IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v12y2025i1d10.1057_s41599-025-05134-x.html
   My bibliography  Save this article

Unveiling the spatiotemporal propagation patterns of sentiments regarding the Israeli–Palestinian military conflict

Author

Listed:
  • Dajiang Wang

    (Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Songshan Yue

    (Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University
    State Key Laboratory Cultivation Base of Geographical Environment Evolution and Regional Response (Jiangsu Province))

  • Yongning Wen

    (Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Kai Wu

    (Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Teng Zhong

    (Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Min Chen

    (Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Zhaoyuan Yu

    (Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Linwang Yuan

    (Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Guonian Lü

    (Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

Abstract

Military conflicts occur in the physical world, while conflicting opinions simultaneously emerge in cyberspace. The rise of social media has facilitated the dissemination and exchange of these opinions. This paper examines the spatiotemporal propagation patterns of sentiments expressed by X.com (formerly Twitter) users regarding “Israel and Palestine” since October 7, 2023. Approximately 151,400 tweets were collected from 146 countries and 293 cities. A BERT model was fine-tuned to classify these tweets into six basic sentiment categories: anger, fear, joy, love, sadness, and surprise. In the spatiotemporal propagation analysis, community detection in social networks was employed to explore the spatial propagation patterns of sentiments. Time series and indicator data from different geographic regions were clustered and classified to identify the main factors influencing sentiment propagation. The results indicate that (1) the sentiment propagation network is divided into multiple communities (e.g., the US-group, the France-group, and the UK-group) with clear geographic clustering characteristics, suggesting that X.com users are inclined to engage in sentiment exchanges regarding “Israel and Palestine” with others from similar regions; (2) the six basic sentiments expressed by X.com users are more frequently propagated within individual countries, while international sentiment propagation is concentrated in regions such as Northern America and Western Europe; and (3) sentiments expressed by X.com users in geographic regions with similar factors—such as economy, Internet usage, and religion—exhibit aligned patterns in time-series trends (e.g., Northern America, Europe, Australia, and New Zealand). These findings offer a global perspective for understanding the Israeli‒Palestinian military conflict.

Suggested Citation

  • Dajiang Wang & Songshan Yue & Yongning Wen & Kai Wu & Teng Zhong & Min Chen & Zhaoyuan Yu & Linwang Yuan & Guonian Lü, 2025. "Unveiling the spatiotemporal propagation patterns of sentiments regarding the Israeli–Palestinian military conflict," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-17, December.
  • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05134-x
    DOI: 10.1057/s41599-025-05134-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-025-05134-x
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-025-05134-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    2. Lingli Yu & Ling Yang, 2024. "News media in crisis: a sentiment and emotion analysis of US news articles on unemployment in the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    3. Faheem Aslam & Tahir Mumtaz Awan & Jabir Hussain Syed & Aisha Kashif & Mahwish Parveen, 2020. "Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-9, December.
    4. Clément Bénard & Sébastien Da Veiga & Erwan Scornet, 2022. "Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA [Explaining individual predictions when features are dependent: more accurate approximations to ," Biometrika, Biometrika Trust, vol. 109(4), pages 881-900.
    5. Shalak Mendon & Pankaj Dutta & Abhishek Behl & Stefan Lessmann, 2021. "A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters," Information Systems Frontiers, Springer, vol. 23(5), pages 1145-1168, September.
    6. Marina Paolanti & Adriano Mancini & Emanuele Frontoni & Andrea Felicetti & Luca Marinelli & Ernesto Marcheggiani & Roberto Pierdicca, 2021. "Tourism destination management using sentiment analysis and geo-location information: a deep learning approach," Information Technology & Tourism, Springer, vol. 23(2), pages 241-264, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jyoti Choudrie & Shruti Patil & Ketan Kotecha & Nikhil Matta & Ilias Pappas, 2021. "Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study," Information Systems Frontiers, Springer, vol. 23(6), pages 1431-1465, December.
    2. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    3. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    4. Mohamed Zine & Fouzi Harrou & Mohammed Terbeche & Mohammed Bellahcene & Abdelkader Dairi & Ying Sun, 2023. "E-Learning Readiness Assessment Using Machine Learning Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    5. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    6. Juwon Hwang & Porismita Borah & Dhavan Shah & Markus Brauer, 2021. "The Relationship among COVID-19 Information Seeking, News Media Use, and Emotional Distress at the Onset of the Pandemic," IJERPH, MDPI, vol. 18(24), pages 1-13, December.
    7. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
    8. Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
    9. Ilias Thomas & Alex M. Dickens & Jussi P. Posti & Endre Czeiter & Daniel Duberg & Tim Sinioja & Matilda Kråkström & Isabel R. A. Retel Helmrich & Kevin K. W. Wang & Andrew I. R. Maas & Ewout W. Steyer, 2022. "Serum metabolome associated with severity of acute traumatic brain injury," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    10. Lu, Xuefei & Baraldi, Piero & Zio, Enrico, 2020. "A data-driven framework for identifying important components in complex systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    11. Omid Shafiezad & Hamid Mostofi, 2024. "Sentiment Analysis of Berlin Tourists’ Food Quality Perception Through Artificial Intelligence," Tourism and Hospitality, MDPI, vol. 5(4), pages 1-22, December.
    12. Nirma Sadamali Jayawardena & Aastha Behl, 2025. "Adverse effects of using gamification elements in online communities: a scoping review," Information Systems and e-Business Management, Springer, vol. 23(1), pages 69-97, March.
    13. repec:jdm:journl:v:17:y:2022:i:4:p:745-767 is not listed on IDEAS
    14. Mahyar Jahaninasab & Ehsan Taheran & S. Alireza Zarabadi & Mohammadreza Aghaei & Ali Rajabpour, 2023. "A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers," Energies, MDPI, vol. 16(13), pages 1-13, July.
    15. Jinwen Tang & Jinlin Cheng & Min Zhang, 2024. "Forecasting Airbnb prices through machine learning," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(1), pages 148-160, January.
    16. Jianghong Xu & Wei Lu & Weixin Wang, 2024. "From “fragile smallholders” to “resilient smallholders”: measuring rural household resilience in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    17. repec:hal:spmain:info:hdl:2441/20hflp7eqn97boh50no50tv67n is not listed on IDEAS
    18. Junqi Wang & Rundong Liu & Linfeng Zhang & Hussain Syed ASAD & Erlin Meng, 2019. "Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches," Energies, MDPI, vol. 12(20), pages 1-20, October.
    19. Liu, Yehong & Yin, Guosheng, 2020. "The Delaunay triangulation learner and its ensembles," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    20. Su, Chi-Wei & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Mirza, Nawazish & Umar, Muhammad, 2022. "COVID19: A blessing in disguise for European stock markets?," Finance Research Letters, Elsevier, vol. 49(C).
    21. Aassve, Arnstein & Capezzone, Tommaso & Cavalli, Nicolo' & Conzo, Pierluigi & Peng, Chen, 2022. "Trust in the time of coronavirus: longitudinal evidence from the United States," SocArXiv vwzk7, Center for Open Science.
    22. Ha, Tran Vinh & Asada, Takumi & Arimura, Mikiharu, 2019. "Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods," Journal of Transport Geography, Elsevier, vol. 78(C), pages 70-86.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05134-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.