Author: Zhang, Junwei
Title: Spatial, temporal and semantic analysis of instagram for community discovery in Hong Kong
Advisors: Shi, Wen-zhong (LSGI)
Degree: M.Sc.
Year: 2017
Subject: Online social networks -- China -- Hong Kong
Spatial behavior -- Social aspects -- Data processing
Hong Kong Polytechnic University -- Dissertations
Department: Faculty of Construction and Environment
Department of Land Surveying and Geo-Informatics
Pages: ix, 102 pages : color illustrations
Language: English
Abstract: Abundant research using Participatory Sensing Systems have brought a new perspective for understanding city. The social media as a form of Participatory Sensing Systems, such as Twitter, Facebook, and Foursquare, are greatly used for various domains research in recent years. This study explores the spatial-temporal life pattern, and communities in Hong Kong using an increasingly popular social media, Instagram. The main objective of this research is to discover communities who share similar interests and spatial-temporal behaviors. An Instagram data segmentation scheme is designed for dividing applicable data for latter analysis. The posts with popular hashtag from active users are segmented for the community discovery. A new approach is proposed in this paper to identify clusters of user based on the topic-label, time-label, and geo-label inferred separately. Three topic models are developed through LDA modeling on the user hashtag corpus. Four types of time preferences are identified using k-means clustering on the user-time matrix. Eight types of urban function are classified and quantitatively visualized using digital map POI. The mixture of urban function in Hong Kong are mapped and discussed. Communities are identified and their characteristics are explored, which indicates the strong connections and communications between Hong Kong Island and Kowloon. The high concentrations of industrial estate in these two regions are discussed at the end.
Rights: All rights reserved
Access: restricted access

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/10524