[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123467-en":3,"doc-seo-123467-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123467,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Detection and Classification of Twitter Users' Opinions on Drought Crises in Iran Using Machine Learning Techniques","The research aims to identify and classify Persian-speaking Twitter users’ opinions about drought crises in Iran and to build a platform-based detection model. Machine learning and text mining techniques are used to classify drought-related tweets collected over one year, totaling 42,028 posts. A qualitative sample of 2,300 tweets is labeled and categorized, yielding four opinion categories about drought impacts and Iranian resilience. A logistic regression model trained on these categories achieves 66.09% accuracy and a 60% F-score, supporting policymakers with measurable shifts in public sentiment.","Detection and Classification of Twitter Users' Opinions on Drought Crises in Iran Using Machine Learning Techniques  \nSomayeh Labafi, Assistant Professor, Iranian Research Institute for Information Science and Technology  \n(IranDoc), Tehran, Iran, [labafi@irandoc.ac.ir](labafi@irandoc.ac.ir)  \nLeila Rabiei, Iranian Telecommunication Research Center (ITRC), Tehran, Iran, [l.rabiei@itrc.ac.ir](l.rabiei@itrc.ac.ir)  \n[Zeinab Rajabi](Zeinab Rajabi), Assistant Professor, Faculty of Computer Department, Hazrat-e Masoumeh University,  \nQom, Iran, [z.rajabi@hmu.ac.ir](z.rajabi@hmu.ac.ir)  \nAbstract:  \nThe main objective of this research is to identify and classify the opinions of Persian-speaking Twitter users related to drought crises in Iran and subsequently develop a model for detecting these opinions on the platform. To achieve this, a model has been developed using machine learning and text mining methods to detect the opinions of Persian-speaking Twitter users regarding the drought issues in Iran. The statistical population for the research included 42,028 drought-related tweets posted over a one-year period. These tweets were extracted from Twitter using keywords related to the drought crises in Iran. Subsequently, a sample of 2,300 tweets was qualitatively analyzed, labeled, categorized, and examined. Next, a four-category classification of users` opinions regarding drought crises and Iranians' resilience to these crises was identified. Based on these four categories, a machine learning model based on logistic regression was trained to predict and detect various opinions in Twitter posts. The developed model exhibits an accuracy of 66.09% and an Fscore of 60%, indicating that this model has good performance for detecting Iranian Twitter users'opinions regarding drought crises. The ability to detect opinions regarding drought crises on platforms like Twitter using machine learning methods can intelligently represent the resilience level of the Iranian society in the face of these crises, and inform policymakers in this area about changes in public opinion.  \nKeywords: Resilience, Drought, Twitter, Opinion Detection, Machine Learning, Logistic Regression Method  \n1 .Introduction and statement of the problem  \nThe emergence of social media platforms has transformed the structure of human societies. The revolution of social media platforms signifies a transition from the social construction of temporal and spatial reality to the social construction of atemporal and non-spatial reality (Gorska, 2020) .(Gurska, 2020) . Social media platforms provide the possibility of divergence in societies by networking. Social media users are more likely to be part of a network of social symbols that are in line with their personal beliefs. In social media platforms, networking based on social symbols makes the members of the network more biased. The risk of convergence in the polarization and the fear of social fragmentation has arisen (Sta, 2020) . The history of social media platforms in creating social fragmentation in relation to social issues has been studied (Dillon, Neo, & Freelich, 2020) . However, some social issues are more important and require further attention. In each period, specific social issues are challenged by the public, and governments prioritize these issues. By examining the current situation in Iran, it can be seen that a social challenge in the context of social resilience against drought has emerged. While there were limited activities around the issue of drought in Iranian society before the rise of social media platforms, the presence of these in response to this issue. Analyzing the theme of resilience in the face of drought provides an analysis of the resilience of Iranians in the face of drought and the behavior of social media platform users in relation to this issue. While no effort has been made so far to understand the issue of resilience in the face of drought on the basis of social media platforms and to use","cbCaiiAxQMwvwXUT","https://ap.wps.com/l/cbCaiiAxQMwvwXUT","pdf",411050,1,19,"English","en",105,"# 1. Introduction and statement of the problem\n## Social media impact and polarization risks\n## Iranian drought resilience and regulatory focus motivation\n# 2. Theoretical Concepts of Research\n## 2.1 Resilience","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To detect and classify Persian-speaking Twitter users’ opinions about drought crises in Iran and to develop a machine learning model for this purpose.\"},{\"question\":\"How was the dataset collected and prepared?\",\"answer\":\"Drought-related tweets were extracted using drought-related keywords over one year, producing 42,028 tweets; then 2,300 tweets were qualitatively analyzed, labeled, and categorized.\"},{\"question\":\"What classification approach and performance did the model achieve?\",\"answer\":\"A logistic regression-based machine learning model predicts opinions into four categories, achieving 66.09% accuracy and a 60% F-score.\"}]","Detection and Classification of Twitter Users' Opinions on Drought Crises in Iran Using Machine Learning Techniques | PDF",1785816684,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detection-and-classification-of-twitter-users-opinions-on-drought-crises-in-iran-using-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detection-and-classification-of-twitter-users-opinions-on-drought-crises-in-iran-using-machine-learning-techniques/123467/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To detect and classify Persian-speaking Twitter users’ opinions about drought crises in Iran and to develop a machine learning model for this purpose.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset collected and prepared?",{"text":80,"@type":76},"Drought-related tweets were extracted using drought-related keywords over one year, producing 42,028 tweets; then 2,300 tweets were qualitatively analyzed, labeled, and categorized.",{"name":82,"@type":73,"acceptedAnswer":83},"What classification approach and performance did the model achieve?",{"text":84,"@type":76},"A logistic regression-based machine learning model predicts opinions into four categories, achieving 66.09% accuracy and a 60% F-score.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]