###### Abstract  
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 machine-learning 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. machine learning; generalized linear model; logistic regression; deep learning; gradient boosted trees; Weibo; location-based social network; tourism; smart city +
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have more opportunities to utilize the data regarding the majority of the population for various kinds of analysis. These data provide a sample of various aspects of human behavior and traits while interacting with the LBSN during a variety of activities through check-ins from different venues. The study of these behaviors provides valuable insights into the general trends within the population for the planning and development of events, festivals, parks, shopping malls, restaurants and, ultimately, a smart city [8]. The LBSN data have also been used in more specialized studies like finding the popularity factors of restaurants, the role of parks, tourism behavior, and many more, which are proved to be tremendously valuable in these fields. However, for these specialized studies, it is important to consider the data relevant only to these venues within the huge number of records and manually classify the specific data for each individual research. As one of the strengths of using LBSN data for human behavior is the availability of huge amounts of data, it is often difficult and more time-consuming to classify the data for finding relevant records [5]. An important domain of LBSN analysis is based on multiple venue types, such as observing check-in analysis [3; 4; 5; 6; 7; 8; 9] or the analysis of user activities and behavior of a single venue category like the attraction feature of restaurants as well as an analysis of parks, tourist destinations, and many more [10; 11; 12; 13; 14; 15; 16] The fundamental goal of this research is to find and develop machine-learning methods that can be used to classify the LBSN data into the most commonly used venue categories and predict the tourism venues for analyzing the behavior of tourists and residents in Shanghai city while showing the efficiency of the proposed models for LBSN studies.