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:: Volume 3, Issue 3 (12-2025) ::
Health Res Develop 2025, 3(3): 22-27 Back to browse issues page
Sentiment Analysis of Twitter Users during the 12-Day Iran-Israel War: A Psychological Approach
Nader Sharifi1 , Kia Jahanbin2 , Mohammad Jokar3 , Vahid Rahmanian *4
1- Associated Proffessor, Department of Public Health, Khomein University of Medical Sciences, Khomein, Iran
2- Department of Computer Engineering Yazd University, Yazd, Iran
3- University of Calgary, Calgary, AB T2N 1N4, Canada
4- Assistant Professor in Epidemiology, Department of Public Health, Torbat Jam Faculty of Medical Sciences, Torbat Jam, Iran , vahid.rahmani1392@gmail.com
Abstract:   (198 Views)
Background: Social networks, especially Twitter, have become an effective platform for reflecting the views of individuals and elites on world political and social events. The aim of this study was to psychologically analyze Twitter users' reactions to the 12-day Iran-Israel war (June 2025) with a focus on the constructs of action guides (HBM) and subjective norms (TPB).
Materials: To ensure credibility and reduce noise, 3,367 tweets were selected from a total of 35,428 tweets posted by verified accounts between June 14 and 25 and analyzed using the fuzzy classifier Eclass1-MIMO.
Results: The results indicated that 44% of the tweets were negative, 46% were neutral, and only 10% were positive. The findings showed that regions with a history of social and historical tensions exhibited the highest proportion of negative sentiments, while positive tweets often contained Cues to Action, which were shared 2.3 times more frequently. Additionally, tweets based on Subjective Norms, emphasizing social acceptance or rejection, achieved the highest engagement rates.
Conclusion: The study highlights the critical role of verified influencers in shaping public sentiment and the potential dangers of unchecked information flow in polarized environments. It recommends that media policymakers and platforms enhance their content verification mechanisms, issue clear warnings on risky content, and promote messages aligned with constructive norms to counter misinformation and foster public trust in times of crisis.
 
Keywords: Sentiment analysis, Twitter, War
Full-Text [PDF 948 kb]   (61 Downloads)    
Type of Study: Review | Subject: General
Received: 2025/07/12 | Accepted: 2025/08/29 | Published: 2025/08/30
References
1. Kharde V, Sonawane P. Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971. 2016.
2. Barbosa L, Feng J. Robust sentiment detection on twitter from biased and noisy data. InColing 2010: Posters 2010 (pp. 36-44).
3. Covello VT. Best practices in public health risk and crisis communication. Journal of health communication. 2003 Jun 1;8(S1):5-8. [DOI:10.1080/713851971] [PMID]
4. Karami A, Shah V, Vaezi R, Bansal A. Twitter speaks: A case of national disaster situational awareness. Journal of Information Science. 2020 Jun;46(3):313-24. [DOI:10.1177/0165551519828620]
5. Vosoughi S, Roy D, Aral S. The spread of true and false news online. science. 2018;359(6380):1146-51. [DOI:10.1126/science.aap9559] [PMID]
6. Keller TR, Klinger U. Social bots in election campaigns: Theoretical, empirical, and methodological implications. Political Communication. 2019 Jan 2;36(1):171-89. [DOI:10.1080/10584609.2018.1526238]
7. Mao Z, Wang D, Zheng S. Health belief model and social media engagement: a cross-national study of health promotion strategies against COVID-19 in 2020. Frontiers in Public Health. 2023;11:1093648. [DOI:10.3389/fpubh.2023.1093648] [PMID] []
8. Kharde V, Sonawane P. Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971. 2016 Jan 26.
9. Geber S, Fretwurst B, Vogler D, Siegen D, Eisenegger M, Friemel T. Norm Setting in Times of Crisis: A Time-Series Analysis of the Dynamics Between Media Reporting and Perceived Norms in the Context of the COVID-19 Vaccination Roll-Out. Mass Communication and Society. 2024, 19:1-25. [DOI:10.1080/15205436.2024.2389833]
10. Varni A, Thai CL, Jamaleddine S. Using an Instagram campaign to influence knowledge, subjective norms, perceived behavioral control, and behavioral intentions for sustainable behaviors. Frontiers in Psychology. 2024;15:1377211. [DOI:10.3389/fpsyg.2024.1377211] [PMID] []
11. Jahanbin K, Rahmanian V. Using Twitter and web news mining to predict COVID-19 outbreak. Asian Pacific Journal of Tropical Medicine. 2020;13:1-3 [DOI:10.4103/1995-7645.279651]
12. Wang D, Yue S, Wen Y, Wu K, Zhong T, Chen M, Yu Z, Yuan L, Lü G. Unveiling the spatiotemporal propagation patterns of sentiments regarding the Israeli-Palestinian military conflict. Humanities and Social Sciences Communications. 2025;12(1):1-7. [DOI:10.63313/SSH.2003]
13. Pina JM. Public Opinion in Conflict Situations: A Sentiment Analysis of Tweets About Russia During the War on Ukraine. Defence and Peace Economics. 2025;36(3):292-306. [DOI:10.1080/10242694.2024.2381911]
14. Hameleers M, Garnier Ortiz M. Risk Perceptions of Misinformation Exposure Across Platforms, Issues, Modalities, and Countries: A Comparative Study Across the Global North and South. The International Journal of Press/Politics. 2024 7:19401612241304050. [DOI:10.1177/19401612241304050]
15. Caulfield M, Bayar MC, Aske AB. The 'new elites' of X: Identifying the most influential accounts engaged in Hamas/Israel discourse. Center for an informed public, Washington University. 2023.
16. Kim Y, Lim H. Alleviating the Bandwagon Effect of Crisis Misinformation on Social Media: Understanding Social Media Users' Bandwagon Perceptions and the Credibility of Crisis Misinformation to Protect Organizational Reputation. Communication Studies. 2025 Apr 9:1-29. [DOI:10.1080/10510974.2025.2485368]
17. Gruzd A, Mai P, Soares FB. To Share or Not to Share: Randomized Controlled Study of Misinformation Warning Labels on Social Media. InMultidisciplinary International Symposium on Disinformation in Open Online Media 2024 , 31 (pp. 46-69). Cham: Springer Nature Switzerland. [DOI:10.1007/978-3-031-71210-4_4]
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Sharifi N, Jahanbin K, Jokar M, Rahmanian V. (2025). Sentiment Analysis of Twitter Users during the 12-Day Iran-Israel War: A Psychological Approach. Health Res Develop. 3(3), 22-27.
URL: http://jhrd.trjums.ac.ir/article-1-121-en.html
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Volume 3, Issue 3 (12-2025) Back to browse issues page
پژوهش و توسعه سلامت Health research and development
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