# Find You: Multi-View-Based Location Inference for Twitter Users
## 2 Related Work  
Existing methods for inferring the location of Twitter users can be divided into three categories: text-based, network-based, and hybrid methods, which combine text- and network-based approaches. Text-based inference methods rely on the geographic bias exhibited by users in their language usage on social media. Users in different regions have different habits when using words [15]. Location-related words, such as regional dialects, building names, and special food names, often appear in tweets from users in the same location. [PERSON] et al. [16] analyzed the relationship between potential topics and geographical regions and proposed a latent variable model to infer user location. [PERSON] et al. [4] proposed an unsupervised method for autonomously identifying local words from users' tweets and inferring the user's city-level location. [PERSON] et al. [8] used the Term Frequency-Inverse Document Frequency (TF-IDF) to extract statistical features of the text and inferred the user location using a multilayer perceptron. [PERSON] et al. [17] proposed a method to embed two-dimensional locations in a continuous vector space and used logistic regression to localize the user. Some studies extracted local words based on statistical features of words [7; 18] and inferred the user's location from the extracted local words. The text-based approach ignores the influence of users' friendships and can achieve limited precision. Network-based inference methods rely on the social relationships between users to infer their locations. These methods consider the proximity between a given user and his/her friends, inferring the user's location based on his/her friends' locations [19; 20]. Earlier studies [21; 22] considered users who follow each other as friends and geolocated users through social relationships between them. [PERSON] et al. [9] used a simple maximum-voting strategy for location inference. [PERSON] et al. [23] constructed the mentioned network using connectivity between user nodes and extended the label propagation algorithm [24] by propagating location labels from known users to unknown users. [PERSON] et al. [25] identified and filtered celebrity users and inferred user locations by considering celebrity effects. [PERSON] et al. [26] proposed a hybrid method that treated one-way mentions as undirected edges, extended the tag propagation algorithm to connected users, and employed a text-based approach as a fallback strategy for users not connected to the social network. However, the network-based approach is incapable of inferring the locations of non-network-connected users. Hybrid approaches combine user-generated text and social relationships to infer user locations, compensating for the limitations of individual text-based or network-based methods [11]. [PERSON] et al. [27] utilized a tag propagation algorithm based on modified adsorption [28], filtered out celebrity influences in the social network, and incorporated textual information for location inference. [PERSON] et al. [12] constructed a user relationship view based on user interactions, extracted user text features using Bag-of-Words representations, and employed graph convolutional networks for location inference. [PERSON] et al. [29] used tweet content and social network relationships as input features to construct a hybrid method that maps the original spatial distribution of input features to the prediction space. [PERSON] et al. [30] built a multi-view geolocation model that uses not only text and network features but also timestamp data. Graph neural networks have a wide range of applications within this field [31]. For example, [PERSON] et al. [11] combined node2 vec-learned user network features with doc2 vec-learned text representations to infer the user's location. [PERSON] et al. [14] proposed a location inference model based on Graph Attention Mechanisms and Graph Convolutional Networks (GCNs) to fuse text and network information.