Find You: Multi-View-Based Location Inference for Twitter Users
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Published:2023-10-30
Issue:21
Volume:13
Page:11848
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Wu Huixin1, Li Jiahui1, Li Shuqing1, Li Hanbing2, Ma Jiangtao23ORCID, Qiao Yaqiong14
Affiliation:
1. School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China 2. Songshan Laboratory, Zhengzhou 450000, China 3. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China 4. Henan Key Laboratory of Cyberspace Situation Awareness, Zhengzhou 450001, China
Abstract
Location information on Twitter plays a critical role in emergency detection, event recommendation, and disaster warning. However, only a limited amount of Twitter data are geotagged. Previous research has presented various models for inferring location based on text, social relations, and contextual data, yielding highly promising results. Nonetheless, these existing methods have certain limitations that need to be addressed. Firstly, most of the existing methods overlook the role of local celebrities (well-known users in the local community) as indicators of location within the social network. Secondly, they fail to consider the associations between words in tweets, resulting in insufficiently rich features extracted from the tweets. We propose a multi-view-based location inference model called MVGeo to overcome these limitations. In the network view, our approach employs the Gaussian Mixture Model (GMM) to identify and retain local celebrities, thereby strengthening user location associations. In the tweet view, we construct a heterogeneous graph based on the co-occurrence relationship between words in tweets and the user’s mentioned relationship with the words. This allows us to fully leverage the local correlation between words and the global correlation to extract tweet features more comprehensively. Finally, we employ a modified multi-layer graph convolutional network, called Gate-GCN, to fuse the network and tweet information. This expansion of the feature space enables us to extract sample features from multiple perspectives. To demonstrate the effectiveness of MVGeo, we conduct exhaustive experimental evaluations on a publicly available dataset and compare its performance against several state-of-the-art benchmark models. The results confirm the superior performance of the proposed model.
Funder
National Natural Science Foundation of China Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness Songshan Laboratory Henan Province Science Foundation
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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