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2018 | OriginalPaper | Chapter

Leveraging Local Interactions for Geolocating Social Media Users

Authors : Mohammad Ebrahimi, Elaheh ShafieiBavani, Raymond Wong, Fang Chen

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

Predicting the geolocation of social media users is one of the core tasks in many applications, such as rapid disaster response, targeted advertisement, and recommending local events. In this paper, we introduce a new approach for user geolocation that unifies users’ social relationships, textual content, and metadata. Our two key contributions are as follows: (1) We leverage semantic similarity between users’ posts to predict their geographic proximity. To achieve this, we train and utilize a powerful word embedding model over millions of tweets. (2) To deal with isolated users in the social graph, we utilize a stacking-based learning approach to predict users’ locations based on their tweets’ textual content and metadata. Evaluation on three standard Twitter benchmark datasets shows that our approach outperforms state-of-the-art user geolocation methods.

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previous chapter Predictive Team Formation Analysis via Feature Representation Learning on Social Networks

next chapter Utilizing Sequences of Touch Gestures for User Verification on Mobile Devices

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Title: Leveraging Local Interactions for Geolocating Social Media Users
Authors: Mohammad Ebrahimi
Elaheh ShafieiBavani
Raymond Wong
Fang Chen
Publisher: Springer International Publishing
Book: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-93039-8

Electronic ISBN: 978-3-319-93040-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-93040-4_63

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