OpenRaiser/Intern-Atlas huggingface dataset:
Users' behavioral prediction for phishing detection
Authors: Lung-Hao Lee, Kuei-Ching Lee, Yen-Cheng Juan, Hsin-Hsi Chen, Yuen-Hsien Tseng
Venue: WWW
Year: 2014

This study explores the users' web browsing behaviors that confront phishing situations for context-aware phishing detection. We extract discriminative features of each clicked URL, i.e., domain name, bag-of-words, generic Top-Level Domains, IP address, and port number, to develop a linear chain CRF model for users' behavioral prediction. Large-scale experiments show that our method achieves promising performance for predicting the phishing threats of users' next accesses. Error analysis indicates that our model results in a favorably low false positive rate. In practice, our solution is complementary to the existing anti-phishing techniques for cost-effectively blocking phishing threats from users' behavioral perspectives.
A novel link prediction approach for scale-free networks
Authors: Chungmok Lee, Minh Pham, Norman Kim, Myong K. Jeong, Dennis K. J. Lin, Wanpracha Art Chaovalitwongse
Venue: WWW
Year: 2014

The link prediction problem is to predict the existence of a link between every node pair in the network based on the past observed networks arising in many practical applications such as recommender systems, information retrieval, and the marketing analysis of social networks. Here, we propose a new mathematical programming approach for predicting a future network utilizing the node degree distribution identified from historical observation of the past networks. We develop an integer programming problem for the link prediction problem, where the objective is to maximize the sum of link scores (probabilities) while respecting the node degree distribution of the networks. The performance of the proposed framework is tested on the real-life Facebook networks. The computational results show that the proposed approach can considerably improve the performance of previously published link prediction methods.
Learning to predict trending queries: classification - based
Authors: Chi-Hoon Lee, Hengshuai Yao, Xu He, Su Han Chan, JieYang Chang, Farzin Maghoul
Venue: WWW
Year: 2014

Among the many tasks driven by very large scaled web search queries, it is an interesting task to predict how likely queries about a topic become popular (a.k.a. trending or buzzing) as the news in the near future, which is known as "Detecting trending queries." This task is nontrivial since the realization of buzzing trends of queries often requires sufficient statistics through users' activities. To address this challenge, we propose a novel framework that predicts whether queries become trending in the future. In principle, our system is built on the two learners. The first is to learn dynamics of time series for queries. The second, our decision maker, is to learn a binary classifier that determines whether queries become trending. Our framework is extremely efficient to be built taking advantage of the grid architecture that allows to deal with the large volume of data. In addition, it is flexible to continuously adapt as trending patterns evolve. The experiments results show that our approach achieves high quality of accuracy (over 77.5%} true positive rate) and yet detects much earlier (on average 29 hours advanced) than that of the baseline system.
Indicators and functionalities of exploitation of academic staff CV using semantic web technologies
Authors: Isaac Lera, Carlos Guerrero, Carlos Juiz
Venue: WWW
Year: 2014

We have transformed five years of curriculum data of our academic staff from relational databases to a semantic model. Thanks to semantic queries, capabilities of NoSQL models, inference reasoners and data mining techniques we obtain knowledge that it improves the personal management of curriculum data, the quality and efficiency of exploitation tasks, and the transparency, dissemination and collaboration with citizens. The huge catalogue of CV data remains an underutilized resource. Private companies such as editorials have robust services based only on publications but academic institutions have the option of integrating other databases related with their staff to obtain more indicators. We analyse the transformation of data, highlighting the mapping process of authors, and we present two ways of exploitation using semantic queries and complex networks. Thus, institutions, researchers and citizens will have a quality data catalogue for diverse studies.
User profiling in an ego network: co-profiling attributes and relationships
Authors: Rui Li, Chi Wang, Kevin Chen-Chuan Chang
Venue: WWW
Year: 2014

User attributes, such as occupation, education, and location, are important for many applications. In this paper, we study the problem of profiling user attributes in social network. To capture the correlation between attributes and social connections, we present a new insight that social connections are discriminatively correlated with attributes via a hidden factor -- relationship type. For example, a user's colleagues are more likely to share the same employer with him than other friends. Based on the insight, we propose to co-profile users' attributes and relationship types of their connections. To achieve co-profiling, we develop an efficient algorithm based on an optimization framework. Our algorithm captures our insight effectively. It iteratively profiles attributes by propagation via certain types of connections, and profiles types of connections based on attributes and the network structure. We conduct extensive experiments to evaluate our algorithm. The results show that our algorithm profiles various attributes accurately, which improves the state-of-the-art methods by 12%.
Quality evaluation of social tags according to web resource types
Authors: Lei Li, Chengzhi Zhang
Venue: WWW
Year: 2014

In the social tagging system, users annotate different web resources according to their need of future information organization and retrieval, and users also annotate resources with different types of tags, such as objective tag, subjective tag, self-organized tag and so on. Because every web resource has its own characteristics, the tag types of each web resource are different. According to the web resource, the quality of each tag type is different. We should depend on resource types to evaluate the quality of tag types, in order to provide efficient tag recommendation service and design better user tagging interfaces. In this paper, we firstly selected five web resources, namely the blog, book, image, music and video, to explore the tag types when annotating different resources. Then we chose specific resource and tags to explore the quality of each tag type according to these five web resources and study the relationship between tag type and quality. The conclusion is that the quality of tag types for different web resources is different.
Do you know the speaker?: an online experiment with authority messages on event websites
Authors: Kwan Hui Lim, Binyan Jiang, Ee-Peng Lim, Palakorn Achananuparp
Venue: WWW
Year: 2014

With the widespread adoption of the Web, many companies and organizations have established websites that provide information and support online transactions (e.g., buying products or viewing content). Unfortunately, users have limited attention to spare for interacting with online sites. Hence, it is of utmost importance to design sites that attract user attention and effectively guide users to the product or content items they like. Thus, we propose a novel and scalable experimentation approach to evaluate the effectiveness of online site designs. Our case study focuses on the effects of an authority message on visitors' browsing behavior on workshop and seminar online announcement sites. An authority message emphasizes a particular prominent speaker and his/her achievements. Through dividing users into control and treatment groups and carefully tracking their online activities, we observe that the authority message influences the way users interact with page elements on the website and increases their interests in the authority speakers.
LASER: a living analytics experimentation system for large-scale online controlled experiments
Authors: Kwan Hui Lim, Ee-Peng Lim, Palakorn Achananuparp, Adrian Vu, Agus Trisnajaya Kwee, Feida Zhu
Venue: WWW
Year: 2014

Tracking user browsing data and measuring the effectiveness of website design and web services are important to businesses that want to attract the consumers today who spend much more time online than before. Instead of using randomized controlled experiments, the existing approach simply tracks user browsing behaviors before and after a change is made to website design or web services, and evaluate the differences. To address the effects caused by hidden factors (e.g. promotion activities on the website) and to give fair comparison of different website designs, we propose the LASER system, a unified experimentation platform that enables randomized online controlled experiments to be easily conducted with minimal human effort and modifications to the experimented websites. More importantly, the LASER system manages the various aspects of online controlled experiments, namely the selection of participants into groups, exposure of different user interface features or recommendation algorithms to these groups, measuring their responses, and summarizing the results in the visual manner.
Classifying latent infection states in complex networks
Authors: Yeon-Sup Lim, Bruno F. Ribeiro, Don Towsley
Venue: WWW
Year: 2014

Algorithms for identifying the infection states of nodes in a network are crucial for understanding and containing epidemics and cascades. Often, however, only the infection states of a small set of nodes are known. Moreover, the length of time that each node has been infected is also unknown. This missing data – infection states of most nodes and the infection times of the observed infected nodes – poses a challenge to the study of real-world epidemics/cascades.In this work, we develop techniques to identify the latent infected nodes in the presence of missing infection time-and-state data. Based on likely epidemic paths predicted by a simple susceptible-infected epidemic model, we propose a measure (infection betweenness centrality) for uncovering unknown infection states. We evaluate infection state classifiers based on Naive Bayes, Naive Bayes with kernel density estimation, and decision trees, in combination with features including infection betweenness centrality and different centrality measures. Our experimental results show that among the set of features that include degree and different centrality measures, infection betweenness centrality is the most effective feature for identifying latent infected nodes.
Towards online review spam detection
Authors: Yuming Lin, Tao Zhu, Xiaoling Wang, Jingwei Zhang, Aoying Zhou
Venue: WWW
Year: 2014

User reviews play a crucial role in Web, since many decisions are made based on them. However, review spam would misled the users, which is extremely obnoxious. In this poster, we explore the problem of online review spam detection. Firstly, we devise six features to find the spam based on the review content and reviewer behaviors. Secondly, we apply supervised methods and an unsupervised one for spotting the review spam as early as possible. Finally, we carry out intensive experiments on a real-world review set to verify the proposed methods.
Automatic keywords generation for contextual advertising
Authors: Pengqi Liu, Javad Azimi, Ruofei Zhang
Venue: WWW
Year: 2014

Contextual Advertising (CA) is an important area in the industry of online advertising. Typically, CA algorithms return a set of related ads based on some keywords extracted from the content of webpages. Therefore, extracting the best set of representative keywords from a given webpage is the key to the success of CA. In this paper, we introduce a new keywords generation approach that uses some novel NLP features including POS and named-entities tagging. Unlike most of the existing keyword extraction algorithms, our proposed framework is able to generate some related keywords which do not exist in the webpage. A monetization parameter, predicted from historical search keyword performance, is also used to rank potential keywords in order to balance the RPM (Revenue Per 1000 Matches) and relevance. Experimental results over a very large real-world data set shows that the proposed approach can outperform the state-of-the-art system in both relevance and monetization metrics.
An article level metric in the context of research community
Authors: Yu Liu, Zhen Huang, Jing Fang, Yizhou Yan
Venue: WWW
Year: 2014

With the rapid increase of research papers, article-level metrics are of growing importance for helping researchers select papers. Classical metrics have a significant drawback of just using single factor, which limits the effectiveness of assessing papers in different periods after publication. Moreover, with the development of web 2.0, some new factors are introduced to assess papers. So, a novel article level metric in the context of research community (ALM_RC) is proposed. It integrates the impact of different factors comprehensively, because different factors have different time features and can complement each other in different periods after publication. In addition, as a research community is based on certain research directions, it is a relatively stable environment with related journals and scholars contributing their efforts to development of this research field. So in the context of research community, it is consistent, practical and reasonable to calculate the impact of the journals and scholars under relatively fair criteria. Experimental results show the novel metric is effective and robust in assessing papers.
Efficient RDF stream reasoning with graphics processingunits (GPUs)
Authors: Chang Liu, Jacopo Urbani, Guilin Qi
Venue: WWW
Year: 2014

In this paper, we study the problem of stream reasoning and propose a reasoning approach over large amounts of RDF data, which uses graphics processing units (GPU) to improve the performance. First, we show how the problem of stream reasoning can be reduced to a temporal reasoning problem. Then, we describe a number of algorithms to perform stream reasoning with GPUs.
Predicting ideological friends and foes in Twitter conflicts
Authors: Zhe Liu, Ingmar Weber
Venue: WWW
Year: 2014

The rise in popularity of Twitter in recent years has in parallel led to an increase in online controversies. To monitor and control such conflicts early on, we design and evaluate a language-agnostic classifier to tell pairs of ideological friends from foes. We build the classifier using features from four different aspects: user-based, interaction-based, relationship-based and conflict-based. By experimenting with three large data sets containing diverse conflicts, we demonstrate the effectiveness of language-agnostic classification of ideological relation, achieving satisfactory results across all three data sets. Such a classifier potentially enables studies of diverse conflicts on Twitter on a large scale.
Detecting community structure for undirected big graphs based on random walks
Authors: Xiaoming Liu, Yadong Zhou, Chengchen Hu, Xiaohong Guan, Junyuan Leng
Venue: WWW
Year: 2014

Community detection is a common problem in various types of big graphs. It is meaningful to understand the functions and dynamics of networks. The challenges of detecting community for big graphs include high computational cost, no prior information, etc.. In this work, we analyze the process of random walking in graphs, and find out that the weight of an edge gotten by processing the vertices visited by the walker could be an indicator to measure the closeness of vertex connection. Based on this idea, we propose a community detection algorithm for undirected big graphs which consists of three steps, including random walking using a single walker, weight calculating for edges and community detecting. Our algorithm is running in O(n2) without prior information. Experimental results show that our algorithm is capable of detecting the community structure and the overlapping parts of graphs in real-world effectively, and handling the challenges of community detection in big graph era.
Analysis of local online review systems as digital word-of-mouth
Authors: Claudia López, Rosta Farzan
Venue: WWW
Year: 2014

Using a large dataset of Yelp's online reviews for local businesses, we investigate how Word-of-Mouth research can inform the design of local online review systems and how these systems' data can extend our understanding of digital WOM in a local context. In this paper, we analyze how visual cues currently present in Yelp map to WOM concepts. We also show that these concepts are highly related to the perceived usefulness of the local reviews, which is aligned with prior WOM literature. Additionally, we found that local expertise, measured at the level of the neighborhood, strongly correlates with the perceived usefulness of reviews. Our findings augment the understanding of local online WOM and have design implications for local review systems.
Population dynamics in open source communities: an ecological approach applied to github
Authors: Pablo S. Loyola, In-Young Ko
Venue: WWW
Year: 2014

Open Source Software (OSS) has gained high amount of popularity during the last few years. It is becoming used by public and private institutions, even companies release portions of their code to obtain feedback from the community of voluntary developers. As OSS is based on the voluntary contributions of developers, the number of participants represents one of the key elements that impact the quality of the software. In order to understand how the the population of contributors evolve over time, we propose a methodology that adapts Lotka-Volterra-based biological models used for describing host-parasite interactions. Experiments based on data from the Github collaborative platform showed that the proposed approach performs effectively in terms of providing an estimation of the population of developers for each project over time.
Organizing the digital world to empower people to do more, know more, and be more
Authors: Qi Lu
Venue: WWW
Year: 2014

The web is rapidly evolving into the web of the world where people, places, things and their relationships are all digitally represented. This evolution opens up unparalleled opportunities to organize this vast digital universe for even greater human purpose. In this talk, Dr. Lu will share an outline of Microsoft's quest and aspiration to organize the digital universe with a pervasive computational fabric of digital information, digital services, and digital experiences that empower every human being on the planet to accomplish more and enrich their life. Dr. Lu will discuss high level computational structures and present specific examples across Bing, Windows and other products and services to illustrate Microsoft's approach to delivering end user value and accelerating the pace of innovation for the industry as a whole.
Complex network comparison using random walks
Authors: Shan Lu, Jieqi Kang, Weibo Gong, Don Towsley
Venue: WWW
Year: 2014

In this paper, we proposed a network comparison method based on the mathematical theory of diffusion over manifolds using random walks over graphs. We show that our method not only distinguishes between graphs with different degree distributions, but also different graphs with the same degree distributions. We compare the undirected power law graphs generated by Barabasi-Albert model and directed power law graphs generated by Krapivsky's model to the random graphs generated by Erdos-Renyi model. We also compare power law graphs generated by four different generative models with the same degree distribution.
The design of a live social observatory system
Authors: Huan-Bo Luan, Juanzi Li, Maosong Sun, Tat-Seng Chua
Venue: WWW
Year: 2014

With the emergence of social networks and their potential impact on society, many research groups and originations are collecting huge amount of social media data from various sites to serve different applications. These systems offer insights on different facets of society at different moments of time. Collectively they are known as social observatory systems. This paper describes the architecture and implementation of a live social observatory system named 'NExT-Live'. It aims to analyze the live online social media data streams to mine social senses, phenomena, influences and geographical trends dynamically. It incorporates an efficient and robust set of crawlers to continually crawl online social interactions on various social network sites. The data crawled are stored and processed in a distributed Hadoop architecture. It then performs the analysis on these social media streams jointly to generate analytics at different levels. In particular, it generates high-level analytics about the sense of different target entitles, including People, Locations, Topics and Organizations. NExT-Live offers a live observatory platform that enables people to know the happenings of the place in order to lead better life.
Modeling collaboration in academia: a game theoretic approach
Authors: Qiang Ma, S. Muthukrishnan, Brian Thompson, Graham Cormode
Venue: WWW
Year: 2014

In this work, we aim to understand the mechanisms driving academic collaboration. We begin by building a model for how researchers split their effort between multiple papers, and how collaboration affects the number of citations a paper receives, supported by observations from a large real-world publication and citation dataset, which we call the h-Reinvestment model. Using tools from the field of Game Theory, we study researchers' collaborative behavior over time under this model, with the premise that each researcher wants to maximize his or her academic success. We find analytically that there is a strong incentive to collaborate rather than work in isolation, and that studying collaborative behavior through a game-theoretic lens is a promising approach to help us better understand the nature and dynamics of academic collaboration.
When machines dominate humans: the challenges of mining and consuming machine-generated web mail
Authors: Yoelle Maarek
Venue: WWW
Year: 2014

In spite of personal communications moving more and more towards social and mobile, especially with younger generations, email traffic continues to grow. This growth is mostly attributed to (non-spam) machine-generated email, which, against common perception, is often extremely valuable. Indeed, together with monthly newsletters that can easily be ignored, inboxes contain flight itineraries, booking confirmations, receipts or invoices that are critical to many users. In this talk, I will discuss the new nature of consumer email, which is dominated by machine-generated messages of highly heterogeneous forms and value. I will show how the change has not been fully recognized yet by my most email clients (as an example, why should there still be a reply option associated with a message coming from a "[email protected]" address?). I will introduce some approaches for large-scale mail mining specifically tailored to machine-generated email. I will conclude by discussing possible applications and research directions.
Processing scientific mesh queries in graph databases
Authors: Alireza Rezaei Mahdiraji, Peter Baumann
Venue: WWW
Year: 2014

In this work-in-progress paper, we model scientific meshes as a multi-graph in Neo4j graph database using the graph property model. We conduct experiments to measure the performance of the graph database solution in processing mesh queries and compare it with GrAL mesh library and PostgreSQL database on synthetic and real mesh datasets. The experiments show that the databases outperform the mesh library. However, each of the databases perform better on specific query type, i.e, the graph database shows the best performance on global path-intensive queries and the relational database on local and field queries. Based on the experiments, we propose a mediator architecture for processing mesh queries by using the three database systems.