OpenRaiser/Intern-Atlas huggingface dataset:
Tweeting Cameras for Event Detection
Authors: Yuhui Wang, Mohan S. Kankanhalli
Venue: WWW
Year: 2015

We are living in a world of big sensor data. Due to the widespread prevalence of visual sensors (e.g. surveillance cameras) and social sensors (e.g. Twitter feeds), many events are implicitly captured in real-time by such heterogeneous "sensors". Combining these two complementary sensor streams can significantly improve the task of event detection and aid in comprehending evolving situations. However, the different characteristics of these social and sensor data make such information fusion for event detection a challenging problem. To tackle this problem, we propose an innovative multi-layer tweeting camera framework integrating both physical sensors and social sensors to detect various concepts of real-world events. In this framework, visual concept detectors are applied on camera video frames and these concepts can be construed as "camera tweets" posted regularly. These tweets are represented by a unified probabilistic spatio-temporal (PST) data structure which is then aggregated to a concept-based image (Cmage) as the common representation for visualization. To facilitate event analysis, we define a set of operators and analytic functions that can be applied on the PST data by the user to discover occurrences of events and to analyse evolving situations. We further leverage on geo-located social media data by mining current topics discussed on Twitter to obtain the high-level semantic meaning of detected events in images. We quantitatively evaluate our framework with a large-scale dataset containing images from 150 New York real-time traffic CCTV cameras, university foodcourt camera feeds and Twitter data, which demonstrates the feasibility and effectiveness of the proposed framework. Results of combining camera tweets and social tweets are shown to be promising for detecting real-world events.
What Size Should A Mobile Ad Be?
Authors: Pengyuan Wang, Wei Sun, Dawei Yin
Venue: WWW
Year: 2015

We present a causal inference framework for evaluating the impact of advertising treatments. Our framework is computationally efficient by employing a tree structure that specifies the relationship between user characteristics and the corresponding ad treatment. We illustrate the applicability of our proposal on a novel advertising effectiveness study: finding the best ad size on different mobile devices in order to maximize the success rates. The study shows a surprising phenomenon that a larger mobile device does not need a larger ad. In particular, the 300*250 ad size is universally good for all the mobile devices, regardless of the mobile device size.
Kvasir: Seamless Integration of Latent Semantic Analysis-Based Content Provision into Web Browsing
Authors: Liang Wang, Sotiris K. Tasoulis, Teemu Roos, Jussi Kangasharju
Venue: WWW
Year: 2015

The Internet is overloading its users with excessive information flows, so that effective content-based filtering becomes crucial in improving user experience and work efficiency. We build Kvasir, a semantic recommendation system, atop latent semantic analysis and other state-of-art technologies to seamlessly integrate an automated and proactive content provision service into web browsing. We utilize the power of Apache Spark to scale up Kvasir to a practical Internet service. Herein we present the architecture of Kvasir, along with our solutions to the technical challenges in the actual system implementation.
Rethink Targeting: Detect 'Smart Cheating' in Online Advertising through Causal Inference
Authors: Pengyuan Wang, Dawei Yin, Jian Yang, Yi Chang, Marsha Meytlis
Venue: WWW
Year: 2015

In online advertising, one of the central questions of ad campaign assessment is whether the ad truly adds values to the advertisers. To measure the incremental effect of ads, the ratio of the success rates of the users who were and who were not exposed to ads is usually calculated to represent ad effectiveness. Many existing campaigns simply target the users with high predicted success (e.g. purchases, searches) rate, which often neglect the fact that even without ad exposure, the targeted group of users might still perform the success actions, and hence show higher ratio than the true ad effectiveness. We call such phenomena `smart cheating'. Failure to discount smart cheating when assessing ad campaigns may favor the targeting plan that cheats hard, but such targeting does not lead to the maximal incremental success actions and results in wasted budget. In this paper we define and quantify smart cheating with a smart cheating ratio (SCR) through causal inference. We apply our approach to multiple real ad campaigns, and find that smart cheating exists extensively and can be rather severe in current advertising industry.
Research Collaboration and Topic Trends in Computer Science: An Analysis Based on UCP Authors
Authors: Yan Wu, Srinivasan Venkatramanan, Dah-Ming Chiu
Venue: WWW
Year: 2015

Academic publication metadata can be used to analyze the collaboration, productivity and hot topic trends of a research community. Recently, it is shown that authors with uninterrupted and continuous presence (UCP) over a time window, though small in number (about 1%), amass the majority of significant and high-influence academic output. We adopt the UCP metric to retrieve the most active authors in the Computer Science (CS) community over different time windows in the past $50$ years, and use them to analyze collaboration, productivity and topic trends. We show that the UCP authors are representative of the overall population; the community is increasingly moving in the direction of Team Research (as opposed to Soloist or Mentor-mentee research), with increased level and degree of collaboration; and the research topics become increasingly inter-related. By focusing on the UCP authors, we can more easily visualize these trends.
SemMobi: A Semantic Annotation System for Mobility Data
Authors: Fei Wu, Hongjian Wang, Zhenhui Li, Wang-Chien Lee, Zhuojie Huang
Venue: WWW
Year: 2015

The wide adaptation of mobile devices embedded with modern positioning technology enables the collection of valuable mobility data from users. At the same time, the large-scale user-generated data from social media, such as geo-tagged tweets, provide rich semantic information about events and locations. The combination of the mobility data and social media data brings opportunities for us to study the semantics behind people's movement, i.e., understand why a person travels to a location at a particular time. Previous work have used map or POI (point of interest) database as source for semantics. However, those semantics are static, and thus missing important dynamic event information. To provide dynamic semantic annotation, we propose to use contextual social media. More specifically, the semantics could be landmark information (e.g., a museum or an arena) or event information (e.g., sports games or concerts). The SemMobi system implements our recently developed annotation method, which has been recently accepted to WWW 2015 conference. The annotation method annotates words to each mobility records based on local density of words, estimated by Kernel Density Estimation model. The annotated mobility data contain rich and interpretable information, therefore can benefit applications, such as personalized recommendation, targeted advertisement, and movement prediction. Our system is built upon large-scale tweet datasets. A user-friendly interface is designed to support interactive exploration of the result.
Executing Provenance-Enabled Queries over Web Data
Authors: Marcin Wylot, Philippe Cudré-Mauroux, Paul Groth
Venue: WWW
Year: 2015

The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, because of this heterogeneity, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data. We propose, implement and empirically evaluate five different query execution strategies for RDF queries that incorporate knowledge of provenance. The evaluation is conducted on Web Data obtained from two different Web crawls (The Billion Triple Challenge, and the Web Data Commons). Our evaluation shows that using an adaptive query materialization execution strategy performs best in our context. Interestingly, we find that because provenance is prevalent within Web Data and is highly selective, it can be used to improve query processing performance. This is a counterintuitive result as provenance is often associated with additional overhead.
Temporal Multi-View Inconsistency Detection for Network Traffic Analysis
Authors: Houping Xiao, Jing Gao, Deepak S. Turaga, Long H. Vu, Alain Biem
Venue: WWW
Year: 2015

In this paper, we investigate the problem of identifying inconsistent hosts in large-scale enterprise networks by mining multiple views of temporal data collected from the networks. The time-varying behavior of hosts is typically consistent across multiple views, and thus hosts that exhibit inconsistent behavior are possible anomalous points to be further investigated. To achieve this goal, we develop an effective approach that extracts common patterns hidden in multiple views and detects inconsistency by measuring the deviation from these common patterns. Specifically, we first apply various anomaly detectors on the raw data and form a three-way tensor (host, time, detector) for each view. We then develop a joint probabilistic tensor factorization method to derive the latent tensor subspace, which captures common time-varying behavior across views. Based on the extracted tensor subspace, an inconsistency score is calculated for each host that measures the deviation from common behavior. We demonstrate the effectiveness of the proposed approach on two enterprise-wide network-based anomaly detection tasks. An enterprise network consists of multiple hosts (servers, desktops, laptops) and each host sends/receives a time-varying number of bytes across network protocols (e.g.,TCP, UDP, ICMP) or send URL requests to DNS under various categories. The inconsistent behavior of a host is often a leading indicator of potential issues (e.g., instability, malicious behavior, or hardware malfunction). We perform experiments on real-world data collected from IBM enterprise networks, and demonstrate that the proposed method can find hosts with inconsistent behavior that are important to cybersecurity applications.
Why Do You Follow Him?: Multilinear Analysis on Twitter
Authors: Yuto Yamaguchi, Mitsuo Yoshida, Christos Faloutsos, Hiroyuki Kitagawa
Venue: WWW
Year: 2015

Why does Smith follow Johnson on Twitter? In most cases, the reason why users follow other users is unavailable. In this work, we answer this question by proposing TagF, which analyzes the who-follows-whom network (matrix) and the who-tags-whom network (tensor) simultaneously. Concretely, our method decomposes a coupled tensor constructed from these matrix and tensor. The experimental results on million-scale Twitter networks show that TagF uncovers different, but explainable reasons why users follow other users.
Tackling the Achilles Heel of Social Networks: Influence Propagation based Language Model Smoothing
Authors: Rui Yan, Ian En-Hsu Yen, Cheng-Te Li, Shiqi Zhao, Xiaohua Hu
Venue: WWW
Year: 2015

Online social networks nowadays enjoy their worldwide prosperity, as they have revolutionized the way for people to discover, to share, and to distribute information. With millions of registered users and the proliferation of user-generated contents, the social networks become "giants", likely eligible to carry on any research tasks. However, the giants do have their Achilles Heel: extreme data sparsity. Compared with the massive data over the whole collection, individual posting documents, (e.g., a microblog less than 140 characters), seem to be too sparse to make a difference under various research scenarios, while actually they are different. In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation. We formulate a socialized factor graph model, which utilizes both the textual correlations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and social interactions. These factors are modeled as attributes and dependencies among documents and their corresponding users. An efficient algorithm is designed to learn the proposed factor graph model. Finally we propagate term counts to smooth documents based on the estimated influence. Experimental results on Twitter and Weibo datasets validate the effectiveness of the proposed model. By leveraging the smoothed language model with social factors, our approach obtains significant improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of perplexity, nDCG and MAP results.
A Game Theoretic Model for the Formation of Navigable Small-World Networks
Authors: Zhi Yang, Wei Chen
Venue: WWW
Year: 2015

Kleinberg proposed a family of small-world networks to explain the navigability of large-scale real-world social networks. However, the underlying mechanism that drives real networks to be navigable is not yet well understood. In this paper, we present a game theoretic model for the formation of navigable small world networks. We model the network formation as a game in which people seek for both high reciprocity and long-distance relationships. We show that the navigable small-world network is a Nash Equilibrium of the game. Moreover, we prove that the navigable small-world equilibrium tolerates collusions of any size and arbitrary deviations of a large random set of nodes, while non-navigable equilibria do not tolerate small group collusions or random perturbations. Our empirical evaluation further demonstrates that the system always converges to the navigable network even when limited or no information about other players' strategies is available. Our theoretical and empirical analyses provide important new insight on the connection between distance, reciprocity and navigability in social networks.
Topic-aware Social Influence Minimization
Authors: Qipeng Yao, Ruisheng Shi, Chuan Zhou, Peng Wang, Li Guo
Venue: WWW
Year: 2015

In this paper, we address the problem of minimizing the negative influence of undesirable things in a network by blocking a limited number of nodes from a topic modeling perspective. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking $k$ nodes outside the infected set. We first employ the HDP-LDA and KL divergence to analysis the influence and relevance from a topic modeling perspective. Then two topic-aware heuristics based on betweenness and out-degree for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally the high performance of the proposed models and learning schemes.
Mining Social and Urban Big Data
Authors: Nicholas Jing Yuan
Venue: WWW
Year: 2015

In recent years, with the rapid development of positioning technologies, online social networks, sensors and smart devices, large scale human behavioral data are now readily available. The growing availability of such behavioral data provides us unprecedented opportunities to gain more in depth understanding of users in both the physical world and cyber world, especially in online social networks. In this talk, I will introduce our recent research efforts in social and urban mining based on large-scale human behavioral datasets showcased by two projects: 1) LifeSpec: Modeling the spectrum of urban lifestyles based on heterogeneous online social network data. 2) L2P: Inferring demographic attributes from location check-ins.
Interconnecting and Enriching Higher Education Programs using Linked Data
Authors: Fouad Zablith
Venue: WWW
Year: 2015

Online environments are increasingly used as platforms to support and enhance learning experiences. In higher education, students enroll in programs that are usually formed of a set of courses and modules. Such courses are designed to cover a set of concepts and achieve specific learning objectives that count towards the related degree. However we observe that connections among courses and the way they conceptually interlink are hard to exploit. This is normal as courses are traditionally described using text in the form of documents such as syllabi and course catalogs. We believe that linked data can be used to create a conceptual layer around higher education programs to interlink courses in a granular and reusable manner. We present in this paper our work on creating a semantic linked data layer to conceptually connect courses taught in a higher education program. We highlight the linked data model we created to be collaboratively extended by course instructors and students using a semantic Mediawiki platform. We also present two applications that we built on top of the data to (1) showcase how learning material can now float around courses through their interlinked concepts in eLearning environments (we use moodle as a proof of concept); and (2) to support the process of higher education program reviews.
Linking Stanford Typed Dependencies to Support Text Analytics
Authors: Fouad Zablith, Ibrahim H. Osman
Venue: WWW
Year: 2015

With the daily increase of the amount of published information, research in the area of text analytics is gaining more visibility. Text processing for improving analytics is being studied from different angles. In the literature, text dependencies have been employed to perform various tasks. This includes for example the identification of semantic relations and sentiment analysis. We observe that while text dependencies can boost text analytics, managing and preserving such dependencies in text documents that spread across various corpora and contexts is a challenging task. We present in this paper our work on linking text dependencies using the Resource Description Framework (RDF) specification, following the Stanford typed dependencies representation. We contribute to the field by providing analysts the means to query, extract, and reuse text dependencies for analytical purposes. We highlight how this additional layer can be used in the context of feedback analysis by applying a selection of queries passed to a triple-store containing the generated text dependencies graphs.
Towards An Interactive Keyword Search over Relational Databases
Authors: Zhong Zeng, Zhifeng Bao, Mong-Li Lee, Tok Wang Ling
Venue: WWW
Year: 2015

Keyword search over relational databases has been widely studied for the exploration of structured data in a user-friendly way. However, users typically have limited domain knowledge or are unable to precisely specify their search intention. Existing methods find the minimal units that contain all the query keywords, and largely ignore the interpretation of possible users' search intentions. As a result, users are often overwhelmed with a lot of irrelevant answers. Moreover, without a visually pleasing way to present the answers, users often have difficulty understanding the answers because of their complex structures. Therefore, we design an interactive yet visually pleasing search paradigm called ExpressQ. ExpressQ extends the keyword query language to include keywords that match meta-data, e.g., names of relations and attributes. These keywords are utilized to infer users' search intention. Each possible search intention is represented as a query pattern, whose meaning is described in human natural language. Through a series of user interactions, ExpressQ can determine the search intention of the user, and translate the corresponding query patterns into SQLs to retrieve answers to the query. The ExpressQ prototype is available at http://expressq.comp.nus.edu.sg.
Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis
Authors: Yongfeng Zhang, Min Zhang, Yi Zhang, Guokun Lai, Yiqun Liu, Honghui Zhang, Shaoping Ma
Venue: WWW
Year: 2015

The frequently changing user preferences and/or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user/item records when splitting the already sparse data across time dimension, previous methods have to restrict the drifting purchasing patterns to pre-assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to alleviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts.
Towards Entity Correctness, Completeness and Emergence for Entity Recognition
Authors: Lei Zhang, Yunpeng Dong, Achim Rettinger
Venue: WWW
Year: 2015

Linking words or phrases in unstructured text to entities in knowledge bases is the problem of entity recognition and disambiguation. In this paper, we focus on the task of entity recognition in Web text to address the challenges of entity correctness, completeness and emergence that existing ap-proaches mainly suffer from. Experimental results show that our approach significantly outperforms the state-of-the-art approaches in terms of precision, F-measure, micro-accuracy and macro-accuracy, while still preserving high recall. 1.
Automatic Detection of Information Leakage Vulnerabilities in Browser Extensions
Authors: Rui Zhao, Chuan Yue, Qing Yi
Venue: WWW
Year: 2015

A large number of extensions exist in browser vendors' online stores for millions of users to download and use. Many of those extensions process sensitive information from user inputs and webpages; however, it remains a big question whether those extensions may accidentally leak such sensitive information out of the browsers without protection. In this paper, we present a framework, LvDetector, that combines static and dynamic program analysis techniques for automatic detection of information leakage vulnerabilities in legitimate browser extensions. Extension developers can use LvDetector to locate and fix the vulnerabilities in their code; browser vendors can use LvDetector to decide whether the corresponding extensions can be hosted in their online stores; advanced users can also use LvDetector to determine if certain extensions are safe to use. The design of LvDetector is not bound to specific browsers or JavaScript engines, and can adopt other program analysis techniques. We implemented LvDetector and evaluated it on 28 popular Firefox and Google Chrome extensions. LvDetector identified 18 previously unknown information leakage vulnerabilities in 13 extensions with a 87% accuracy rate. The evaluation results and the feedback to our responsible disclosure demonstrate that LvDetector is useful and effective.
PTHMM: Beyond Single Specific Behavior Prediction
Authors: Suncong Zheng, Hongyun Bao, Guanhua Tian, Yu-Fang Wu, Bo Xu, Hongwei Hao
Venue: WWW
Year: 2015

Existing works on user behavior analysis mainly focus on modeling a single behavior and predicting whether a user will take an action or not. However, users' behaviors do not always happen in isolation, sometimes, different behaviors may happen simultaneously. Therefore, in this paper, we try to analyze the combination of basic behaviors, called behavioral state here, which can describes users' complex behaviors comprehensively. We propose a model, called Personal Timed Hidden Markov Model (PTHMM), to settle the problem by considering time-interval information of users' behaviors and users' personalization. The experimental result on sina-weibo demonstrates the effectiveness of the model. It also shows that users' behavioral state is affected by their historical behaviors, and the influence of historical behaviors declines with the increasing of historical time.