Understanding human dynamics from large-scale location-centric social media data : analysis and applications Dingqi Yang To cite this version: Dingqi Yang. Understanding human dynamics from large-scale location-centric social media data : analysis and applications. Other [cs.OH]. Institut National des T´el´ecommunications, 2015. English. <NNT : 2015TELE0002>. <tel-01115101v3> HAL Id: tel-01115101 https://tel.archives-ouvertes.fr/tel-01115101v3 Submitted on 24 Nov 2015 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. 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THESE DE DOCTORAT DE TELECOM SUDPARIS et L’UNIVERSITE PIERRE ET MARIE CURIE Spécialité : Informatique Ecole doctorale : Informatique, Télécommunications et Electronique de Paris Présentée par Dingqi YANG Pour obtenir le grade de DOCTEUR DE TELECOM SUDPARIS Exploration de la Dynamique Humaine Basée sur les Données Massives des Réseaux Sociaux de Géolocalisation : Analyse et Applications Soutenue le 27 Janvier 2015 Devant le jury composé de : Eric Gaussier Rapporteur Professeur, Université Joseph Fourier - Grenoble, France Daniel Gatica-Perez Rapporteur Professeur, EPFL - Lausanne, Suisse Pierre Sens Examinateur Professeur, UPMC – Paris, France Marie-Aude Aufaure Examinateur Professeur, Ecole Centrale Paris - Paris, France Cécile Bothorel Examinateur Maître de conférence, Institut Mines-Télécom – Brest, France Djamal Zeghlache Directeur de thèse Professeur, Institut Mines-Télécom – Evry, France Daqing Zhang Co-encadrant Directeur d’études, Institut Mines-Télécom – Evry, France Thèse No : 2015TELE0002 Doctor of Philosophy (PhD) Thesis Universit´ePierre & Marie Curie -TELECOM SudParis Specialization INFORMATIQUE Presented by Dingqi YANG Submitted for the partial requirement of Doctor of Philosophy from Universit´ePierre & Marie Curie (UPMC) - TELECOM SudParis Understanding Human Dynamics from Large-Scale Location-Centric Social Media Data: Analysis and Applications January 27, 2015 Committee: Eric´ Gaussier Reviewer Professor, Universit´eJoseph Fourier - Grenoble, France Daniel Gatica-Perez Reviewer Professor, EPFL - Lausanne, Switzerland Pierre Sens Examiner Professor, UPMC - Paris, France Marie-Aude Aufaure Examiner Professor, Ecole Centrale Paris - Paris, France C´ecileBothorel Examiner Associate Professor, Institut Mines-T´el´ecom- Brest, France Djamal Zeghlache Thesis Director Professor, Institut Mines-T´el´ecom/T´el´ecomSudParis - Evry, France Daqing Zhang Advisor Professor, Institut Mines-T´el´ecom/T´el´ecomSudParis - Evry, France Declaration This thesis: | is the result of my own research work and contains nothing which is the outcome of work done in collaboration with others, except where specifically indicated in the text; | has not previously been submitted for a degree or diploma, or other qualification at any other university. Dingqi Yang January 2015 Abstract Human dynamics is an essential aspect of human-centric computing which is a transdis- ciplinary research field combining human factors and computer science. Studying human dynamics focuses on understanding the underlying patterns, relationships, and changes of human behavior. By analyzing human dynamics, we can understand not only individual's behavior, such as a presence at a specific place, but also collective behavior, such as crowd mobility and social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services in smart city scenarios. However, before the availability of the ubiquitous smart devices (e.g., sensor-embedded smartphones), it is practically difficult to collect large-scale human behavior data. With the ubiquity of GPS-equipped smartphones, location-centric social media, i.e., location based social networks (LBSNs), has gained increasing popularity in recent years, which makes large-scale user activity data become attainable. In LBSNs, users can share their real time activities with their friends by checking in at points of interests (POIs), such as a restaurant or a bar. Such location-centric social media data massively implies human dynamics. For example, from individual perspective, we can explore spatial temporal regularity of user activities; from collective perspective, we can investigate the collective behavior patterns and study their difference across societies. In this dissertation, we explore human dynamics based on big location-centric social media data, and investigate into the whole life-circle of the research process, including data collection, analysis and applications. Concretely, in order to collect large-scale user activity data, we first build a platform to collect user activity data from various LBSNs, such as Foursquare and Twitter. Based on this location-centric social media data, we then study human dynamics and their applications from both individual and collective perspectives. From individual perspective, based on city-scale user activity data, we explore user preference on POIs and the spatial-temporal regularity of user activities. Specifically, In order to study user preference with different granularity and its applications in ser- vice personalization, we define and extract two types of user preference, viz., coarse- grained user preference (i.e., user-POI preference) and fine-grained user preference (i.e., user-POI-item preference), from heterogeneous user activity data in LBSNs (e.g., check-ins and user's comments). To incorporate these two types of user pref- erence into personalized location based services, we propose a preference-aware POI recommendation and search framework by designing two novel algorithms based on low-rank approximation techniques for efficient user preference prediction. In order to study the spatial-temporal regularity of user activities and its applications in activity preference inference tasks, we propose a novel spatial temporal activity model, which can efficiently capture spatial and temporal patterns of user activity from the sparse check-in data. For spatial patterns, we propose the notion of per- sonal functional region and related parameters to model and infer user spatial activity preference. For temporal patterns, we exploit the temporal activity similarity among iv different users and apply non-negative tensor factorization to collaboratively infer temporal activity preference. Finally, we put forward a context-aware fusion frame- work to combine the spatial and temporal models for accurate activity preference inference. From collective perspective, based on global-scale user activity data, we study the collective activity pattern with both country and city granularity, and its correlation with global cultures. In order to study the nation-wide collective behavior, we develop NationTelescope, a platform that monitors, compares, and visualize large-scale collective behavior in LBSNs. It is designed to let user efficiently explore the behavioral differences across countries. To achieve this goal, we leverage a slide-window based approach to de- tect the discriminative activities according to the related traffic patterns in different countries, and implement an interactive map interface for data visualization. In order to study the correlation between collective behavior and human cultures on a global scale, we investigate into the city-wide collective behavior, and propose a par- ticipatory cultural mapping approach to automatically discover the cultural clusters of cities and generate a cultural map. Specifically, since only local users are eligible for representing local cultures, we propose a progressive \home" location identification method to filter out ineligible users. By extracting three key cultural features from daily activity, mobility and linguistic perspectives respectively, we propose a cultural clustering method based on spectral clustering techniques to discover the cultural clusters of cities. Finally, we summarize our findings with regard to individual and community dynamics and discuss potential future research trends, such as privacy issues of such location-centric social media data, combination of human activity data and various ubiquitous sensor data, the big data challenges of processing such large-scale human activity data and more inno- vative applications in smart city scenarios. Keywords Human dynamics, Social media analysis, Location based social networks, Location based services, Participatory sensing, Recommendation system, Personalized search, Sen- timent analysis, Spatial, Temporal, Collective behavior, Cultural analysis. Resum´ e´ La dynamique humaine est un sujet essentiel de l'informatique centr´eesur l'homme, domaine de recherche transdisciplinaire combinant les facteurs humains et l'informatique. L'´etudede la dynamique humaine se concentre sur la compr´ehensiondes r´egularit´essous- jacentes, des relations, et des changements dans les comportements humains. En analysant la dynamique humaine, nous pouvons comprendre non seulement des comportements indi- viduels, tels que la pr´esenced'une personne `aun endroit pr´ecis,mais aussi des comporte- ments collectifs, comme la mobilit´ede la foule et les