The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing. Twitter user geolocation is a difficult and active research topic with a vast literature. Most of the proposed methods follow either a content-based or a network-based approach. The former exploits user-generated content while the latter utilizes the connection or interaction between Twitter users. In this paper, we introduce a novel method combining the strength of both approaches. Concretely, we propose a multi-entry neural network architecture named MENET leveraging the advances in deep learning and multiview learning. The generalizability of MENET enables the integration of multiple data representations. In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features. Considering the natural distribution of Twitter users across the concerned geographical area, we subdivide the surface of the earth into multi-scale cells and train MENET with the labels of the cells. We show that our method outperforms the state of the art by a large margin on three benchmark datasets.
Recent works have shown a correlation between the likelihood of friendship of two social network users and the geographical distance between them @cite_37 . Using this correlation, the location of users can be estimated using their friends' location. This is the key idea behind the network-based approach. By leveraging the social interactions like bi-directional following Twitter users follow other people to see their latest updates. Bi-directional following means two users follow each other. and bi-directional mentioning Twitter users can mention other people in their tweets by typing @username . Bi-directional mentioning is the two-way interaction which happens when two users have mentioned each other. , one can establish graphs of Twitter users where a label propagation algorithm @cite_59 or its variants @cite_28 @cite_19 are used to identify locations of unlabeled users @cite_8 @cite_2 @cite_11 @cite_29 . The network-based approach has several advantages over the content-based counterpart, including language independence. Also, it does not require training, which is a very resource intensive and time-consuming process on big datasets. However, the inherent weakness of this approach is that it cannot propagate labels (locations) to users that are not connected to the graph. As a result, isolated users remain unlabeled.
@cite_37 @cite_8 @cite_28 @cite_29 @cite_19 @cite_59 @cite_2 @cite_11
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User interaction in social networks, such as Twitter and Facebook, is increasingly becoming a source of useful information on daily events. The online monitoring of short messages posted in such networks often provides insight on the repercussions of events of several different natures, such as (in the recent past) the earthquake and tsunami in Japan, the royal wedding in Britain and the death of Osama bin Laden. Studying the origins and the propagation of messages regarding such topics helps social scientists in their quest for improving the current understanding of human relationships and interactions. However, the actual location associated to a tweet or to a Facebook message can be rather uncertain. Some tweets are posted with an automatically determined location (from an IP address), or with a user-informed location, both in text form, usually the name of a city. We observe that most Twitter users opt not to publish their location, and many do so in a cryptic way, mentioning non-existing places or providing less specific place names (such as “Brazil”). In this article, we focus on the problem of enriching the location of tweets using alternative data, particularly the social relationships between Twitter users. Our strategy involves recursively expanding the network of locatable users using following-follower relationships. Verification is achieved using cross-validation techniques, in which the location of a fraction of the users with known locations is used to determine the location of the others, thus allowing us to compare the actual location to the inferred one and verify the quality of the estimation. With an estimate of the precision of the method, it can then be applied to locationless tweets. Our intention is to infer the location of as many users as possible, in order to increase the number of tweets that can be used in spatial analyses of social phenomena. The article demonstrates the feasibility of our approach using a dataset comprising tweets that mention keywords related to dengue fever, increasing by 45 the number of locatable tweets. With the emerging of location-based social networks, study on the relationship between human mobility and social relationships becomes quantitatively achievable. Understanding it correctly could result in appealing applications, such as targeted advertising and friends recommendation. In this paper, we focus on mining users’ relationship based on their mobility information. More specifically, we propose to use distance between two users to predict whether they are friends. We first demonstrate that distance is a useful metric to separate friends and strangers. By considering location popularity together with distance, the difference between friends and strangers gets even larger. Next, we show that distance can be used to perform an effective link prediction. In addition, we discover that certain periods of the day are more social than others. In the end, we use a machine learning classifier to further improve the prediction performance. Extensive experiments on a Twitter dataset collected by ourselves show that our model outperforms the state-of-the-art solution by 30 . 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 . We propose a novel network-based approach for location estimation in social media that integrates evidence of the social tie strength between users for improved location estimation. Concretely, we propose a location estimator -- FriendlyLocation -- that leverages the relationship between the strength of the tie between a pair of users, and the distance between the pair. Based on an examination of over 100 million geo-encoded tweets and 73 million Twitter user profiles, we identify several factors such as the number of followers and how the users interact that can strongly reveal the distance between a pair of users. We use these factors to train a decision tree to distinguish between pairs of users who are likely to live nearby and pairs of users who are likely to live in different areas. We use the results of this decision tree as the input to a maximum likelihood estimator to predict a user's location. We find that this proposed method significantly improves the results of location estimation relative to a state-of-the-art technique. Our system reduces the average error distance for 80 of Twitter users from 40 miles to 21 miles using only information from the user's friends and friends-of-friends, which has great significance for augmenting traditional social media and enriching location-based services with more refined and accurate location estimates.