# Find You: Multi-View-Based Location Inference for Twitter Users
## 1 Introduction  
Users can share brief updates about their activities on social platforms, acting as social sensors and disseminating information about local events. As a virtual online social platform, Twitter generates millions of tweets daily that contain explicit or implicit information about users' locations, bridging the virtual and physical worlds. These location data are utilized in a variety of applications, including event detection [1] and location-based recommendations [2]. Users rarely reveal their geographic locations on social platforms due to privacy protection restrictions, despite the diverse applications of user location data. For instance, only 5% of Twitter users include their home locations in their profile [3], and only 1% of tweets are geotagged [4]. Therefore, research on location-based inference methods for Twitter users is an urgent matter. Early research centered on extracting location signals from users' media posts, such as location-related vocabulary and linguistic patterns. Worddistribution comparison [5; 6] and location-indicative word extraction [7; 8] are representative text-based techniques. In addition, social networks extracted from user interactions online can disclose the distance between users. To process network structure data, the majority of network-based methods employ label propagation or node embedding [9; 10]. Recent hybrid approaches [11; 12] have emerged that combine text and network features to infer user locations, leveraging the strengths of both approaches and achieving improved performance. Despite the satisfactory results of previous work, there are still some things that need to be improved to address: (1) Highly mentioned users (celebrities) in social networks are categorized as local celebrities (well-known users in the local community) and global celebrities (well-known users worldwide). Since celebrity users in social networks form many connections but rarely carry location information, existing methods remove celebrity users by setting a threshold to prevent them from negatively affecting location inference [13; 14]. However, they do not consider the location-indicative role of local celebrities in the network. (2) A tweet is often regarded as a set of unordered lexical items based on the assumption of term independence and is represented as a vector using natural language processing techniques (e.g., TF-IDF and doc2 vec) to embed tweets into low-dimensional vectors during text processing. This method is straightforward and quick, but it ignores structural information in the text and does not capture the overall semantics of the text content, resulting in the loss of contextual information. To address the above issues, we propose a multi-view-based location inference model for Twitter users. Firstly, to exploit the role of local celebrities as location indicators in social relationship networks, GMM is used in the network view to detect local celebrities and enhance the location correlation among users by keeping them in the network. Next, to better capture the features of user tweets, we construct the corpus as a heterogeneous graph and mine user tweet features by analyzing user-word and word-word correlations. Finally, we combine the previously processed social relationship network and the learned user tweet embeddings to learn the final embedding representation of users through a modified multilayer graph convolutional network Gate-GCN. The main contributions of our work are as follows:  
* We propose a local celebrity discovery algorithm that preserves the location-indicative role of local celebrities in the social relationship network.
* We represent the entire corpus as a heterogeneous graph, leveraging local and global correlations between words to extract user tweet features.
* We introduce MVGeo, a multi-view-based location inference model, which demonstrates superior performance in experimental results on public datasets.