The geographic location of social media users is crucial for understanding user behavior, optimizing advertising, and supporting location-based services such as emergency awareness and event monitoring services. However, existing Twitter user geolocation methods primarily focus on explicit social relationships between users while overlooking implicit location correlations, which affects the accuracy of user geolocation. To address this, this article proposes a Twitter user geolocation method (GeoICMF) based on implicit location correlations and multi-scale feature fusion. GeoICMF introduces a novel location association graph construction method to effectively capture implicit location correlations among users, an innovative multi-scale feature fusion model to dynamically fuse multi-scale features and generate richer user representations, and a pioneering geographic partitioning method to better adapt to user location distributions and enhance geolocation accuracy. Extensive experiments on three real-world datasets demonstrate that GeoICMF outperforms state-of-the-art baseline methods in Twitter user geolocation tasks, validating the effectiveness and superiority of the proposed method.

The geolocation of social users is crucial for understanding user behavior, optimizing advertisement placement, and enhancing public safety.However, existing methods tend to show some deficiencies when handling sparse datasets and may not fully capture the natural clustering characteristics of user locations, thereby resulting in inadequate geolocation accuracy.This paper proposes a novel social user geolocation method (MILGCN) that innovatively integrates Mean Shift Clustering, Incremental Learning, and Graph Convolutional Networks.Specifically, Mean Shift performs fine-grained clustering of user locations based on density peak characteristics, ensuring that geographically close users are grouped into the same cluster.Introducing an incremental learning mechanism into graph convolutional networks enables MILGCN to have progressive learning ability.As a result, the problem of incomplete feature extraction from sparse data is alleviated, resulting in more comprehensive user features and improved geolocation accuracy.Extensive experiments proved that the proposed method significantly outperforms the state-of-the-art baselines on the real Twitter datasets, demonstrating a substantial improvement in geolocation performance.

Cross-network anchor link discovery is an important research problem and has many applications in heterogeneous social network. Existing schemes of cross-network anchor link discovery can provide reasonable link discovery results, but the quality of these results depends on the features of the platform. Therefore, there is no theoretical guarantee to the stability. This article employs user embedding feature to model the relationship between cross-platform accounts, that is, the more similar the user embedding features are, the more similar the two accounts are. The similarity of user embedding features is determined by the distance of the user features in the latent space. Based on the user embedding features, this article proposes an embedding representation-based method Con&Net(Content and Network) to solve cross-network anchor link discovery problem. Con&Net combines the user’s profile features, user-generated content (UGC) features, and user’s social structure features to measure the similarity of two user accounts. Con&Net first trains the user’s profile features to get profile embedding. Then it trains the network structure of the nodes to get structure embedding. It connects the two features through vector concatenating, and calculates the cosine similarity of the vector based on the embedding vector. This cosine similarity is used to measure the similarity of the user accounts. Finally, Con&Net predicts the link based on similarity for account pairs across the two networks. A large number of experiments in Sina Weibo and Twitter networks show that the proposed method Con&Net is better than state-of-the-art method. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve predicted by the anchor link is 11% higher than the baseline method, and Precision@30 is 25% higher than the baseline method.