Iranian Journal of Information Processing and Management

Iranian Journal of Information Processing and Management

Detection and Classification of Twitter Users' Opinions on Drought Crises in Iran Using Machine Learning Techniques

Document Type : Original Article

Authors
somayeh labafi 1
Leila Rabiei 2
Zeinab Rajabi 3
1 Associate Professor, Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran
2 Iran Telecommunication Research Center (ITRC), Tehran, Iran,
3 Faculty member in Computer Engineering Department, Hazrat-e Masoumeh University, Qom, Iran, z.rajabi@hmu.ac.ir
10.22034/jipm.2025.2043182.1806
Abstract
Drought crisis is one of the most significant environmental and social challenges in Iran, exerting widespread impacts on people's lives and the resilience of society. Identifying and analyzing the opinions of Persian-speaking Twitter users regarding this crisis can contribute to a better understanding of public opinion and support policymakers in their decision-making processes. This study aims to develop a model for detecting and categorizing user opinions related to drought crisis on the Twitter platform, utilizing machine learning and text mining methods to provide a more precise analysis of perspectives.
At the outset of this study, Twitter content related to the drought crisis in Iran was systematically examined through the lens of Regulatory Focus Theory, enabling the categorization of user opinions within this theoretical framework. The dataset comprised 42,028 tweets collected over a one-year period using pertinent keywords associated with drought crises in Iran. A qualitative thematic analysis was subsequently performed on a representative sample of 2,300 tweets, which were meticulously labeled and classified. The thematic analysis revealed four principal categories of user opinions. In accordance with Regulatory Focus Theory, promotive tweets were further subdivided into Gain and Non-gain subcategories, while preventive tweets were classified into Non-losses and Losses subcategories. In the subsequent phase, a machine learning model employing logistic regression combined with word embedding vectors was developed to automatically predict and classify tweets into these defined categories. The dataset was processed and analyzed utilizing advanced text mining techniques and machine learning algorithms to ensure precise and robust categorization.
The developed model effectively classified user opinions into four distinct categories related to the drought crisis and community resilience in Iran. It achieved an accuracy of 66.09% and an F1-score of 60%, reflecting a satisfactory level of performance in opinion classification. These results indicate that the model can reliably identify and categorize diverse user perspectives expressed on Twitter, thereby providing valuable insights to policymakers for a more informed understanding of public opinion dynamics.
This study presents the proposed model as a novel contribution to the social sciences, particularly within the domain of drought and water crisis management, representing the first instance of framing this issue as a machine learning problem. The construction of a unique dataset, combined with the application of Regulatory Focus Theory to establish meaningful and contextually relevant categories, laid a robust foundation for the development of the machine learning framework.
Integrating social science theories within the context of social media platforms facilitates more nuanced analyses aimed at understanding the underlying reasons and mechanisms driving changes in user behavior within these digital environments. Accordingly, this study applies Regulatory Focus Theory to extract and categorize user opinions expressed on Twitter concerning resilience to drought. Within this framework, Regulatory Focus Theory provides a foundational perspective for interpreting users’ motivations in producing and sharing content related to drought resilience on the Twitter platform.
This research contributes to the field by developing a machine learning–based model for opinion detection and classification of Persian-language tweets addressing the drought crisis. Beyond advancing machine learning methodologies in this area, the study establishes a vital foundation for future research in the social sciences. The proposed model offers policymakers a valuable tool to intelligently evaluate community resilience and to gain deeper insights into public reactions during crises.
Moreover, this study enriches the existing knowledge base in Persian opinion mining and its practical applications in crisis management. Future research can further enhance the model by incorporating larger datasets and employing more advanced deep learning techniques, as well as expanding its applicability to other environmental crises.
Keywords
Resilience, Drought, Twitter, Opinion Detection, Machine Learning, Logistic Regression Method
Subjects
References:
Agarwal, Raksha, Sharut Gupta, and Niladri Chatterjee. 2022. Profiling fake news spreaders on twitter: a clickbait and linguistic feature based scheme. In International Conference on Applications of Natural Language to Information Systems, 345-57. London: Springer.
Andia, T., & N. Chorev. 2021. Inequalities and Social Resilience in Times of COVID-19. Revista de Estudios Sociales 78: 2-13.
Bargués, P. 2020. Peacebuilding without peace? On how pragmatism complicates the practice of international intervention. Review of International Studies 46 (2): 237-255.
Berawi, M. A. 2020. Empowering healthcare, economic, and social resilience during global pandemic COVID-19. International Journal of Technology 11 (3): 436-439.
Blanc, J., A. Q. Briggs, A. A. Seixas, M. Reid, G. Jean-Louis, & S. R. Pandi-Perumal. 2021. Addressing psychological resilience during the coronavirus disease 2019 pandemic: a rapid review. Current opinion in psychiatry 34 (1): 29-35.
Bozdağ, F., & N. Ergün. 2020. Psychological resilience of healthcare professionals during COVID-19 pandemic. Psychological reports 11 (2): 12-33.
Braun, V., & V. Clarke. 2021. To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative research in sport, exercise and health 13 (2): 201-216.
Brzezinski, J. R. and G.J. Knafl. Logistic regression modeling for context-based classification. in Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99. 1999. IEEE. Spain.
Burns, R., & G. Wark. 2020. Where’s the database in digital ethnography? Exploring database ethnography for open data research. Qualitative Research 20 (5): 598-616.
Colón-Emeric, C., C. F. Pieper, K. E. Schmader, R. Sloane, A. Bloom, M. McClain, ... & H. E. Whitson. 2020. Two approaches to classifying and quantifying physical resilience in longitudinal data. The Journals of Gerontology: Series A 75 (4): 731-738.
de Seta, G. 2020. Three lies of digital ethnography. Journal of Digital Social Research 2 (1): 77-97.
Dillon, L., L. S. Neo, J. D. & Freilich,. 2020. A comparison of ISIS foreign fighters and supporter’s social media posts: an exploratory mixed-method content analysis. Behavioral sciences of terrorism and political aggression 12 (4): 268-291.
Fatahi, S., M. Vahedi, M. B. Arayesh & R. Eshraghi Samani. 2023. An assessment of farmers’ social resilience level in the face of water-based crises: The case of Haris County. Iranian Journal of Agricultural Economics and Development Research, 54-2 (1): 107-129. DOI: http//doi.org/10.22059/IJAEDR.2022.338067.669128.
Garcia, D., & B. Rimé. 2019. Collective emotions and social resilience in the digital traces after a terrorist attack. Psychological science 30 (4): 617-628.
Ghatrani, N, M. Shams, & S. Rahmani. 2018. The Role of Quality of Life in Building Social Resilience. Haft Hesar Journal of Environ Studies 6 (24): 41-54. In Persian.
Giannotta, A. P. 2020. Qualitative relationism about subject and object of perception and experience. Phenomenology and the Cognitive Sciences 14 (3): 1-20.
Góralska, M. 2020. Anthropology from Home: Advice on Digital Ethnography for the Pandemic Times. Anthropology in Action 27 (1): 46-52.
Guba, E. G., & Y. S. Lincoln. 1994. Competing paradigms in qualitative research. Handbook of qualitative research 2 (163-194): 105.
Guyon, H., J. L. Kop, J. Juhel, & B. Falissar. 2018. Measurement, ontology, and epistemology: Psychology needs pragmatism-realism. Theory & Psychology 28 (2): 149-171.
Haken, H., & J. Portugali. 2021. Urban Scaling, Urban Regulatory Focus and Their Interrelations. In Synergetic Cities: Information, Steady State and Phase Transition (pp. 199-215). Cham: Springer.
Hamzekhani, F. G., B. aghafian, & S. Araghinejad. 2016. Environmental management in Urmia Lake: thresholds approach. International Journal of Water Resources Development 32 (1): 77-88.
Hass, B. S. 2020. The burka ban: Islamic dress, freedom and choice in the Netherlands in light of the 2019 burka ban law. Religions 11 (2): 93.
Higgins, E. T., & F. Pinelli. 2020. Regulatory focus and fit effects in organizations. Annual Review of Organizational Psychology and Organizational Behavior 7: 25-48.
Higgins, E. T., E. Nakkawita, J. F. & Cornwell. 2020. Beyond outcomes: How regulatory focus motivates consumer goal pursuit processes. Consumer Psychology Review 3 (1): 76-90.
Hou, W. K., H. Tong, L. Liang, T. W. Li, H. Liu, M. Ben-Ezra, ... & T. M. C. Lee. 2021. Probable anxiety and components of psychological resilience amid COVID-19: A population-based study. Journal of affective disorders 282: 594-601.
Hu, F. W., C. H. Lin, P. H., Lai, & C. Y. Lin. 2021. Predictive Validity of the Physical Resilience Instrument for Older Adults (PRIFOR). The journal of nutrition, health & aging 25 (9): 1042-1045.
Hwang, C., & T. H. Kim. 2020. Religiosity and modesty: how veiled Muslim women in the United States define modest activewear. International Journal of Fashion Design, Technology and Education 13 (2): 131-139.
Indra, S., L. Wikarsa, and R. Turang. 2016. Using logistic regression method to classify tweets into the selected topics. in 2016 international conference on advanced computer science and information systems (icacsis). 2016. IEEE. Italy.
Jia, R. M., A. Y. Mikami, & S. Normand. 2021. Social Resilience in Children with ADHD: Parent and Teacher Factors. Journal of Child and Family Studies 30 (4): 839-854.
Killgore, W. D., E. C. Taylor, S. A. Cloonan, & N. S. Dailey. 2020. Psychological resilience during the COVID-19 lockdown. Psychiatry research 291: 113-123.
Klatt, S., & B. Noël. 2020. Regulatory focus in sport revisited: Does the exact wording of instructions really matter? Sport, Exercise, and Performance Psychology 9 (4): 532-545.
Klockner, K., P. Shields, M. Pillay, & K. Ames. 2021. Pragmatism as a teaching philosophy in the safety sciences: A higher education pedagogy perspective. Safety science 138: 105-125.
Krishen, A. S., O. Berezan, & C. Raab. 2019. Feelings and functionality in social networking communities: A regulatory focus perspective. Psychology & Marketing 36 (7): 675-686.
Lahlou, Yasmine, Sanaa El Fkihi, and Rdouan Faizi. 2021. Automatic Detection of Fake News on Twitter by Using a New Feature: User Credibility. In International Conference On Big Data and Internet of Things, 568-80. Paris: Springer.
Lee, Colin Jingwei, and Hui Na Chua. 2022. Using Linguistics and Psycholinguistics Features in Machine Learning for Fake News Classification Through Twitter. In Proceedings of International Conference on Data Science and Applications: ICDSA 2021, Volume 1, 717-30. Spain: Springer.
Lim, K. K., D. B. Matchar, C. S. Tan, W. Yeo, T. Østbye, T. S. Howe, & J. S. Koh. 2020. The association between psychological resilience and physical function among older adults with hip fracture surgery. Journal of the American Medical Directors Association 21 (2): 260-266.
Mazoochi, M., L. Rabiei, F. Rahmani, and Z. Rajabi. 2025. "Constructing colloquial dataset for persian sentiment analysis of social microblogs," Multimedia Tools and Applications 9 (4): 1-16.
Mikolov, T., K. Chen, G. Corrado, and J. Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
Mosteller, J., & A. Poddar. 2017. To share and protect: Using regulatory focus theory to examine the privacy paradox of consumers' social media engagement and online privacy protection behaviors. Journal of Interactive Marketing 39: 27-38.
Pennington, J., R. Socher, and C. Manning. 2014. Glove: Global vectors for word representation. in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). New York.
Pichierri, M., G. Pino, A. M. Peluso, & G. Guido. 2020. The interplay between health claim type and individual regulatory focus in determining consumers’ intentions toward extra-virgin olive oil. Food Research International 136: 109-137.
Prouse, C. 2015. Harnessing the hijab: the emergence of the Muslim Female Footballer through international sport governance. Gender, Place & Culture 22 (1): 20-36.
Rajabi, Z., M. R. Valavi, and M. Hourali. 2020. "A context-based disambiguation model for sentiment concepts using a bag-of-concepts approach. Cognitive computation 12: 1299-1312.
Rajabi, Z., and M. Valavi. 2021. "A survey on sentiment analysis in Persian: a comprehensive system perspective covering challenges and advances in resources and methods. Cognitive Computation 13: 882-902.
Ran, L., W. Wang, M. Ai, Y. Kong, J. Chen, & L. Kuang. 2020. Psychological resilience, depression, anxiety, and somatization symptoms in response to COVID-19: A study of the general population in China at the peak of its epidemic. Social Science & Medicine 262: 11-61.
Riehm, K. E., S. G. Brenneke, L.B. Adams, D. Gilan, K. Lieb, A. M. Kunzler, ... & J. Thrul. 2021. Association between psychological resilience and changes in mental distress during the COVID-19 pandemic. Journal of Affective Disorders 282: 381-385.
Safavi, S., A. Shamsai, & B. Saghafian. 2018. Reduced-order salinity modeling of the Urmia Lake using MIKE3 and proper orthogonal decomposition models. Water Resources 45 (5): 728-737.
Scoresby, R. B., M. C Withers, & R. D. Ireland. 2021. The effect of CEO regulatory focus on changes to investments in R&D. Journal of Product Innovation Management 38 (4): 401-420.
Sevincer, A. T., & G. Oettingen. 2021. Regulatory focus and thinking about the future versus reality. Motivation Science 7 (3): 334-341.
International AAAI Conference on Web and Social Media, 2024, pp. 2051 Shang, L., B. Chen, A. Vora, Y. Zhang, X. Cai, and D. Wang. 2024. "SocialDrought: a social and news media driven dataset and analytical platform towards understanding societal impact of drought," in Proceedings of the -2062. California.
Shojaei, M., M. Bijani, & E. Abbasi. 2020. Farmers' resilience behaviour in the face of water scarcity in the eastern part of Lake Urmia, Iran: an environmental psychological analysis. Water and Environment Journal 34 (4): 611-622.
Sotos-Prieto, M., R. Ortolá, E. López-García, F. Rodríguez-Artalejo, & E. García-Esquinas. 2021. Adherence to the Mediterranean diet and physical resilience in older adults: The Seniors-ENRICA Cohort. The Journals of Gerontology: Series A 76 (3): 505-512.
Super, C. M., & S. Harkness. 2020. Culture and the perceived organization of newborn behavior: A comparative study in Kenya and the United States. New Directions for Child and Adolescent Development 172: 11-24.
Sweya, L. N., S. Wilkinson, & G. Kassenga. 2021. A social resilience measurement tool for Tanzania’s water supply systems. International Journal of Disaster Risk Reduction 65: 102558.
Taylor, M. G., & D. Carr. 2021. Psychological resilience and health among older adults: a comparison of personal resources. The Journals of Gerontology: Series B 76 (6): 1241-1250.
Tennberg, M., T. Vuojala-Magga, & J. Vola. 2020. Social resilience in practice: insights from Finnish Lapland. Acta Borealia 37 (1-2): 94-109.
Wang, L., M. Jin, & Z. Yang. 2020. Regulatory focus and consumption of counterfeit luxury goods: Roles of functional theories of attitudes and perceived similarity. Journal of Business Research 107: 50-61.
Wine, M. L. 2020. Climatization of environmental degradation: A widespread challenge to the integrity of earth science. Hydrological Sciences Journal 65 (6): 867-883.
Winter, C., P. Neumann, A. Meleagrou-Hitchens, M. Ranstorp, L. Vidino, & J. Fürst. 2020. Online extremism: research trends in internet activism, radicalization, and counter-strategies. International Journal of Conflict and Violence (IJCV) 14 (2): 1-20.
Wu, L., & N. A. Dodoo. 2020. Being accepted or ostracized: how social experience influences consumer responses to advertisements with different regulatory focus. Journal of Advertising 49 (3): 234-249.
Yudaputra, A. N. G. G. A. 2020. Modelling potential current distribution and future dispersal of an invasive species Calliandra calothyrsus in Bali Island, Indonesia. Biodiversitas 21 (2): 674-682.
Zhang, L., S. Wang, and B. Liu. 2018. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8 (4): e1253.
Iranian Journal of Information Processing and Management
Volume 40, Issue 4 - Serial Number 124
Summer 2025
Pages 1307-1339

  • Receive Date 11 October 2024
  • Revise Date 02 July 2025
  • Accept Date 02 July 2025