## 2 Literature Review  
One of the major sources of big data used in different kinds of analysis is LBSN. It is a valuable research field that is considered the center of various research domains like geography analysis, human behavior, activities, preferences, etc. This kind of research initially used manual data collection methods such as surveys, interviews, questionnaires, and other statistical methods [20; 21; 22]. However, with the passage of time, the manual collection of data is not deemed appropriate due to the requirement of big data in the true sense for finding significant patterns within the data. The data collection method evolvedinto the use of global positioning systems coordinates, location-based online services, and smart cards with the developments in mobile technology [23; 24]. As communication devices became more and more portable, data collection about user activities through these devices became easier and more accessible to researchers. One of the earlier research studies by [PERSON] et al. [25] used data from about 100,000 users. It was the early-stage introduction of portable devices, and the technology to pinpoint the exact location of users was not mature enough. However, it provided a reasonable approximation of the users with the nearest base tower while making a call. Numerous researchers have discussed the importance of using location-based data for user behavior and activity patterns, including the articles [10; 26; 27; 28; 29; 30; 31]. The user activity analysis then shifted to the use of online social networks as a source of big data because portable mobile devices became more readily available almost everywhere in the world [32]. The facility of posting activities and preferences with locations provided by the online services not only interests users to share their life with friends, but also works as a tool for generating huge amounts of data, which can be used to find patterns in the general user behavior by exploring the similarities and differences in these activities as discussed in the articles [33; 34; 35]. Many research articles are published comprising the study of users' behavior, including user mobility [36], geo-social recommendations [37], recommendation systems based on the study of two different cities in the United Kingdom [11], etc. One of the major research fields in the study of LBSN is the exploration of patterns in the data with respect to venues [9]. An enormous dataset was used by [PERSON] et al. [12] containing about 2.4-million sites in 14 different countries from Foursquare to find the popularity features of different venues. The authors concluded three core features - a venue's profile, age, and nature of the activities--as the most influential factors in the popularity of venues. Similarly, [PERSON] et al. [38] conducted a study about \"Food\" venues and the general behavior of users in the capital of Saudi Arabia, Riyadh. They suggested that people share their experiences more frequently from food venues as compared to other venues. A study based on Foursquare containing nearly 19,000 users from New York, San Francisco, and Hong Kong was conducted by [PERSON] et al. [39] for the purpose of finding about preferences among different types of venues during different times of the day. Most of the literature mentioned above is based on data from mobile phones or some of the most famous LBSNs used almost all over the world, such as Twitter, Foursquare, etc. The study of patterns within the LBSN data is based mostly on these internationally recognized platforms, which represent the most common user behavior and trends [40]. However, these renowned applications are not commonly used in China. One of the most frequently used LBSNs in China is called Sina Weibo (or Weibo), which is utilized by the majority of people and, therefore, famous amongst researchers for LBSN data analysis.