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Articles 2221 - 2250 of 3441
Full-Text Articles in Databases and Information Systems
Wenzher: Comprehensive Vertical Search For Healthcare Domain, Liqiang Nie, Tao Li, Mohammad Akbari, Jialie Shen, Tat-Seng Chua
Wenzher: Comprehensive Vertical Search For Healthcare Domain, Liqiang Nie, Tao Li, Mohammad Akbari, Jialie Shen, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Online health seeking has transformed the way of health knowledge exchange and reusability. The existing general and vertical health search engines, however, just routinely return lists of matched documents or question answer (QA) pairs, which may overwhelm the seekers or not sufficiently meet the seekers’ expectations. Instead, our multilingual system is able to return one multi-faceted answer that is well-structured and precisely extracted from multiple heterogeneous healthcare sources. Further, should the seekers not be satisfied with the returned search results, our system can automatically route the unsolved questions to the professionals with relevant expertise
Mini-Track Introduction: Information Economics, Competition, Regulation, Law And Society, Eric K. Clemons, Robert John Kauffman, Thomas A. Weber
Mini-Track Introduction: Information Economics, Competition, Regulation, Law And Society, Eric K. Clemons, Robert John Kauffman, Thomas A. Weber
Research Collection School Of Computing and Information Systems
This mini-track is informed by the most modern thinking in information economics and competitive strategy, and includes many interdisciplinary applications of IS and technology.
Learning To Recommend Descriptive Tags For Questions In Social Forums, Liqiang Nie, Yiliang Zhao, Xiangyu Wang, Jialie Shen, Tat-Seng Chua
Learning To Recommend Descriptive Tags For Questions In Social Forums, Liqiang Nie, Yiliang Zhao, Xiangyu Wang, Jialie Shen, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Around 40% of the questions in the emerging social-oriented question answering forums have at most one manually labeled tag, which is caused by incomprehensive question understanding or informal tagging behaviors. The incompleteness of question tags severely hinders all the tag-based manipulations, such as feeds for topic-followers, ontological knowledge organization, and other basic statistics. This article presents a novel scheme that is able to comprehensively learn descriptive tags for each question. Extensive evaluations on a representative real-world dataset demonstrate that our scheme yields significant gains for question annotation, and more importantly, the whole process of our approach is unsupervised and can …
Inferring The Untold: Mining Software Engineering Research Publication Networks, Santonu Sarkar, Subhajit Datta
Inferring The Untold: Mining Software Engineering Research Publication Networks, Santonu Sarkar, Subhajit Datta
Research Collection School Of Computing and Information Systems
Since the inception of organized research publication in software engineering in 1975, the discipline has gained maturity. This journey has been guided by the synergy of ideas and interactions of individuals. In this paper, we discuss a method for aggregating the corpus of 19,000+ papers and 21,000+ authors across 16 specialized software engineering venues. We focus on the approach of data collection, processing and storage. It can be used to address questions by the software engineering research community. We evaluate three questions: patterns of research topics with time, factors influencing the contribution of individual researchers, and the interaction among the …
Loki: A Privacy-Conscious Platform For Crowdsourced Surveys, Thivya Kandappu, Vijay Sivaraman, Arik Friedman, Roksana Boreli
Loki: A Privacy-Conscious Platform For Crowdsourced Surveys, Thivya Kandappu, Vijay Sivaraman, Arik Friedman, Roksana Boreli
Research Collection School Of Computing and Information Systems
Emerging platforms such as Amazon Mechanical Turk and Google Consumer Surveys are increasingly being used by researchers and market analysts to crowdsource large-scale survey data from on-line populations at extremely low-cost. However, by participating in successive surveys, users risk being profiled and targeted, both by surveyors and by the platform itself. In this paper we propose, develop, and evaluate the design of a crowdsourcing platform, called Loki, that is privacy conscious. Our contributions are three-fold: (a) We propose Loki, a system that allows users to obfuscate their (ratings-based or multiple-choice) responses at-source based on their chosen privacy level, and gives …
Online Portfolio Selection: A Survey, Bin Li, Steven C. H. Hoi
Online Portfolio Selection: A Survey, Bin Li, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation …
Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu
Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu
Research Collection School Of Computing and Information Systems
This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques. We formulate the learning problem as a convex optimization and develop effective optimization algorithms to solve …
Detecting Click Fraud In Online Advertising: A Data Mining Approach, Richard Oentaryo, Ee Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng-Yeow Cheu, Ghim-Eng Yap, Kelvin Sim, Kasun Perera, Bijay Neupane, Mustafa Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar
Detecting Click Fraud In Online Advertising: A Data Mining Approach, Richard Oentaryo, Ee Peng Lim, Michael Finegold, David Lo, Feida Zhu, Clifton Phua, Eng-Yeow Cheu, Ghim-Eng Yap, Kelvin Sim, Kasun Perera, Bijay Neupane, Mustafa Faisal, Zeyar Aung, Wei Lee Woon, Wei Chen, Dhaval Patel, Daniel Berrar
Research Collection School Of Computing and Information Systems
Click fraud - the deliberate clicking on advertisements with no real interest on the product or service offered - is one of the most daunting problems in online advertising. Building an elective fraud detection method is thus pivotal for online advertising businesses. We organized a Fraud Detection in Mobile Advertising (FDMA) 2012 Competition, opening the opportunity for participants to work on real-world fraud data from BuzzCity Pte. Ltd., a global mobile advertising company based in Singapore. In particular, the task is to identify fraudulent publishers who generate illegitimate clicks, and distinguish them from normal publishers. The competition was held from …
A Simple Integration Of Social Relationship And Text Data For Identifying Potential Customers In Microblogging, Guansong Pang, Shengyi Jiang, Dongyi Chen
A Simple Integration Of Social Relationship And Text Data For Identifying Potential Customers In Microblogging, Guansong Pang, Shengyi Jiang, Dongyi Chen
Research Collection School Of Computing and Information Systems
Identifying potential customers among a huge number of users in microblogging is a fundamental problem for microblog marketing. One challenge in potential customer detection in microblogging is how to generate an accurate characteristic description for users, i.e., user profile generation. Intuitively, the preference of a user’s friends (i.e., the person followed by the user in microblogging) is of great importance to capture the characteristic of the user. Also, a user’s self-defined tags are often concise and accurate carriers for the user’s interests. In this paper, for identifying potential customers in microblogging, we propose a method to generate user profiles via …
Dynamic Joint Sentiment-Topic Mode, Yulan He, Chenghua Lin, Wei Gao, Kam-Fai Wong
Dynamic Joint Sentiment-Topic Mode, Yulan He, Chenghua Lin, Wei Gao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Social media data are produced continuously by a large and uncontrolled number of users. The dynamic nature of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. We propose a dynamic Joint Sentiment-Topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic-specific word distributions are generated according to the word distributions at previous epochs. We study three different …
Partial Least Squares Regression On Grassmannian Manifold For Emotion Recognition, M. Liu, R. Wang, Zhiwu Huang, S. Shan, X. Chen
Partial Least Squares Regression On Grassmannian Manifold For Emotion Recognition, M. Liu, R. Wang, Zhiwu Huang, S. Shan, X. Chen
Research Collection School Of Computing and Information Systems
In this paper, we propose a method for video-based human emotion recognition. For each video clip, all frames are represented as an image set, which can be modeled as a linear subspace to be embedded in Grassmannian manifold. After feature extraction, Class-specific One-to-Rest Partial Least Squares (PLS) is learned on video and audio data respectively to distinguish each class from the other confusing ones. Finally, an optimal fusion of classifiers learned from both modalities (video and audio) is conducted at decision level. Our method is evaluated on the Emotion Recognition In The Wild Challenge (EmotiW 2013). The experimental results on …
Adaptive Computer‐Generated Forces For Simulator‐Based Training, Expert Systems With Applications, Teck-Hou Teng, Ah-Hwee Tan, Loo-Nin Teow
Adaptive Computer‐Generated Forces For Simulator‐Based Training, Expert Systems With Applications, Teck-Hou Teng, Ah-Hwee Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Simulator-based training is in constant pursuit of increasing level of realism. The transition from doctrine-driven computer-generated forces (CGF) to adaptive CGF represents one such effort. The use of doctrine-driven CGF is fraught with challenges such as modeling of complex expert knowledge and adapting to the trainees’ progress in real time. Therefore, this paper reports on how the use of adaptive CGF can overcome these challenges. Using a self-organizing neural network to implement the adaptive CGF, air combat maneuvering strategies are learned incrementally and generalized in real time. The state space and action space are extracted from the same hierarchical doctrine …
Modeling Temporal Adoptions Using Dynamic Matrix Factorization, Freddy Chong-Tat Chua, Richard Jayadi Oentaryo, Ee Peng Lim
Modeling Temporal Adoptions Using Dynamic Matrix Factorization, Freddy Chong-Tat Chua, Richard Jayadi Oentaryo, Ee Peng Lim
Research Collection School Of Computing and Information Systems
The problem of recommending items to users is relevant to many applications and the problem has often been solved using methods developed from Collaborative Filtering (CF). Collaborative Filtering model-based methods such as Matrix Factorization have been shown to produce good results for static rating-type data, but have not been applied to time-stamped item adoption data. In this paper, we adopted a Dynamic Matrix Factorization (DMF) technique to derive different temporal factorization models that can predict missing adoptions at different time steps in the users' adoption history. This DMF technique is an extension of the Non-negative Matrix Factorization (NMF) based on …
Two Formulas For Success In Social Media: Social Learning And Network Effects, Liangfei Qiu, Qian Tang, Andrew B. Whinston
Two Formulas For Success In Social Media: Social Learning And Network Effects, Liangfei Qiu, Qian Tang, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
This paper examines social learning and network effects that are particularly important for online videos, considering the limited marketing campaigns of user-generated content. Rather than combining both social learning and network effects under the umbrella of social contagion or peer influence, we develop a theoretical model and empirically identify social learning and network effects separately. Using a unique data set from YouTube, we find that both mechanisms have statistically and economically significant effects on video views, and which mechanism dominates depends on the specific video type.
Topicsketch: Real-Time Bursty Topic Detection From Twitter, Wei Xie, Feida Zhu, Jing Jiang, Ee Peng Lim, Ke Wang
Topicsketch: Real-Time Bursty Topic Detection From Twitter, Wei Xie, Feida Zhu, Jing Jiang, Ee Peng Lim, Ke Wang
Research Collection School Of Computing and Information Systems
Twitter has become one of the largest platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in Twitter is one that triggers a surge of relevant tweets within a short time, which often reflects important events of mass interest. How to leverage Twitter for early detection of bursty topics has therefore become an important research problem with immense practical value. Despite the wealth of research work on topic modeling and analysis in Twitter, it remains a huge challenge to detect bursty topics in real-time. As existing methods can hardly scale …
Query-Document-Dependent Fusion: A Case Study Of Multimodal Music Retrieval, Zhonghua Li, Bingjun Zhang, Yi Yu, Jialie Shen, Ye Wang
Query-Document-Dependent Fusion: A Case Study Of Multimodal Music Retrieval, Zhonghua Li, Bingjun Zhang, Yi Yu, Jialie Shen, Ye Wang
Research Collection School Of Computing and Information Systems
In recent years, multimodal fusion has emerged as a promising technology for effective multimedia retrieval. Developing the optimal fusion strategy for different modality (e.g. content, metadata) has been the subject of intensive research. Given a query, existing methods derive a unified fusion strategy for all documents with the underlying assumption that the relative significance of a modality remains the same across all documents. However, this assumption is often invalid. We thus propose a general multimodal fusion framework, query-document-dependent fusion (QDDF), which derives the optimal fusion strategy for each query-document pair via intelligent content analysis of both queries and documents. By …
Modeling Preferences With Availability Constraints, Bingtian Dai, Hady W. Lauw
Modeling Preferences With Availability Constraints, Bingtian Dai, Hady W. Lauw
Research Collection School Of Computing and Information Systems
User preferences are commonly learned from historical data whereby users express preferences for items, e.g., through consumption of products or services. Most work assumes that a user is not constrained in their selection of items. This assumption does not take into account the availability constraint, whereby users could only access some items, but not others. For example, in subscription-based systems, we can observe only those historical preferences on subscribed (available) items. However, the objective is to predict preferences on unsubscribed (unavailable) items, which do not appear in the historical observations due to their (lack of) availability. To model preferences in …
Vireo/Ecnu @ Trecvid 2013: A Video Dance Of Detection, Recounting And Search With Motion Relativity And Concept Learning From Wild, Chong-Wah Ngo, Feng Wang, Wei Zhang, Chun-Chet Tan, Zhanhu Sun, Shi-Ai Zhu, Ting Yao
Vireo/Ecnu @ Trecvid 2013: A Video Dance Of Detection, Recounting And Search With Motion Relativity And Concept Learning From Wild, Chong-Wah Ngo, Feng Wang, Wei Zhang, Chun-Chet Tan, Zhanhu Sun, Shi-Ai Zhu, Ting Yao
Research Collection School Of Computing and Information Systems
The VIREO group participated in four tasks: instance search, multimedia event recounting, multimedia event detection, and semantic indexing. In this paper, we will present our approaches and discuss the evaluation results
Predicting Best Answerers For New Questions: An Approach Leveraging Topic Modeling And Collaborative Voting, Yuan Tian, Pavneet Singh Kochhar, Ee Peng Lim, Feida Zhu, David Lo
Predicting Best Answerers For New Questions: An Approach Leveraging Topic Modeling And Collaborative Voting, Yuan Tian, Pavneet Singh Kochhar, Ee Peng Lim, Feida Zhu, David Lo
Research Collection School Of Computing and Information Systems
Community Question Answering (CQA) sites are becoming increasingly important source of information where users can share knowledge on various topics. Although these platforms bring new opportunities for users to seek help or provide solutions, they also pose many challenges with the ever growing size of the community. The sheer number of questions posted everyday motivates the problem of routing questions to the appropriate users who can answer them. In this paper, we propose an approach to predict the best answerer for a new question on CQA site. Our approach considers both user interest and user expertise relevant to the topics …
Electroweak Measurements In Electron-Positron Collisions At W-Boson-Pair Energies At Lep, S. Schael, Manoj Thulasidas
Electroweak Measurements In Electron-Positron Collisions At W-Boson-Pair Energies At Lep, S. Schael, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
Electroweak measurements performed with data taken at the electron–positron collider LEP at CERN from 1995 to 2000 are reported. The combined data set considered in this report corresponds to a total luminosity of about 3 fb −1 collected by the four LEP experiments ALEPH, DELPHI, L3 and OPAL, at centre-of-mass energies ranging from 130 GeV to 209 GeV. Combining the published results of the four LEP experiments, the measurements include total and differential cross-sections in photon-pair, fermion-pair and four-fermion production, the latter resulting from both double-resonant WW and ZZ production as well as singly resonant production. Total and differential cross-sections …
Covariance Selection By Thresholding The Sample Correlation Matrix, Binyan Jiang
Covariance Selection By Thresholding The Sample Correlation Matrix, Binyan Jiang
Research Collection School Of Computing and Information Systems
This article shows that when the nonzero coefficients of the population correlation matrix are all greater in absolute value than (C1logp/n)1/2 for some constant C1, we can obtain covariance selection consistency by thresholding the sample correlation matrix. Furthermore, the rate (logp/n)1/2 is shown to be optimal.
Upsizer: Synthetically Scaling An Empirical Relational Database, Y. C. Tay, Bing Tian Dai, Daniel T. Wang, Eldora Y. Sun, Yong Lin, Yuting Lin
Upsizer: Synthetically Scaling An Empirical Relational Database, Y. C. Tay, Bing Tian Dai, Daniel T. Wang, Eldora Y. Sun, Yong Lin, Yuting Lin
Research Collection School Of Computing and Information Systems
The TPC benchmarks have helped users evaluate database system performance at different scales. Although each benchmark is domain-specific, it is not equally relevant to different applications in the same domain. The present proliferation of applications also leaves many of them uncovered by the very limited number of current TPC benchmarks. There is therefore a need to develop tools for application-specific database benchmarking. This paper presents UpSizeR, a software that addresses the Dataset Scaling Problem: Given an empirical set of relational tables D and a scale factor s, generate a database state e D that is similar to D but s …
A Link-Bridged Topic Model For Cross-Domain Document Classification, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong
A Link-Bridged Topic Model For Cross-Domain Document Classification, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Transfer learning utilizes labeled data available from some related domain (source domain) for achieving effective knowledge transformation to the target domain. However, most state-of-the-art cross-domain classification methods treat documents as plain text and ignore the hyperlink (or citation) relationship existing among the documents. In this paper, we propose a novel cross-domain document classification approach called Link-Bridged Topic model (LBT). LBT consists of two key steps. Firstly, LBT utilizes an auxiliary link network to discover the direct or indirect co-citation relationship among documents by embedding the background knowledge into a graph kernel. The mined co-citation relationship is leveraged to bridge the …
Second Order Online Collaborative Filtering, Jing Lu, Steven C. H. Hoi, Jialei Wang, Peilin Zhao
Second Order Online Collaborative Filtering, Jing Lu, Steven C. H. Hoi, Jialei Wang, Peilin Zhao
Research Collection School Of Computing and Information Systems
Collaborative Filtering (CF) is one of the most successful learning techniques in building real-world recommender systems. Traditional CF algorithms are often based on batch machine learning methods which suffer from several critical drawbacks, e.g., extremely expensive model retraining cost whenever new samples arrive, unable to capture the latest change of user preferences over time, and high cost and slow reaction to new users or products extension. Such limitations make batch learning based CF methods unsuitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. To address these limitations, we investigate online collaborative …
Efficient Index-Based Approaches For Skyline Queries In Location-Based Applications, Ken C. K. Lee, Baihua Zheng, Cindy Chen, Chi-Yin Chow
Efficient Index-Based Approaches For Skyline Queries In Location-Based Applications, Ken C. K. Lee, Baihua Zheng, Cindy Chen, Chi-Yin Chow
Research Collection School Of Computing and Information Systems
Enriching many location-based applications, various new skyline queries are proposed and formulated based on the notion of locational dominance, which extends conventional one by taking objects' nearness to query positions into account additional to objects' nonspatial attributes. To answer a representative class of skyline queries for location-based applications efficiently, this paper presents two index-based approaches, namely, augmented R-tree and dominance diagram. Augmented R-tree extends R-tree by including aggregated nonspatial attributes in index nodes to enable dominance checks during index traversal. Dominance diagram is a solution-based approach, by which each object is associated with a precomputed nondominance scope wherein query points …
Classification In P2p Networks With Cascade Support Vendor Machines, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee-Keong Ng
Classification In P2p Networks With Cascade Support Vendor Machines, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee-Keong Ng
Research Collection School Of Computing and Information Systems
Classification in Peer-to-Peer (P2P) networks is important to many real applications, such as distributed intrusion detection, distributed recommendation systems, and distributed antispam detection. However, it is very challenging to perform classification in P2P networks due to many practical issues, such as scalability, peer dynamism, and asynchronism. This article investigates the practical techniques of constructing Support Vector Machine (SVM) classifiers in the P2P networks. In particular, we demonstrate how to efficiently cascade SVM in a P2P network with the use of reduced SVM. In addition, we propose to fuse the concept of cascade SVM with bootstrap aggregation to effectively balance the …
Multimedia Modeling, Chong-Wah Ngo, Klaus Schoeffmann, Yiannis Andreopoulos, Christian Breiteneder
Multimedia Modeling, Chong-Wah Ngo, Klaus Schoeffmann, Yiannis Andreopoulos, Christian Breiteneder
Research Collection School Of Computing and Information Systems
Multimedia modeling aims to study computational models for addressing real-world multimedia problems from various perspectives, including information fusion, perceptual understanding, performance evaluation and social media. The topic becomes increasingly important with the massive amount of data available over the Internet, representing different pieces of information in heterogeneous forms that need to be consolidated before being used for multimedia problems. On the other hand, the advancement in technologies such as mobile and sensing devices drive the needs for revisiting the existing models for not only dealing with audio-visual cues but also incorporating various sensory modalities that have potential in providing cheaper …
Why Do I Retweet It? An Information Propagation Model For Microblogs, Fabio Pezzoni, Jisun An, Andrea Passarella, Jon Crowcroft, Marco Conti
Why Do I Retweet It? An Information Propagation Model For Microblogs, Fabio Pezzoni, Jisun An, Andrea Passarella, Jon Crowcroft, Marco Conti
Research Collection School Of Computing and Information Systems
Microblogging platforms are Web 2.0 services that represent a suitable environment for studying how information is propagated in social networks and how users can become influential. In this work we analyse the impact of the network features and of the users' behaviour on the information diffusion. Our analysis highlights a strong relation between the level of visibility of a message in the flow of information seen by a user and the probability that the user further disseminates the message. In addition, we also highlight the existence of other latent factors that impact on the dissemination probability, correlated with the properties …
Mining Fraudulent Patterns In Online Advertising, Richard J. Oentaryo, Ee-Peng Lim
Mining Fraudulent Patterns In Online Advertising, Richard J. Oentaryo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Advances in web technologies have rendered onlineadvertising as an effective means for small and large businesses to target different market segments on the fly. Online advertising is a huge industry. According to Gartner Inc., worldwide online advertising revenue is projected tohit $11.4 billion in 2013, up from $9.6 billion in 2012. Global revenue will also reach $24.5 billion in 2016, with online advertising creating opportunities for app developers, advertising networks, and service providersin various regions. An online advertising ecosystem is typically coordinated by an advertising commissioner, acting as a broker between advertisers and content publishers. An advertiser plans a budget, …
Social Informatics, Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, Gael Dias, Katsumi Tanaka, Andrew J. Flanagin, Bing Tian Dai
Social Informatics, Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, Gael Dias, Katsumi Tanaka, Andrew J. Flanagin, Bing Tian Dai
Research Collection School Of Computing and Information Systems
This book constitutes the proceedings of the 5th International Conference on Social Informatics, SocInfo 2013, held in Kyoto, Japan, in November 2013. The 23 full papers, 15 short papers, and three poster papers included in this volume were carefully reviewed and selected from 103 submissions. The papers present original research work on studying the interplay between socially-centric platforms and social phenomena.