandy. PLoS ONE 10(2), e117288 (2015)

    Article  Google Scholar 

  11. Texas Hurricane Harvey (DR-4332). https://www.fema.gov/disaster/4332. Accessed 5 Mar 2018

  12. Louisiana Tropical Storm Harvey (DR-4345). https://www.fema.gov/disaster/4345. Accessed 5 Mar 2018

  13. Powell, J.W.: An introduction to the natural history of disaster. University of Maryland: Disaster Research Project (1954)

    Google Scholar 

  14. Kogan, M., Palen, L., Anderson, K.M.: Think local, retweet global: retweeting by the geographically-vulnerable during Hurricane Sandy. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 981–993 (2015)

    Google Scholar 

  15. Ozturk, N., Ayvaz, S.: Sentiment analysis on Twitter: a text mining approach to the Syrian refugee crisis. Telemat. Inform. 35(1), 136–147 (2018)

    Article  Google Scholar 

  16. Thelwall, M., Buckley, K., Paltoglou, G., et al.: Sentiment strength detection in short informal text. J. Am. Soc. Inform. Sci. Technol. 61(12), 2544–2558 (2010)

    Article  Google Scholar 

  17. Thelwall, M., Buckley, K., Paltoglou, G., et al.: Sentiment in Twitter events. J. Am. Soc. Inf. Sci. Technol. 63(1), 163–173 (2012)

    Article  Google Scholar 

  18. SentiStrength. http://sentistrength.wlv.ac.uk/. Accessed 20 Mar 2018

  19. Tsugawa, S., Ohsaki, H.: Negative messages spread rapidly and widely on social media. In: Proceedings of the 2015 ACM on Conference on Online Social Networks, pp. 151–160 (2015)

    Google Scholar 

  20. Gaspar, R., Pedro, C., Panagiotopoulos, P., et al.: Beyond positive or negative: qualitative sentiment analysis of social media reactions to unexpected stressful events. Comput. Hum. Behav. 56, 179–191 (2016)

    Article  Google Scholar 

  21. Stieglitz, S., Dang-Xuan, L.: Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior. J. Manag. Inf. Syst. 29(4), 217–248 (2013)

    Article  Google Scholar 

  22. Kryvasheyeu, Y., Chen, H., Obradovich, N., et al.: Rapid assessment of disaster damage using social media activity. Sci. Adv. 2(3), e1500779 (2016)

    Article  Google Scholar 

  23. Yoo, E., Rand, W., Eftekhar, M., et al.: Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises. J. Oper. Manag. 45, 123–133 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

The paper is supported by the National Natural Science Foundation of China (No. 71790612, No. 71804135 and No. 71603189).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jin Mao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, S., Mao, J., Li, G. (2019). Spatiotemporal Analysis on Sentiments and Retweet Patterns of Tweets for Disasters. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15742-5_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15741-8

  • Online ISBN: 978-3-030-15742-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics