Xinjiang Lu

Predicting the content dissemination trends by repost behavior modeling in mobile social networks

Abstract Internet of Things (IoT) invasions a future in which digital and physical entities (e.g., mobile devices, wearable devices) can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this paper, we study the dissemination of content over a mobile social network, which has become an attractive proxy for investigating human behaviors due to the rapid development of mobile phones. One of the most interesting and challenging problems about content dissemination is that how much attention of a specific post from a user can ultimately gain? Hence, in other words, can we forecast the crowds׳ concern in the mobile social networking environment, and how? We try to tackle this issue by exploring approaches to predict the amount of reposts any given post will obtain in Sina Weibo, a well-known mobile social networking service in China. We examine several novel implicit factors impacting the popularity of content, such as Modality, MaxMediaWeight and Activeness. Furthermore, we propose a RepostsTree based method to model the reposting process in a temporal dynamic manner. Experimental results over the collected data from Sina Weibo indicate that our method is effective on content diffusion prediction in mobile social networks.

Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps

The rapid adoption of mobile messaging Apps has enabled us to collect massive amount of encrypted Internet traffic of mobile messaging. The classification of this traffic into different types of in-App service usages can help for intelligent network management, such as managing network bandwidth budget and providing quality of services. Traditional approaches for classification of Internet traffic rely on packet inspection, such as parsing HTTP headers. However, messaging Apps are increasingly using secure protocols, such as HTTPS and SSL, to transmit data. This imposes significant challenges on the performances of service usage classification by packet inspection. To this end, in this paper, we investigate how to exploit encrypted Internet traffic for classifying in-App usages. Specifically, we develop a system, named CUMMA, for classifying service usages of mobile messaging Apps by jointly modeling user behavioral patterns, network traffic characteristics, and temporal dependencies. Along this line, we first segment Internet traffic from traffic-flows into sessions with a number of dialogs in a hierarchical way. Also, we extract the discriminative features of traffic data from two perspectives: (i) packet length and (ii) time delay. Next, we learn a service usage predictor to classify these segmented dialogs into single-type usages or outliers. In addition, we design a clustering Hidden Markov Model (HMM) based method to detect mixed dialogs from outliers and decompose mixed dialogs into sub-dialogs of single-type usage. Indeed, CUMMA enables mobile analysts to identify service usages and analyze end-user in-App behaviors even for encrypted Internet traffic. Finally, the extensive experiments on real-world messaging data demonstrate the effectiveness and efficiency of the proposed method for service usage classification.

Discovering Information Propagation Patterns in Microblogging Services

During the last decade, microblog has become an important social networking service with billions of users all over the world, acting as a novel and efficient platform for the creation and dissemination of real-time information. Modeling and revealing the information propagation patterns in microblogging services cannot only lead to more accurate understanding of user behaviors and provide insights into the underlying sociology, but also enable useful applications such as trending prediction, recommendation and filtering, spam detection and viral marketing. In this article, we aim to reveal the information propagation patterns in Sina Weibo, the biggest microblogging service in China. First, the cascade of each message is represented as a tree based on its retweeting process. Afterwards, we divide the information propagation pattern into two levels, that is, the macro level and the micro level. On one hand, the macro propagation patterns refer to general propagation modes that are extracted by grouping propagation trees based on hierarchical clustering. On the other hand, the micro propagation patterns are frequent information flow patterns that are discovered using tree-based mining techniques. Experimental results show that several interesting patterns are extracted, such as popular message propagation, artificial propagation, and typical information flows between different types of users.

Tree-Based Mining for Discovering Patterns of Reposting Behavior in Microblog

Discovering behavior patterns is important in online human interaction understanding (e.g., how information is shared through reposting, what roles do people play in a conversation). As reposting has become the key mechanism for information propagation in social media (e.g. microblog) and contributes a lot to users’ participation in online events, it is important to explore how repost works. Different from previous studies, we make two contributions in this work: firstly, we analyze the patterns of reposting behavior from the perspective of microblog user and employ a special mining method which successfully find interesting results; secondly, our analysis is based on the Sina Weibo, which has different characteristics with Twitter. Specifically, information flow for a certain message in Weibo is represented as a tree. Tree-based pattern mining algorithm is presented to extract a number of interesting patterns which are useful for understanding information diffusion in the Weibo network.

Characterizing the life cycle of point of interests using human mobility patterns

A Point of Interest (POI) refers to a specific location that people may find useful or interesting. While a large body of research has been focused on identifying and recommending POIs, there are few studies on characterizing the life cycle of POIs. Indeed, a comprehensive understanding of POI life cycle can be helpful for various tasks, such as urban planning, business site selection, and real estate evaluation. In this paper, we develop a framework, named POLIP, for characterizing the POI life cycle with multiple data sources. Specifically, to investigate the POI evolution process over time, we first formulate a serial classification problem to predict the life status of POIs. The prediction approach is designed to integrate two important perspectives: 1) the spatial-temporal dependencies associated with the prosperity of POIs, and 2) the human mobility dynamics hidden in the citywide taxicab data related to the POIs at multiple granularity levels. In addition, based on the predicted life statuses in successive time windows for a given POI, we design an algorithm to characterize its life cycle. Finally, we performed extensive experiments using large-scale and real-world datasets. The results demonstrate the feasibility in automatic characterizing POI life cycle and shed important light on future research directions.

Efficient karaoke song recommendation via multiple kernel learning approximation

Abstract Online karaoke allows users to practice singing and distribute recordings. Different from traditional music recommendation, online karaoke need to consider users’ vocal competence besides their tastes. In this paper, we develop a karaoke recommender system by taking into account vocal competence. Alone this line, we propose a joint modeling method named MKLA by adopting bregman divergence as the regularizer in the formulation of multiple kernel learning. Specially, we first extract users’ vocal ratings from their singing recordings. Due to an ever-increasing number of recordings, the evaluations in large-scale kernel matrix may cost lots of time and internal storage. Therefore, we propose a sample compression method to eliminate users’ vocal ratings, exploit an MKL method, and learn the latent features of the vocal ratings. These latent features are simultaneously fed into a bregman divergence and then we use the trained classifier to predict the overall rating of a user with respect to a song. Enhanced by this new formulation, we develop the SMO method for optimizing the MKLA dual and present a theoretical analysis to show the lower bound of our method. With the estimated model, we compute the matching degree of users and songs in terms of pitch, volume and rhythm and recommend songs to users. Finally, we conduct extensive experiments with online karaoke data. The results demonstrate the effectiveness of our method.

Understanding Human Dynamics of Check-in Behavior in LBSNs

With the increase of popularity and pervasive use of sensor-embedded smart phones, location-based social network services (LBSNs) are widely used in recent years. In this paper, we investigate human dynamics of the check-in data crawled from Jie Pang, a famous Chinese LBSN service. We study interval time and jump size (i.e. distance) between consecutive check-ins at both population level and individual level. We find out that both the interval time and jump size follow a Weibull distribution rather than a power law distribution at the population level. As for individual level for the top 10, 000 most active users, we find out that on one hand 9406 individuals follow a power law distribution and only 594 individuals follow a Weibull distribution in interval time distribution. On the other hand, 5096 individuals follow a Weibull distribution and 4904 individuals follow a power law distribution in jump size distribution. In addition, human check-in behavior from different gender and different cities are analyzed. Our experimental results show that users in Shanghai are more active than the users from other cities and females are more active than males in terms of check-in service.

Modeling and Predicting the Re-post Behavior in Sina Weibo

Study of human behavior patterns is of utmost importance to many areas, such as disease spread, resource allocation, and emergency response. Because of its widespread availability and use, online social networks (OSNs) have become an attractive proxy for studying human behaviors. One of the interesting and challenging problems about OSNs is that how much attention of a post from a user can gain? In this paper, we try to tackle this issue by exploring approaches to predict the amount of reposts any given post will obtain in Sina Weibo, a famous microblogging service in China. Specifically, we propose a Reposts Tree based method to model the reposting process in a temporal dynamic manner. Experiments over the real world collected data indicate that our method is effective on repost predicting.

Point-of-Interest Demand Modeling with Human Mobility Patterns

Point-of-Interest (POI) demand modeling in urban regions is critical for many applications such as business site selection and real estate investment. While some efforts have been made for the demand analysis of some specific POI categories, such as restaurants, it lacks systematic means to support POI demand modeling. To this end, in this paper, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data. Specifically, we first partition the urban space into spatially differentiated neighborhood regions formed by many small local communities. Then, the daily activity patterns of people traveling in the city will be extracted from human mobility data. Since the trip activities, even aggregated, are sparse and insufficient to directly identify the POI demands, especially for underdeveloped regions, we develop a latent factor model that integrates human mobility data, POI profiles, and demographic data to robustly model the POI demand of urban regions in a holistic way. In this model, POI preferences and supplies are used together with demographic features to estimate the POI demands simultaneously for all the urban regions interconnected in the city. Moreover, we also design efficient algorithms to optimize the latent model for large-scale data. Finally, experimental results on real-world data in New York City (NYC) show that our method is effective for identifying POI demands for different regions.

Trending Words Based Event Detection in Sina Weibo

Online social networks provide us an unprecedented volume of available data, which results from the pervasive adoption of online social applications. In particular, for the unique characteristics on promoting content sharing, microblogging social networks offer us a new proxy for detecting and tracking the events being taken place in the real world. In spite of large amount of social babble involved, the microblog data contains fresh news coming from human sensors at a humungous rate. As the online social network is a platform that is able to process fast changing streaming data, however it is hard to discover meaningful events in such noisy circumstances in time. In this paper, we study the keywords determining problem in event detection and propose a novel and much more effective method for discovering bursty words in microblogging social networks by leveraging temporal dynamics information. Based on this, we propose an efficient event detection framework applied in Sina Weibo---a Chinese microblogging site similar to Twitter. With experiments conducted on real data sourced from Sina Weibo, we show the effectiveness and feasibility of our proposed method and framework.