Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure which is effective at removing outlying errors. Leave-many-out evaluation shows that our method is able to infer location for 101, 846, 236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80 of public tweets.
Geographic locations of users form an important axis in public polls and localized advertising, but are not available by default. The number of users who make their locations public or use GPS tagging is relatively small, compared to the huge number of users in online social networking services and social media platforms. In this work we propose a new framework to infer a user's main location of activities in Twitter using their textual contents. Our approach is based on a probabilistic generative model that filters local words, employs data binning for scalability, and applies a map projection technique for performance. For Korean Twitter users, we report that 60 of users are identified within 10 km of their locations, a significant improvement over existing approaches. Location plays an essential role in our lives, bridging our online and offline worlds. This paper explores the interplay between people's location, interactions, and their social ties within a large real-world dataset. We present and evaluate Flap, a system that solves two intimately related tasks: link and location prediction in online social networks. For link prediction, Flap infers social ties by considering patterns in friendship formation, the content of people's messages, and user location. We show that while each component is a weak predictor of friendship alone, combining them results in a strong model, accurately identifying the majority of friendships. For location prediction, Flap implements a scalable probabilistic model of human mobility, where we treat users with known GPS positions as noisy sensors of the location of their friends. We explore supervised and unsupervised learning scenarios, and focus on the efficiency of both learning and inference. We evaluate Flap on a large sample of highly active users from two distinct geographical areas and show that it (1) reconstructs the entire friendship graph with high accuracy even when no edges are given; and (2) infers people's fine-grained location, even when they keep their data private and we can only access the location of their friends. Our models significantly outperform current comparable approaches to either task. Location profiles of user accounts in social media can be utilized for various applications, such as disaster warnings and location-aware recommendations. In this paper, we propose a scheme to infer users' home locations in social media. A large portion of existing studies assume that connected users (i.e., friends) in social graphs are located in close proximity. Although this assumption holds for some fraction of connected pairs, sometimes connected pairs live far from each other. To address this issue, we introduce a novel concept of landmarks, which are defined as users with a lot of friends who live in a small region. Landmarks have desirable features to infer users' home locations such as providing strong clues and allowing the locations of numerous users to be inferred using a small number of landmarks. Based on this concept, we propose a landmark mixture model (LMM) to infer users' location. The experimental results using a large-scale Twitter dataset show that our method improves the accuracy of the state-of-the-art method by about 27 .