[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118292-en":3,"doc-seo-118292-105":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},118292,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data","Current research develops machine-learning approaches for classifying location-based social network data and predicting user activities based on the characteristics of different venue types. Analysis of user behavior from LBSN data often depends on venue categories, traditionally built through manual, time-intensive processes. This study proposes models to automatically extract and classify venue categories using a Weibo dataset. Four techniques are evaluated—generalized linear model, logistic regression, deep learning, and gradient-boosted trees—using ROC/AUC, accuracy, recall, precision, F-score, and sensitivity. Deep learning achieves the best results with up to 99% accuracy.","applied sciences  \nArticle  \nPrediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data  \nNaimat Ullah Khan 1,2,3, *, Wanggen Wan 1,2, Rabia Riaz 4, Shuitao Jiang 1,2 and Xuzhi Wang 1,2  \nCitation: Khan, N.U.; Wan, W.; Riaz, R.; Jiang, S.; Wang, X. Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data. Appl. Sci. 2023, 13, 3517. [https://](https://)[ ](https://)[doi.org/10.3390/app13063517](doi.org/10.3390/app13063517)  \nAcademic Editors: Paolo Renna and Jos² Salvador S¡nchez Garreta  \nReceived: 18 January 2023  \nRevised: 3 March 2023  \nAccepted: 7 March 2023  \nPublished: 9 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China  \n2 Institute of Smart City, Shanghai University, Shanghai 200444, China  \n3 School of Computer Science, University of Technology Sydney, Ultimo, NSW 2007, Australia  \n4 Department of CS & IT, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan  \n* [Correspondence: naimat@shu.edu.cn](Correspondence: naimat@shu.edu.cn)  \nAbstract: The current research has aimed to investigate and develop machine-learning approaches by using the data in the dataset to be applied to classify location-based social network data and predict user activities based on the nature of various locations (such as entertainment) . The analysis of user activities and behavior from location-based social network data is often based on venue types, which require the input of data into various categories. This has previously been done through a tedious and time-consuming manual method. Therefore, we proposed a novel approach of using machinelearning models to extract these venue categories. In this study, we used a Weibo dataset as the main source of research and analyzed machine-learning methods for more efficient implementation. We proposed four models based on well-known machine-learning techniques, including the generalized linear model, logistic regression, deep learning, and gradient-boosted trees. We designed, tested, and evaluated these models. We then used various assessment metrics, such as the Receiver Operating Characteristic or Area Under the Curve, Accuracy, Recall, Precision, F-score, and Sensitivity, to show how well these methods performed. We discovered that the proposed machine-learning models are capable of accurately classifying the data, with deep learning outperforming the other models with 99% accuracy, followed by gradient-boosted tree with 98% and 93%, generalized linear model with 90% and 85%, and logistic regression with 86% and 91%, for multiclass distributions and single class predictions, respectively. We classified the data using our machine-learning models into the 10 classes we used in our previous study and predicted tourist destinations among the data to demonstrate the effectiveness of using machine learning for location-based social network data analysis, which is vital for the development of smart city environments in the current technological era.  \nKeywords: machine learning; generalized linear model; logistic regression; deep learning; gradient boosted trees; Weibo; location-based social network; tourism; smart city  \n1. Introduction  \nThe research on Location-Based Social Network (LBSN) data has gained huge attention from scholars with the rapid growth of mobile technologies. The LBSN data have been used for analysis in various specialized fields, such as the study of people's behavior in festivals, shopping malls, food venues, tourism, and many more. These ","cbCaiacSGHVuuG3Z","https://ap.wps.com/l/cbCaiacSGHVuuG3Z","pdf",3189913,1,17,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the study address in location-based social network analysis?\",\"answer\":\"It tackles the need to classify and filter large LBSN datasets by venue type, which has previously required tedious manual work.\"},{\"question\":\"Which machine-learning models are used for prediction and classification?\",\"answer\":\"The study evaluates four models: generalized linear model, logistic regression, deep learning, and gradient-boosted trees.\"},{\"question\":\"How is model performance evaluated in the research?\",\"answer\":\"Performance is assessed using metrics such as ROC/AUC, accuracy, recall, precision, F-score, and sensitivity.\"}]","Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data | PDF",1785682837,43,{"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},"prediction-and-classification-of-user-activities-using-machine-learning-models-from-location-based-social-network-data","",{"@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/prediction-and-classification-of-user-activities-using-machine-learning-models-from-location-based-social-network-data/118292/",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","2026-08-02",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 problem does the study address in location-based social network analysis?","Question",{"text":75,"@type":76},"It tackles the need to classify and filter large LBSN datasets by venue type, which has previously required tedious manual work.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning models are used for prediction and classification?",{"text":80,"@type":76},"The study evaluates four models: generalized linear model, logistic regression, deep learning, and gradient-boosted trees.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated in the research?",{"text":84,"@type":76},"Performance is assessed using metrics such as ROC/AUC, accuracy, recall, precision, F-score, and sensitivity.","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]