OpenRaiser/Intern-Atlas huggingface dataset:
Predictability of distrust with interaction data
Authors: Jiliang Tang, Xia Hu, Yi Chang, Huan Liu
Venue: Conference CIKM 2014
Year: 2014

Trust plays a crucial role in helping users collect reliable information in an online world, and has attracted more and more attention in research communities lately. As a conceptual counterpart of trust, distrust can be as important as trust. However, distrust is rarely studied in social media because distrust information is usually unavailable. The value of distrust has been widely recognized in social sciences and recent work shows that distrust can benefit various online applications in social media. In this work, we investigate whether we can obtain distrust information via learning when it is not directly available, and propose to study a novel problem - predicting distrust using pervasively available interaction data in an online world. In particular, we analyze interaction data, provide a principled way to mathematically incorporate interaction data in a novel framework dTrust to predict distrust information. Experimental results using real-world data show that distrust information is predictable with interaction data by the proposed framework dTrust. Further experiments are conducted to gain a deep understand on which factors contribute to the effectiveness of the proposed framework.
A cross-cultural framework for protecting user privacy in online social media
Authors: Blase Ur, Yang Wang
Venue: Conference WWW 2013
Year: 2013

Social media has become truly global in recent years. We argue that support for users' privacy, however, has not been extended equally to all users from around the world. In this paper, we survey existing literature on cross-cultural privacy issues, giving particular weight to work specific to online social networking sites. We then propose a framework for evaluating the extent to which social networking sites' privacy options are offered and communicated in a manner that supports diverse users from around the world. One aspect of our framework focuses on cultural issues, such as norms regarding the use of pseudonyms or posting of photographs. A second aspect of our framework discusses legal issues in cross-cultural privacy, including data-protection requirements and questions of jurisdiction. The final part of our framework delves into user expectations regarding the data-sharing practices and the communication of privacy information. The framework can enable service providers to identify potential gaps in support for user privacy. It can also help researchers, regulators, or consumer advocates reason systematically about cultural differences related to privacy in social media.
Latent space model for multi-modal social data
Authors: Yoon-Sik Cho, Greg Ver Steeg, Emilio Ferrara, Aram Galstyan
Venue: Conference WWW 2016
Year: 2016

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.
Tailored News in the Palm of Your Hand: A Multi-perspective Transparent Approach to News Recommendation
Authors: Mozhgan Tavakolifard, Jon Atle Gulla, Kevin C. Almeroth, Jon Espen Ingvaldsen, Gaute Nygreen, Erik Berg
Venue: Conference WWW 2013
Year: 2013

Mobile news recommender systems help users retrieve news that is relevant in their particular context and can be presented in ways that require minimal user interaction. In spite of the availability of contextual information about mobile users, though, current mobile news applications employ rather simple strategies for news recommendation. Our multi-perspective approach unifies temporal, locational, and preferential information to provide a more fine-grained recommendation strategy. This demo paper presents the implementation of our solution to efficiently recommend specific news articles from a large corpus of newly-published press releases in a way that closely matches a reader's reading preferences.
Accelerated Focused Crawling Through Online Relevance Feedback
Authors: Soumen Chakrabarti, Kunal Punera, Mallela Subramanyam
Venue: Conference WWW 2002
Year: 2002

The organization of HTML into a tag tree structure, which is rendered by browsers as roughly rectangular regions with embedded text and HREF links, greatly helps surfers locate and click on links that best satisfy their information need. Can an automatic program emulate this human behavior and thereby learn to predict the relevance of an unseen HREF target page w.r.t. an information need, based on information limited to the HREF source page? Such a capability would be of great interest in focused crawling and resource discovery, because it can fine-tune the priority of unvisited URLs in the crawl frontier, and reduce the number of irrelevant pages which are fetched and discarded.
Learning to Discover Domain- Specific Web Content
Authors: Kien Pham, Aécio S. R. Santos, Juliana Freire
Venue: Conference WSDM 2018
Year: 2018

The ability to discover all content relevant to an information domain has many applications, from helping in the understanding of humanitarian crises to countering human and arms trafficking. In such applications, time is of essence: it is crucial to both maximize coverage and identify new content as soon as it becomes available, so that appropriate actions can be taken. In this paper, we propose new methods for efficient domain-specific re-crawling that maximize the yield for new content. By learning patterns of pages that have a high yield, our methods select a small set of pages that can be re-crawled frequently, increasing the coverage and freshness while conserving resources. Unlike previous approaches to this problem, our methods combine different factors to optimize the re-crawling strategy, do not require full snapshots for the learning step, and dynamically adapt the strategy as the crawl progresses. In an empirical evaluation, we have simulated the framework over 600 partial crawl snapshots in three different domains. The results show that our approach can achieve 150% higher coverage compared to existing, state-of-the-art techniques. In addition, it is also able to capture 80% of new relevant content within less than 4 hours of publication.
Pravda-live: interactive knowledge harvesting
Authors: Yafang Wang, Maximilian Dylla, Zhaochun Ren, Marc Spaniol, Gerhard Weikum
Venue: Conference CIKM 2012
Year: 2012

Acquiring high-quality (temporal) facts for knowledge bases is a labor-intensive process. Although there has been recent progress in the area of semi-supervised fact extraction, these approaches still have limitations, including a restricted corpus, a fixed set of relations to be extracted or a lack of assessment capabilities. In this paper we introduce PRAVDA-live, a framework that overcomes these limitations and supports the entire pipeline of interactive knowledge harvesting. To this end, our demo exhibits fact extraction from ad-hoc corpus creation, via relation specification, labeling and assessment all the way to ready-to-use RDF exports.
A Context-aware Collaborative Filtering Approach for Urban Black Holes Detection
Authors: Li Jin, Zhuonan Feng, Ling Feng
Venue: Conference CIKM 2016
Year: 2016

Urban black hole, as a traffic anomaly, has caused lots of catastrophic accidents in many big cities nowadays. Traditional methods only depend on the single source data (e.g., taxi trajectories) to design blackhole detection algorithm from one point of view, which is rather incomplete to describe the regional crowd flow. In this paper, we model the urban black holes in each region of New York City (NYC) at different time intervals with a 3-dimensional tensor by fusing cross-domain data sources. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we leverage the knowledge from geographical features, 311 complaint features and human mobility features to recover the blackhole situation throughout NYC. The information can facilitate local residents and officials' decision making. We evaluate our model with five datasets related to NYC, diagnosing the urban black holes that cannot be identified (or earlier than those detected) by a single dataset. Experimental results demonstrate the advantages beyond four baseline methods.
Exploring the Urban Region-of-Interest Through the Analysis of Online Map Search Queries
Authors: Ying Sun, Hengshu Zhu, Fuzhen Zhuang, Jingjing Gu, Qing He
Venue: Conference KDD 2018
Year: 2018

Urban Region-of-Interest (ROI) refers to the integrated urban areas with specific functionalities that attract people's attentions and activities, such as the recreational business districts, transportation hubs, and city landmarks. Indeed, at the macro level, ROI is one of the representatives for agglomeration economies, and plays an important role in urban business planning. At the micro level, ROI provides a useful venue for understanding the urban lives, demands and mobilities of people. However, due to the vague and diversified nature of ROI, it still lacks of quantitative ways to investigate ROIs in a holistic manner. To this end, in this paper we propose a systematic study on ROI analysis through mining the large-scale online map query logs, which provides a new data-driven research paradigm for ROI detection and profiling. Specifically, we first divide the urban area into small region grids, and calculate their PageRank value as visiting popularity based on the transition information extracted from map queries. Then, we propose a density-based clustering method for merging neighboring region grids with high popularity into integrated ROIs. After that, to further explore the profiles of different ROIs, we develop a spatial-temporal latent factor model URPTM (Urban Roi Profiling Topic Model) to identify the latent travel patterns and Point-of-Interest (POI) demands of ROI visitors. Finally, we implement extensive experiments to empirically evaluate our approaches based on the large-scale real-world data collected from Beijing. Indeed, by visualizing the results obtained from URPTM, we can successfully obtain many meaningful travel patterns and interesting discoveries on urban lives.
Simultaneous Urban Region Function Discovery and Popularity Estimation via an Infinite Urbanization Process Model
Authors: Bang Zhang, Lelin Zhang, Ting Guo, Yang Wang, Fang Chen
Venue: Conference KDD 2018
Year: 2018

Urbanization is a global trend that we have all witnessed in the past decades. It brings us both opportunities and challenges. On the one hand, urban system is one of the most sophisticated social-economic systems that is responsible for efficiently providing supplies meeting the demand of residents in various of domains, e.g., dwelling, education, entertainment, healthcare, etc. On the other hand, significant diversity and inequality exist in the development patterns of urban systems, which makes urban data analysis difficult. Different urban regions often exhibit diverse urbanization patterns and provide distinct urban functions, e.g., commercial and residential areas offer significantly different urban functions. It is desired to develop the data analytic capabilities for discovering the underlying cross-domain urbanization patterns, clustering urban regions based on their function similarity and predicting region popularity in specified domains. Previous studies in the urban data analysis area often just focus on individual domains and rarely consider cross-domain urban development patterns hidden in different urban regions. In this paper, we propose the infinite urbanization process (IUP) model for simultaneous urban region function discovery and region popularity prediction. The IUP model is a generative Bayesian nonparametric process that is capable of describing a potentially infinite number of urbanization patterns. It is developed within the supervised topic modelling framework and is supported by a novel hierarchical spatial distance dependent Bayesian nonparametric prior over the spatial region partition space. The empirical study conducted on the real-world datasets shows promising outcome compared with the state-of-the-art techniques.
Injecting user models and time into precision via Markov chains
Authors: Marco Ferrante, Nicola Ferro, Maria Maistro
Venue: Conference SIGIR 2014
Year: 2014

We propose a family of new evaluation measures, called Markov Precision (MP), which exploits continuous-time and discrete-time Markov chains in order to inject user models into precision. Continuous-time MP behaves like time-calibrated measures, bringing the time spent by the user into the evaluation of a system; discrete-time MP behaves like traditional evaluation measures. Being part of the same Markovian framework, the time-based and rank-based versions of MP produce values that are directly comparable.
How well does result relevance predict session satisfaction?
Authors: Scott B. Huffman, Michael Hochster
Venue: Conference SIGIR 2007
Year: 2007

Per-query relevance measures provide standardized, repeatable measurements of search result quality, but they ignore much of what users actually experience in a full search session. This paper examines how well we can approximate a user's ultimate session-level satisfaction using a simple relevance metric. We find that thisrelationship is surprisingly strong. By incorporating additional properties of the query itself, we construct a model which predicts user satisfaction even more accurately than relevance alone.
Predicting search user examination with visual saliency
Authors: Yiqun Liu, Zeyang Liu, Ke Zhou, Meng Wang, Huanbo Luan, Chao Wang, Min Zhang, Shaoping Ma
Venue: Conference SIGIR 2016
Year: 2016

Predicting users' examination of search results is one of the key concerns in Web search related studies. With more and more heterogeneous components federated into search engine result pages (SERPs), it becomes difficult for traditional position-based models to accurately predict users' actual examination patterns. Therefore, a number of prior works investigate the connection between examination and users' explicit interaction behaviors (e.g.~click-through, mouse movement). Although these works gain much success in predicting users' examination behavior on SERPs, they require the collection of large scale user behavior data, which makes it impossible to predict examination behavior on newly-generated SERPs. To predict user examination on SERPs containing heterogenous components without user interaction information, we propose a new prediction model based on visual saliency map and page content features. Visual saliency, which is designed to measure the likelihood of a given area to attract human visual attention, is used to predict users' attention distribution on heterogenous search components. With an experimental search engine, we carefully design a user study in which users' examination behavior (eye movement) is recorded. Examination prediction results based on this collected data set demonstrate that visual saliency features significantly improve the performance of examination model in heterogeneous search environments. We also found that saliency features help predict internal examination behavior within vertical results.
Context-aware search personalization with concept preference
Authors: Di Jiang, Kenneth Wai-Ting Leung, Wilfred Ng
Venue: Conference CIKM 2011
Year: 2011

As the size of the web is growing rapidly, a well-recognized challenge for developing web search engines is to optimize the search result towards each user's preference. In this paper, we propose and develop a new personalization framework that captures the user's preference in the form of concepts obtained by mining web search contexts. The search context consists of both the user's clickthroughs and query reformulations that satisfy some specific information need, which is able to provide more information than each individual query in a search session. We also propose a method that discovers search contexts by one-pass of raw search query log. Using the information of the search context, we develop eight strategies that derive conceptual preference judgment. A learning-to-rank approach is employed to combine the derived preference judgments and then a Context-Aware User Profile (CAUP) is created. We further employ CAUP to adapt a personalized ranking function. Experimental results demonstrate that our approach captures accurate and comprehensive user's preference and, in terms of Top-N results quality, outperforms those existing concept-based personalization approaches without using search contexts.
Mining long-term search history to improve search accuracy
Authors: Bin Tan, Xuehua Shen, ChengXiang Zhai
Venue: Conference KDD 2006
Year: 2006

Long-term search history contains rich information about a user&apos;s search preferences. In this paper, we study statistical language modeling based methods to mine contextual information from longterm search history and to exploit it for more accurate estimates of the query model. The experiments on a web search test collection show that the algorithms are effective in improving retrieval accuracy for both fresh and recurring queries. The best performance is achieved when using the combination of related past searches and clickthrough data as the main source of search context.
Large-Scale Analysis of Style Injection by Relative Path Overwrite
Authors: Sajjad Arshad, Seyed Ali Mirheidari, Tobias Lauinger, Bruno Crispo, Engin Kirda, William K. Robertson
Venue: Conference WWW 2018
Year: 2018

Relative Path Overwrite (RPO) is a recent technique to inject style directives into sites even when no style sink or markup injection vulnerability is present. It exploits differences in how browsers and web servers interpret relative paths (i.e., path confusion) to make a HTML page reference itself as a stylesheet; a simple text injection vulnerability along with browsers» leniency in parsing CSS resources results in an attacker»s ability to inject style directives that will be interpreted by the browser. Even though style injection may appear less serious a threat than script injection, it has been shown that it enables a range of attacks, including secret exfiltration. In this paper, we present the first large-scale study of the Web to measure the prevalence and significance of style injection using RPO. Our work shows that around 9% of the sites in the Alexa Top 10,000 contain at least one vulnerable page, out of which more than one third can be exploited. We analyze in detail various impediments to successful exploitation, and make recommendations for remediation. In contrast to script injection, relatively simple countermeasures exist to mitigate style injection. However, there appears to be little awareness of this attack vector as evidenced by a range of popular Content Management Systems (CMSes) that we found to be exploitable.
Are All People Married?: Determining Obligatory Attributes in Knowledge Bases
Authors: Jonathan Lajus, Fabian M. Suchanek
Venue: Conference WWW 2018
Year: 2018

An attribute is obligatory for a class in a Knowledge Base (KB), if all instances of the class have the attribute in the real world. For example, has­Birth­Date is an obligatory attribute for the class Person, while has ­ Spouse is not. In this paper, we propose a new way to model incompleteness in KBs. From this model, we derive a method to automatically determine obligatory attributes -- using only the data from the KB. Our algorithm can detect such attributes with a precision of up to 90%.
ShadowBlock: A Lightweight and Stealthy Adblocking Browser
Authors: Shitong Zhu, Umar Iqbal, Zhongjie Wang, Zhiyun Qian, Zubair Shafiq, Weiteng Chen
Venue: Conference WWW 2019
Year: 2019

As the popularity of adblocking has soared over the last few years, publishers are increasingly deploying anti-adblocking paywalls that ask users to either disable their adblockers or pay to access content. In this work we propose ShadowBlock, a new Chromium-based adblocking browser that can hide traces of adblocking activities from anti-adblockers as it removes ads from web pages. To bypass anti-adblocking paywalls, ShadowBlock takes advantage of existing filter lists used by adblockers and hides all ad elements stealthily in such a way that anti-adblocking scripts cannot detect any tampering of the ads (e.g., absence of ad elements). Specifically, ShadowBlock introduces lightweight hooks in Chromium to ensure that DOM states queried by anti-adblocking scripts are exactly as if adblocking is not employed. We implement a fully working prototype by modifying Chromium which shows great promise in terms of adblocking effectiveness and anti-adblocking circumvention but also more efficient than the state-of-the-art adblocking browser extensions. Our evaluation on Alexa top-1K websites shows that ShadowBlock successfully blocks 98.3% of all visible ads while only causing minor breakage on less than 0.6% of the websites. Most importantly, ShadowBlock is able to bypass anti-adblocking paywalls on more than 200 websites that deploy visible anti-adblocking paywalls with a 100% success rate. Our performance evaluation further shows that ShadowBlock loads pages as fast as the state-of-the-art adblocking browser extension on average.
Optimizing parallel algorithms for all pairs similarity search
Authors: Maha Ahmed Alabduljalil, Xun Tang, Tao Yang
Venue: Conference WSDM 2013
Year: 2013

All pairs similarity search is used in many web search and data mining applications. Previous work has used comparison filtering, inverted indexing, and parallel accumulation of partial intermediate results to expedite its execution. However, shuffling intermediate results can incur significant communication overhead as data scales up. This paper studies a scalable two-step approach called Partition-based Similarity Search (PSS) which incorporates several optimization techniques. First, PSS uses a static partitioning algorithm that places dissimilar vectors into different groups and balance the comparison workload with a circular assignment. Second, PSS executes comparison tasks in parallel, each using a hybrid data structure that combines the advantages of forward and inverted indexing. Our evaluation results show that the proposed approach leads to an early elimination of unnecessary I/O and data communication while sustaining parallel efficiency. As a result, it improves performance by an order of magnitude when dealing with large datasets.