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Research Collection School Of Computing and Information Systems

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Full-Text Articles in Databases and Information Systems

Hierarchical Visualization Of Video Search Results For Topic-Based Browsing, Yu-Gang Jiang, Jiajun Wang, Qiang Wang, Wei Liu, Chong-Wah Ngo Nov 2016

Hierarchical Visualization Of Video Search Results For Topic-Based Browsing, Yu-Gang Jiang, Jiajun Wang, Qiang Wang, Wei Liu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Existing video search engines return a ranked list of videos for each user query, which is not convenient for browsing the results of query topics that have multiple facets, such as the "early life," "personal life," and "presidency" of a query "Barack Obama." Organizing video search results into semantically structured hierarchies with nodes covering different topic facets can significantly improve the browsing efficiency for such queries. In this paper, we introduce a hierarchical visualization approach for video search result browsing, which can help users quickly understand the multiple facets of a query topic in a very well-organized manner. Given a …

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Content Sampling, Household Informedness, And The Consumption Of Digital Information Goods, Ai Phuong Hoang, Robert J. Kauffman Nov 2016

Content Sampling, Household Informedness, And The Consumption Of Digital Information Goods, Ai Phuong Hoang, Robert J. Kauffman

Research Collection School Of Computing and Information Systems

Technology and media are delivering content that is transforming society. Providers must compete for consumer attention to sell their digital information goods effectively. This is challenging, since there is a high level of uncertainty associated with the consumption of such goods. Service providers often use free programming to share product information. We examine the effectiveness of content sampling strategy used for on-demand series dramas, a unique class of entertainment goods. The data were extracted from a large set of household video-on-demand (VoD) viewing records and combined with external data sources. We extended a propensity score matching (PSM) approach to handle …

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Summarization Of Egocentric Videos: A Comprehensive Survey, Ana Garcia Del Molino, Cheston Tan, Joo-Hwee Lim, Ah-Hwee Tan Nov 2016

Summarization Of Egocentric Videos: A Comprehensive Survey, Ana Garcia Del Molino, Cheston Tan, Joo-Hwee Lim, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

The introduction of wearable video cameras (e.g., GoPro) in the consumer market has promoted video life-logging, motivating users to generate large amounts of video data. This increasing flow of first-person video has led to a growing need for automatic video summarization adapted to the characteristics and applications of egocentric video. With this paper, we provide the first comprehensive survey of the techniques used specifically to summarize egocentric videos. We present a framework for first-person view summarization and compare the segmentation methods and selection algorithms used by the related work in the literature. Next, we describe the existing egocentric video datasets …

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A Method Of Integrating Correlation Structures For A Generalized Recursive Route Choice Model, Tien Mai Nov 2016

A Method Of Integrating Correlation Structures For A Generalized Recursive Route Choice Model, Tien Mai

Research Collection School Of Computing and Information Systems

We propose a way to estimate a generalized recursive route choice model. The model generalizes other existing recursive models in the literature, i.e., (Fosgerau et al., 2013b; Mai et al., 2015c), while being more flexible since it allows the choice at each stage to be any member of the network multivariate extreme value (network MEV) model (Daly and Bierlaire, 2006). The estimation of the generalized model requires defining a contraction mapping and performing contraction iterations to solve the Bellman’s equation. Given the fact that the contraction mapping is defined based on the choice probability generating functions (CPGF) (Fosgerau et al., …

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Learning Sentence Embeddings With Auxiliary Tasks For Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang Nov 2016

Learning Sentence Embeddings With Auxiliary Tasks For Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also propose to jointly learn this sentence embedding together with the sentiment classifier itself. Experiment results demonstrate that our proposed joint model outperforms several state-of-the-art methods on five benchmark datasets.

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Cost Sensitive Online Multiple Kernel Classification, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi Nov 2016

Cost Sensitive Online Multiple Kernel Classification, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Learning from data streams has been an important open research problem in the era of big data analytics. This paper investigates supervised machine learning techniques for mining data streams with application to online anomaly detection. Unlike conventional machine learning tasks, machine learning from data streams for online anomaly detection has several challenges: (i) data arriving sequentially and increasing rapidly, (ii) highly class-imbalanced distributions; and (iii) complex anomaly patterns that could evolve dynamically.To tackle these challenges, we propose a novel Cost-Sensitive Online Multiple Kernel Classification (CSOMKC) scheme for comprehensively mining data streams and demonstrate its application to online anomaly detection. Specifically, …

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Spiteful, One-Off, And Kind: Predicting Customer Feedback Behavior On Twitter, Agus Sulistya, Abhishek Sharma, David Lo Nov 2016

Spiteful, One-Off, And Kind: Predicting Customer Feedback Behavior On Twitter, Agus Sulistya, Abhishek Sharma, David Lo

Research Collection School Of Computing and Information Systems

Social media provides a convenient way for customers to express their feedback to companies. Identifying different types of customers based on their feedback behavior can help companies to maintain their customers. In this paper, we use a machine learning approach to predict a customer’s feedback behavior based on her first feedback tweet. First, we identify a few categories of customers based on their feedback frequency and the sentiment of the feedback. We identify three main categories: spiteful, one-off, and kind. Next, we build a model to predict the category of a customer given her first feedback. We use profile and …

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On Profiling Bots In Social Media, Richard J. Oentaryo, Arinto Murdopo, Philips K. Prasetyo, Ee Peng Lim Nov 2016

On Profiling Bots In Social Media, Richard J. Oentaryo, Arinto Murdopo, Philips K. Prasetyo, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The popularity of social media platforms such as Twitter has led to the proliferation of automated bots, creating both opportunities and challenges in information dissemination, user engagements, and quality of services. Past works on profiling bots had been focused largely on malicious bots, with the assumption that these bots should be removed. In this work, however, we find many bots that are benign, and propose a new, broader categorization of bots based on their behaviors. This includes broadcast, consumption, and spam bots. To facilitate comprehensive analyses of bots and how they compare to human accounts, we develop a systematic profiling …

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M(2)-Abks: Attribute-Based Multi-Keyword Search Over Encrypted Personal Health Records In Multi-Owner Setting, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Fushan Wei, Zhiquan Liu, Xu An Wang Nov 2016

M(2)-Abks: Attribute-Based Multi-Keyword Search Over Encrypted Personal Health Records In Multi-Owner Setting, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Fushan Wei, Zhiquan Liu, Xu An Wang

Research Collection School Of Computing and Information Systems

Online personal health record (PHR) is more inclined to shift data storage and search operations to cloud server so as to enjoy the elastic resources and lessen computational burden in cloud storage. As multiple patients' data is always stored in the cloud server simultaneously, it is a challenge to guarantee the confidentiality of PHR data and allow data users to search encrypted data in an efficient and privacy-preserving way. To this end, we design a secure cryptographic primitive called as attribute-based multi-keyword search over encrypted personal health records in multi-owner setting to support both fine-grained access control and multi-keyword search …

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Landmark Reranking For Smart Travel Guide Systems By Combining And Analyzing Diverse Media, Junge Shen, Jialie Shen, Tao Mei, Xinbo Gao Nov 2016

Landmark Reranking For Smart Travel Guide Systems By Combining And Analyzing Diverse Media, Junge Shen, Jialie Shen, Tao Mei, Xinbo Gao

Research Collection School Of Computing and Information Systems

Advanced networking technologies and massive online social media have stimulated a booming growth of travel heterogeneous information in recent years. By employing such information, smart travel guide systems, such as landmark ranking systems, have been proposed to offer diverse online travel services. It is essential for a landmark ranking system to structure, analyze, and search the travel heterogeneous information to produce human-expected results. Therefore, currently the most fundamental yet challenging problems can be concluded: 1) how to fuse heterogeneous tourism information and 2) how to model landmark ranking. In this paper, a novel landmark search system is introduced based on …

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Deep-Based Ingredient Recognition For Cooking Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo Oct 2016

Deep-Based Ingredient Recognition For Cooking Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Retrieving recipes corresponding to given dish pictures facilitates the estimation of nutrition facts, which is crucial to various health relevant applications. The current approaches mostly focus on recognition of food category based on global dish appearance without explicit analysis of ingredient composition. Such approaches are incapable for retrieval of recipes with unknown food categories, a problem referred to as zero-shot retrieval. On the other hand, content-based retrieval without knowledge of food categories is also difficult to attain satisfactory performance due to large visual variations in food appearance and ingredient composition. As the number of ingredients is far less than food …

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Satt: Tailoring Code Metric Thresholds For Different Software Architectures, Maurício Aniche, Christoph Treude, Andy Zaidman, Arie Van Deursen, Marco Aurélio Gerosa Oct 2016

Satt: Tailoring Code Metric Thresholds For Different Software Architectures, Maurício Aniche, Christoph Treude, Andy Zaidman, Arie Van Deursen, Marco Aurélio Gerosa

Research Collection School Of Computing and Information Systems

Code metric analysis is a well-known approach for assessing the quality of a software system. However, current tools and techniques do not take the system architecture (e.g., MVC, Android) into account. This means that all classes are assessed similarly, regardless of their specific responsibilities. In this paper, we propose SATT (Software Architecture Tailored Thresholds), an approach that detects whether an architectural role is considerably different from others in the system in terms of code metrics, and provides a specific threshold for that role. We evaluated our approach on 2 different architectures (MVC and Android) in more than 400 projects. We …

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Plackett-Luce Regression Mixture Model For Heterogeneous Rankings, Maksim Tkachenko, Hady W. Lauw Oct 2016

Plackett-Luce Regression Mixture Model For Heterogeneous Rankings, Maksim Tkachenko, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Learning to rank is an important problem in many scenarios, such as information retrieval, natural language processing, recommender systems, etc. The objective is to learn a function that ranks a number of instances based on their features. In the vast majority of the learning to rank literature, there is an implicit assumption that the population of ranking instances are homogeneous, and thus can be modeled by a single central ranking function. In this work, we are concerned with learning to rank for a heterogeneous population, which may consist of a number of sub-populations, each of which may rank objects dierently. …

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Online Adaptive Passive-Aggressive Methods For Non-Negative Matrix Factorization And Its Applications, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun, Ee-Peng Lim Oct 2016

Online Adaptive Passive-Aggressive Methods For Non-Negative Matrix Factorization And Its Applications, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

This paper aims to investigate efficient and scalable machine learning algorithms for resolving Non-negative Matrix Factorization (NMF), which is important for many real-world applications, particularly for collaborative filtering and recommender systems. Unlike traditional batch learning methods, a recently proposed online learning technique named "NN-PA" tackles NMF by applying the popular Passive-Aggressive (PA) online learning, and found promising results. Despite its simplicity and high efficiency, NN-PA falls short in at least two critical limitations: (i) it only exploits the first-order information and thus may converge slowly especially at the beginning of online learning tasks; (ii) it is sensitive to some key …

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Attractiveness Versus Competition: Towards An Unified Model For User Visitation, Thanh-Nam Doan, Ee-Peng Lim Oct 2016

Attractiveness Versus Competition: Towards An Unified Model For User Visitation, Thanh-Nam Doan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Modeling user check-in behavior provides useful insights about venues as well as the users visiting them. These insights can be used in urban planning and recommender system applications. Unlike previous works that focus on modeling distance effect on user’s choice of check-in venues, this paper studies check-in behaviors affected by two venue-related factors, namely, area attractiveness and neighborhood competitiveness. The former refers to the ability of an area with multiple venues to collectively attract checkins from users, while the latter represents the ability of a venue to compete with its neighbors in the same area for check-ins. We first embark …

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Tracking Virality And Susceptibility In Social Media, Tuan Anh Hoang, Ee-Peng Lim Oct 2016

Tracking Virality And Susceptibility In Social Media, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In social media, the magnitude of information propagation hinges on the virality and susceptibility of users spreading and receiving the information respectively, as well as the virality of information items. These users' and items' behavioral factors evolve dynamically at the same time interacting with one another. Previous works however measure the factors statically and independently in a restricted case: each user has only a single adoption on each item, and/or users' exposure to items are observable. In this work, we investigate the inter-relationship among the factors and users' multiple adoptions on items to propose both new static and temporal models …

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Behavior Analysis In Social Networks: Challenges, Technologies, And Trends, Meng Wang, Ee-Peng Lim, Lei Li, Mehmet Orgun Oct 2016

Behavior Analysis In Social Networks: Challenges, Technologies, And Trends, Meng Wang, Ee-Peng Lim, Lei Li, Mehmet Orgun

Research Collection School Of Computing and Information Systems

The research on social networks has advanced significantly, which can be attributed to the prevalence of the online social websites and instant messaging systems as well as the popularity of mobile apps that support easy access to online social networks. These social networks are usually characterized by the complex network structures and rich contextual information. They now become the key platforms for, among others, content dissemination, professional networking, recommendation, alerting, and political campaigns. As online social network users perform activities on the social networks, they leave data traces of human behavior which allow the latter to be studied at scale. …

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Inferring Links Between Concerns And Methods With Multi-Abstraction Vector Space Model, Yun Zhang, David Lo, Xin Xia, Tien-Duy B. Le, Giuseppe Scanniello, Jianling Sun Oct 2016

Inferring Links Between Concerns And Methods With Multi-Abstraction Vector Space Model, Yun Zhang, David Lo, Xin Xia, Tien-Duy B. Le, Giuseppe Scanniello, Jianling Sun

Research Collection School Of Computing and Information Systems

Concern localization refers to the process of locating code units that match a particular textual description. It takes as input textual documents such as bug reports and feature requests and outputs a list of candidate code units that are relevant to the bug reports or feature requests. Many information retrieval (IR) based concern localization techniques have been proposed in the literature. These techniques typically represent code units and textual descriptions as a bag of tokens at one level of abstraction, e.g., each token is a word, or each token is a topic. In this work, we propose a multi-abstraction concern …

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Arise-Pie: A People Information Integration Engine Over The Web, Vincent W. Zheng, Tao Hoang, Penghe Chen, Yuan Fang, Xiaoyan Yang Oct 2016

Arise-Pie: A People Information Integration Engine Over The Web, Vincent W. Zheng, Tao Hoang, Penghe Chen, Yuan Fang, Xiaoyan Yang

Research Collection School Of Computing and Information Systems

Searching for people information on the Web is a common practice in life. However, it is time consuming to search for such information manually. In this paper, we aim to develop an automatic people information search system, named ARISE-PIE. To build such a system, we tackle two major technical challenges: data harvesting and data integration. For data harvesting, we study how to leverage search engine to help crawl the relevant Web pages for a target entity; then we propose a novel learning to query model that can automatically select a set of "best" queries to maximize collective utility (e.g., precision …

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Get Me To My Gate On Time: Efficiently Solving General-Sum Bayesian Threat Screening Games, Aaron Schlenker, Matthew Brown, Arunesh Sinha, Milind Tambe, Ruta Mehta Sep 2016

Get Me To My Gate On Time: Efficiently Solving General-Sum Bayesian Threat Screening Games, Aaron Schlenker, Matthew Brown, Arunesh Sinha, Milind Tambe, Ruta Mehta

Research Collection School Of Computing and Information Systems

Threat Screening Games (TSGs) are used in domains where there is a set of individuals or objects to screen with a limited amount of screening resources available to screen them. TSGs are broadly applicable to domains like airport passenger screening, stadium screening, cargo container screening, etc. Previous work on TSGs focused only on the Bayesian zero-sum case and provided the MGA algorithm to solve these games. In this paper, we solve Bayesian general-sum TSGs which we prove are NP-hard even when exploiting a compact marginal representation. We also present an algorithm based upon a adversary type hierarchical tree decomposition and …

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Human-Centred Design For Silver Assistants, Zhiwei Zheng, Di Wang, Ailiya Borjigin, Chunyan Miao, Ah-Hwee Tan, Cyril Leung Sep 2016

Human-Centred Design For Silver Assistants, Zhiwei Zheng, Di Wang, Ailiya Borjigin, Chunyan Miao, Ah-Hwee Tan, Cyril Leung

Research Collection School Of Computing and Information Systems

To alleviate the rapidly increasing need of the healthcare workforce to serve the enormous ageing population, leveraging intelligent and autonomous caring agents is one promising way. Working towards the design and development of dedicated personal silver assistants for older adults, we follow the human-centred design approach. Specifically, we identify a number of human factors that affect the user experience of the older adults and develop an agent named Mobile Intelligent Silver Assistant (MISA) by applying these human factors. Integrating multiple reusable services onto one platform, MISA acts as a single point of contact while simultaneously providing easy and convenient access …

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Metaflow: A Scalable Metadata Lookup Service For Distributed File Systems In Data Centers, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Haiyong Xie Sep 2016

Metaflow: A Scalable Metadata Lookup Service For Distributed File Systems In Data Centers, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Haiyong Xie

Research Collection School Of Computing and Information Systems

In large-scale distributed file systems, efficient metadata operations are critical since most file operations have to interact with metadata servers first. In existing distributed hash table (DHT) based metadata management systems, the lookup service could be a performance bottleneck due to its significant CPU overhead. Our investigations showed that the lookup service could reduce system throughput by up to 70%, and increase system latency by a factor of up to 8 compared to ideal scenarios. In this paper, we present MetaFlow, a scalable metadata lookup service utilizing software-defined networking (SDN) techniques to distribute lookup workload over network components. MetaFlow tackles …

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Towards Autonomous Behavior Learning Of Non-Player Characters In Games, Shu Feng, Ah-Hwee Tan Sep 2016

Towards Autonomous Behavior Learning Of Non-Player Characters In Games, Shu Feng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Non-Player-Characters (NPCs), as found in computer games, can be modelled as intelligent systems, which serve to improve the interactivity and playability of the games. Although reinforcement learning (RL) has been a promising approach to creating the behavior models of non-player characters (NPC), an initial stage of exploration and low performance is typically required. On the other hand, imitative learning (IL) is an effective approach to pre-building a NPC’s behavior model by observing the opponent’s actions, but learning by imitation limits the agent’s performance to that of its opponents. In view of their complementary strengths, this paper proposes a computational model …

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High Correlation Of Middle East Respiratory Syndrome Spread With Google Search And Twitter Trends In Korea, Soo-Yong Shin, Dong-Woo Seo, Jisun An, Haewoon Kwak, Sung-Han Kim, Jin Gwack, Min-Woo Jo Sep 2016

High Correlation Of Middle East Respiratory Syndrome Spread With Google Search And Twitter Trends In Korea, Soo-Yong Shin, Dong-Woo Seo, Jisun An, Haewoon Kwak, Sung-Han Kim, Jin Gwack, Min-Woo Jo

Research Collection School Of Computing and Information Systems

The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman …

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Probabilistic Models For Contextual Agreement In Preferences, Loc Do, Hady W. Lauw Sep 2016

Probabilistic Models For Contextual Agreement In Preferences, Loc Do, Hady W. Lauw

Research Collection School Of Computing and Information Systems

The long-tail theory for consumer demand implies the need for more accurate personalization technologies to target items to the users who most desire them. A key tenet of personalization is the capacity to model user preferences. Most of the previous work on recommendation and personalization has focused primarily on individual preferences. While some focus on shared preferences between pairs of users, they assume that the same similarity value applies to all items. Here we investigate the notion of "context," hypothesizing that while two users may agree on their preferences on some items, they may also disagree on other items. To …

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Modeling Sequential Preferences With Dynamic User And Context Factors, Duc Trong Le, Yuan Fang, Hady W. Lauw Sep 2016

Modeling Sequential Preferences With Dynamic User And Context Factors, Duc Trong Le, Yuan Fang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Users express their preferences for items in diverse forms, through their liking for items, as well as through the sequence in which they consume items. The latter, referred to as “sequential preference”, manifests itself in scenarios such as song or video playlists, topics one reads or writes about in social media, etc. The current approach to modeling sequential preferences relies primarily on the sequence information, i.e., which item follows another item. However, there are other important factors, due to either the user or the context, which may dynamically affect the way a sequence unfolds. In this work, we develop generative …

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Representation Learning For Homophilic Preferences, Trong T. Nguyen, Hady W. Lauw Sep 2016

Representation Learning For Homophilic Preferences, Trong T. Nguyen, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Users express their personal preferences through ratings, adoptions, and other consumption behaviors. We seek tolearn latent representations for user preferences from such behavioral data. One representation learning model that has been shown to be effective for large preference datasets is Restricted Boltzmann Machine (RBM). While homophily, or the tendency of friends to share their preferences at some level, is an established notion in sociology, thus far it has not yet been clearly demonstrated on RBM-based preference models. The question lies in how to appropriately incorporate social network into the architecture of RBM-based models for learning representations of preferences. In this …

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Control Flow Integrity Enforcement With Dynamic Code Optimization, Yan Lin, Xiaoxiao Tang, Debin Gao, Jianming Fu Sep 2016

Control Flow Integrity Enforcement With Dynamic Code Optimization, Yan Lin, Xiaoxiao Tang, Debin Gao, Jianming Fu

Research Collection School Of Computing and Information Systems

Control Flow Integrity (CFI) is an attractive security property with which most injected and code reuse attacks can be defeated, including advanced attacking techniques like Return-Oriented Programming (ROP). However, comprehensive enforcement of CFI is expensive due to additional supports needed (e.g., compiler support and presence of relocation or debug information) and performance overhead. Recent research has been trying to strike the balance among reasonable approximation of the CFI properties, minimal additional supports needed, and acceptable performance. We investigate existing dynamic code optimization techniques and find that they provide an architecture on which CFI can be enforced effectively and efficiently. In …

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Soft Confidence-Weighted Learning, Jialei Wang, Peilin Zhao, Hoi, Steven C. H. Sep 2016

Soft Confidence-Weighted Learning, Jialei Wang, Peilin Zhao, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

Online learning plays an important role in many big datamining problems because of its high efficiency and scalability. In theliterature, many online learning algorithms using gradient information havebeen applied to solve online classification problems. Recently, more effectivesecond-order algorithms have been proposed, where the correlation between thefeatures is utilized to improve the learning efficiency. Among them,Confidence-Weighted (CW) learning algorithms are very effective, which assumethat the classification model is drawn from a Gaussian distribution, whichenables the model to be effectively updated with the second-order informationof the data stream. Despite being studied actively, these CW algorithms cannothandle nonseparable datasets and noisy datasets very …

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Detecting Community Pacemakers Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang Sep 2016

Detecting Community Pacemakers Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang

Research Collection School Of Computing and Information Systems

Twitter has become one of largest social networks for users to broad-cast burst topics. Influential users usually have a large number of followers and play an important role in the diffusion of burst topic. There have been many studies on how to detect influential users. However, traditional influential users detection approaches have largely ignored influential users in user community. In this paper, we investigate the problem of detecting community pacemakers. Community pacemakers are defined as the influential users that promote early diffusion in the user community of burst topic. To solve this problem, we present DCPBT, a framework that can …

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