This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1349) New Methods (944) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 21, No 5 (2019): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12881, first published .
Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Authors of this article:

Zubair Shah1 Author Orcid Image ;   Paige Martin1 Author Orcid Image ;   Enrico Coiera1 Author Orcid Image ;   Kenneth D Mandl2, 3 Author Orcid Image ;   Adam G Dunn1 Author Orcid Image
Article Authors Cited by (16) Tweetations (18) Metrics

Journals

  1. Hu T, She B, Duan L, Yue H, Clunis J. A Systematic Spatial and Temporal Sentiment Analysis on Geo-Tweets. IEEE Access 2020;8:8658 View
  2. Abd-Alrazaq A, Alhuwail D, Househ M, Hamdi M, Shah Z. Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study. Journal of Medical Internet Research 2020;22(4):e19016 View
  3. Hamdi A, Shaban K, Erradi A, Mohamed A, Rumi S, Salim F. Spatiotemporal data mining: a survey on challenges and open problems. Artificial Intelligence Review 2022;55(2):1441 View
  4. Shah U, Abd-alrazaq A, Schneider J, Househ M, Shah Z. Twitters’ Concerns and Opinions About the COVID-19 Booster Shots: Infoveillance Study. Journal of Consumer Health on the Internet 2022;26(4):337 View
  5. Deori M, Kumar V, Verma M. What news sparks interest on YouTube? A study of news content uploaded by India's top five Hindi news networks. Online Information Review 2023;47(3):550 View
  6. Morrison P, Rossouw S, Greyling T. The impact of exogenous shocks on national wellbeing. New Zealanders’ reaction to COVID-19. Applied Research in Quality of Life 2022;17(3):1787 View
  7. Deori M, Verma M, Kumar V. Sentiment Analysis of Users’ Comments on Indian Hindi News Channels Using Mozdeh: An Evaluation Based on YouTube Videos. Journal of Creative Communications 2021:097325862110492 View
  8. Калабихина И, Лукашевич Н, Банин Е, Алибаева К, Ребрей С, Kurshev E. Automatic extraction of social network users' attitudes on reproductive behavior issues. Program Systems: Theory and Applications 2021;12(4):33 View
  9. Kalabikhina I, Zubova E, Loukachevitch N, Kolotusha A, Kazbekova Z, Banin E, Klimenko G. Identifying Reproductive Behavior Arguments in Social Media Content Users’ Opinions through Natural Language Processing Techniques. Population and Economics 2023;7(2):40 View
  10. Low J, Fung B, Iqbal F. Of Stances, Themes, and Anomalies in COVID-19 Mask-Wearing Tweets. IEEE Access 2023;11:87009 View
  11. Biswas M, Shah Z. Extracting factors associated with vaccination from Twitter data and mapping to behavioral models. Human Vaccines & Immunotherapeutics 2023;19(3) View
  12. P V, V M. Sentiment analysis of multi social media using machine and deep learning models: a review. Multimedia Tools and Applications 2024 View
  13. Almaslukh A, Almaalwy A, Allheeib N, Alajaji A, Almukaynizi M, Alabdulkarim Y. Top-k sentiment analysis over spatio-temporal data. PeerJ Computer Science 2024;10:e2297 View

Books/Policy Documents

  1. Lakshmi Shree K. , Ashok Kumar R. . Global Perspectives on the Strategic Role of Marketing Information Systems. View
  2. Kalabikhina I, Loukachevitch N, Banin E, Kolotusha A. Population and Development in the 21st Century - Between the Anthropocene and Anthropocentrism. View