OpenRaiser/Intern-Atlas huggingface dataset:
Privacy nudges for social media: an exploratory Facebook study
Authors: Yang Wang, Pedro Giovanni Leon, Kevin Scott, Xiaoxuan Chen, Alessandro Acquisti, Lorrie Faith Cranor
Venue: WWW
Year: 2013

Anecdotal evidence and scholarly research have shown that a significant portion of Internet users experience regrets over their online disclosures. To help individuals avoid regrettable online disclosures, we employed lessons from behavioral decision research and research on soft paternalism to design mechanisms that "nudge" users to consider the content and context of their online disclosures before posting them. We developed three such privacy nudges on Facebook. The first nudge provides visual cues about the audience for a post. The second nudge introduces time delays before a post is published. The third nudge gives users feedback about their posts. We tested the nudges in a three-week exploratory field trial with 21 Facebook users, and conducted 13 follow-up interviews. Our system logs, results from exit surveys, and interviews suggest that privacy nudges could be a promising way to prevent unintended disclosure. We discuss limitations of the current nudge designs and future directions for improvement.
How social network is evolving?: a preliminary study on billion-scale twitter network
Authors: Masaru Watanabe, Toyotaro Suzumura
Venue: WWW
Year: 2013

Recently, social network services such as Twitter, Facebook, MySpace, LinkedIn have been remarkably growing. There are various studies about social networks analysis. Haewoon Kwak performed the analysis of the Twitter network on 2009 and shows the degree of separation. However, the number of users on 2009 is about 41.7 million, the graph scale is not very large compared with the current graph. In this paper, we conduct a Twitter network analysis in terms growth by region, scale-free, reciprocity, degree of separation and diameter using Twitter user data with 469.9 million users and 28.7 billion relationships. We report that the value of degree of separation is 4.59 in current Twitter network through our experiments.
Inferring audience partisanship for YouTube videos
Authors: Ingmar Weber, Venkata Rama Kiran Garimella, Erik Borra
Venue: WWW
Year: 2013

Political campaigning and the corresponding advertisement money are increasingly moving online. Some analysts claim that the U.S.~elections were partly won through a smart use of (i) targeted advertising and (ii) social media. But what type of information do politicized users consume online? And, the other way around, for a given content, e.g. a YouTube video, is it possible to predict its political audience? To address this latter question, we present a large scale study of anonymous YouTube video consumption of politicized users, where political orientation is derived from visits to "beacon pages", namely, political partisan blogs. Though our techniques are relevant for targeted political advertising, we believe that our findings are also of a wider interest.
DFT-extractor: a system to extract domain-specific faceted taxonomies from wikipedia
Authors: Bifan Wei, Jun Liu, Jian Ma, Qinghua Zheng, Wei Zhang, Boqin Feng
Venue: WWW
Year: 2013

Extracting faceted taxonomies from the Web has received increasing attention in recent years from the web mining community. We demonstrate in this study a novel system called DFT-Extractor, which automatically constructs domain-specific faceted taxonomies from Wikipedia in three steps: 1) It crawls domain terms from Wikipedia by using a modified topical crawler. 2) Then it exploits a classification model to extract hyponym relations with the use of motif-based features. 3) Finally, it constructs a faceted taxonomy by applying a community detection algorithm and a group of heuristic rules. DFT-Extractor also provides a graphical user interface to visualize the learned hyponym relations and the tree structure of taxonomies.
From cookies to cooks: insights on dietary patterns via analysis of web usage logs
Authors: Robert West, Ryen W. White, Eric Horvitz
Venue: WWW
Year: 2013

Nutrition is a key factor in people's overall health. Hence, understanding the nature and dynamics of population-wide dietary preferences over time and space can be valuable in public health. To date, studies have leveraged small samples of participants via food intake logs or treatment data. We propose a complementary source of population data on nutrition obtained via Web logs. Our main contribution is a spatiotemporal analysis of population-wide dietary preferences through the lens of logs gathered by a widely distributed Web-browser add-on, using the access volume of recipes that users seek via search as a proxy for actual food consumption. We discover that variation in dietary preferences as expressed via recipe access has two main periodic components, one yearly and the other weekly, and that there exist characteristic regional differences in terms of diet within the United States. In a second study, we identify users who show evidence of having made an acute decision to lose weight. We characterize the shifts in interests that they express in their search queries and focus on changes in their recipe queries in particular. Last, we correlate nutritional time series obtained from recipe queries with time-aligned data on hospital admissions, aimed at understanding how behavioral data captured in Web logs might be harnessed to identify potential relationships between diet and acute health problems. In this preliminary study, we focus on patterns of sodium identified in recipes over time and patterns of admission for congestive heart failure, a chronic illness that can be exacerbated by increases in sodium intake.
The web as a laboratory
Authors: Bebo White
Venue: WWW
Year: 2013

Insights from Web Science and Big Data Analysis have led many researchers to the conclusion that the Web not only represents an almost unlimited data store but also a remarkable multi-disciplinary laboratory environment. A new challenge is how to best leverage the potential of this experimental space. What are the procedures for defining, implementing and evaluating "Web-scale" experiments? What are acceptable measures of robustness and repeatability? What are the opportunities for experimental collaboration? What disciplines are likely to benefit from this new research model? The Web Laboratory model provides an exciting new and fertile model for future research.
Numeric query ranking approach
Authors: Jie Wu, Yi Liu, Ji-Rong Wen
Venue: WWW
Year: 2013

We handle a special category of Web queries, queries containing numeric terms. We call them numeric queries. Motivated by some issues in ranking of numeric queries, we detect numeric sensitive queries by mining from retrieved documents using phrase operator. We also propose features based on numeric terms by extracting reliable numeric terms for each document. Finally, a ranking model is trained for numeric sensitive queries, combining proposed numeric-related features and traditional features. Experiments show that our model can significantly improve relevance for numeric sensitive queries.
Inferring dependency constraints on parameters for web services
Authors: Qian Wu, Ling Wu, Guangtai Liang, Qianxiang Wang, Tao Xie, Hong Mei
Venue: WWW
Year: 2013

Recently many popular websites such as Twitter and Flickr expose their data through web service APIs, enabling third-party organizations to develop client applications that provide function-alities beyond what the original websites offer. These client appli-cations should follow certain constraints in order to correctly in-teract with the web services. One common type of such constraints is Dependency Constraints on Parameters. Given a web service operation O and its parameters Pi, Pj, these constraints describe the requirement on one parameter Pi that is dependent on the conditions of some other parameter(s) Pj. For example, when requesting the Twitter operation "GET statuses/user_timeline", a user_id parameter must be provided if a screen_name parameter is not provided. Violations of such constraints can cause fatal errors or incorrect results in the client applications. However, these con-straints are often not formally specified and thus not available for automatic verification of client applications. To address this issue, we propose a novel approach, called INDICATOR, to automatically infer dependency constraints on parameters for web services, via a hybrid analysis of heterogeneous web service artifacts, including the service documentation, the service SDKs, and the web services themselves. To evaluate our approach, we applied INDICATOR to infer dependency constraints for four popular web services. The results showed that INDICATOR effectively infers constraints with an average precision of 94.4% and recall of 95.5%.
Searching the deep web using proactive phrase queries
Authors: Wensheng Wu, Tingting Zhong
Venue: WWW
Year: 2013

This paper proposes ipq, a novel search engine that proactively transforms query forms of Deep Web sources into phrase queries, constructs query evaluation plans, and caches results for popular queries offline. Then at query time, keyword queries are simply matched with phrase queries to retrieve results. ipq embodies a novel dual-ranking framework for query answering and novel solutions for discovering frequent attributes and queries. Preliminary experiments show the great potentials of ipq.
QMapper: a tool for SQL optimization on hive using query rewriting
Authors: Yingzhong Xu, Songlin Hu
Venue: WWW
Year: 2013

Although HiveQL offers similar features with SQL, it is still difficult to map complex SQL queries into HiveQL and manual translation often leads to poor performance. A tool named QMapper is developed to address this problem by utilizing query rewriting rules and cost-based MapReduce flow evaluation on the basis of column statistics. Evaluation demonstrates that while assuring the correctness, QMapper improves the performance up to 42% in terms of execution time.
A biterm topic model for short texts
Authors: Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng
Venue: WWW
Year: 2013

Uncovering the topics within short texts, such as tweets and instant messages, has become an important task for many content analysis applications. However, directly applying conventional topic models (e.g. LDA and PLSA) on such short texts may not work well. The fundamental reason lies in that conventional topic models implicitly capture the document-level word co-occurrence patterns to reveal topics, and thus suffer from the severe data sparsity in short documents. In this paper, we propose a novel way for modeling topics in short texts, referred as biterm topic model (BTM). Specifically, in BTM we learn the topics by directly modeling the generation of word co-occurrence patterns (i.e. biterms) in the whole corpus. The major advantages of BTM are that 1) BTM explicitly models the word co-occurrence patterns to enhance the topic learning; and 2) BTM uses the aggregated patterns in the whole corpus for learning topics to solve the problem of sparse word co-occurrence patterns at document-level. We carry out extensive experiments on real-world short text collections. The results demonstrate that our approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics. Furthermore, we find that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model.
Semantically sampling in heterogeneous social networks
Authors: Cheng-Lun Yang, Perng-Hwa Kung, Chun-An Chen, Shou-De Lin
Venue: WWW
Year: 2013

Online social networks sampling identifies a representative subnetwork that preserves certain graph property given het- erogeneous semantics, with the full network not observed during sampling. This study presents a property, Relational Profile, to account for conditional dependency of node and relation type semantics in a network, and a sampling method to preserve the property. We show the proposed sampling method better preserves Relational Profile. Next, Relational Profile can design features to boost network prediction. Fi- nally, our sampled network trains more accurate prediction models than other sampling baselines.
Unfolding dynamics in a social network: co-evolution oflink formation and user interaction
Authors: Zhi Yang, Jilong Xue, Han Xiao Zhao, Xiao Wang, Ben Y. Zhao, Yafei Dai
Venue: WWW
Year: 2013

Measurement studies of online social networks show that all social links are not equal, and the strength of each link is best characterized by the frequency of interactions between the linked users.To date, few studies have been able to examine detailed interaction data over time, and none have studied the problem of modeling user interactions. This paper proposes a generative model of social interactions that captures the inherently heterogeneous strengths of social links, thus having broad implications on the design of social network algorithms such as friend recommendation, information diffusion and viral marketing.
Unified entity search in social media community
Authors: Ting Yao, Yuan Liu, Chong-Wah Ngo, Tao Mei
Venue: WWW
Year: 2013

The search for entities is the most common search behavior on the Web, especially in social media communities where entities (such as images, videos, people, locations, and tags) are highly heterogeneous and correlated. While previous research usually deals with these social media entities separately, we are investigating in this paper a unified, multi-level, and correlative entity graph to represent the unstructured social media data, through which various applications (e.g., friend suggestion, personalized image search, image tagging, etc.) can be realized more effectively in one single framework. We regard the social media objects equally as "entities" and all of these applications as "entity search" problem which searches for entities with different types. We first construct a multi-level graph which organizes the heterogeneous entities into multiple levels, with one type of entities as vertices in each level. The edges between graphs pairwisely connect the entities weighted by intra-relations in the same level and inter-links across two different levels distilled from the social behaviors (e.g., tagging, commenting, and joining communities). To infer the strength of intra-relations, we propose a circular propagation scheme, which reinforces the mutual exchange of information across different entity types in a cyclic manner. Based on the constructed unified graph, we explicitly formulate entity search as a global optimization problem in a unified Bayesian framework, in which various applications are efficiently realized. Empirically, we validate the effectiveness of our unified entity graph for various social media applications on million-scale real-world dataset.
Correlation discovery in web of things
Authors: Lina Yao, Quan Z. Sheng
Venue: WWW
Year: 2013

With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, Web of Things (WoT) is gaining a considerable momentum as an emerging paradigm where billions of physical objects will be interconnected and present over the World Wide Web. One inevitable challenge in the new era of WoT lies in how to efficiently and effectively manage things, which is critical for a number of important applications such as object search, recommendation, and composition. In this paper, we propose a novel approach to discover the correlations of things by constructing a relational network of things (RNT) where similar things are linked via virtual edges according to their latent correlations by mining three dimensional information in the things usage events in terms of user, temporality and spatiality. With RNT, many problems centered around things management such as objects classification, discovery and recommendation can be solved by exploiting graph-based algorithms. We conducted experiments using real-world data collected over a period of four months to verify and evaluate our model and the results demonstrate the feasibility of our approach.
Graded relevance ranking for synonym discovery
Authors: Andrew Yates, Nazli Goharian, Ophir Frieder
Venue: WWW
Year: 2013

Interest in domain-specific search is steadfastly increasing, yielding a growing need for domain-specific synonym discovery. Existing synonym discovery methods perform poorly when faced with the realistic task of identifying a target term's synonyms from among many candidates. We approach domain-specific synonym discovery as a graded relevance ranking problem in which a target term's synonym candidates are ranked by their quality. In this scenario a human editor uses each ranked list of synonym candidates to build a domain-specific thesaurus. We evaluate our method for graded relevance ranking of synonym candidates and find that it outperforms existing methods.
Predicting positive and negative links in signed social networks by transfer learning
Authors: Jihang Ye, Hong Cheng, Zhe Zhu, Minghua Chen
Venue: WWW
Year: 2013

Different from a large body of research on social networks that has focused almost exclusively on positive relationships, we study signed social networks with both positive and negative links. Specifically, we focus on how to reliably and effectively predict the signs of links in a newly formed signed social network (called a target network). Since usually only a very small amount of edge sign information is available in such newly formed networks, this small quantity is not adequate to train a good classifier. To address this challenge, we need assistance from an existing, mature signed network (called a source network) which has abundant edge sign information. We adopt the transfer learning approach to leverage the edge sign information from the source network, which may have a different yet related joint distribution of the edge instances and their class labels.
SHERLOCK: a system for location-based services in wireless environments using semantics
Authors: Roberto Yus, Eduardo Mena, Sergio Ilarri, Arantza Illarramendi
Venue: WWW
Year: 2013

Nowadays people are exposed to huge amounts of information that are generated continuously. However, current mobile applications, Web pages, and Location-Based Services (LBSs) are designed for specific scenarios and goals. In this demo we show the system SHERLOCK, which searches and shares up-to-date knowledge from nearby devices to relieve the user from knowing and managing such knowledge directly. Besides, the system guides the user in the process of selecting the service that best fits his/her needs in the given context.
Modeling click and relevance relationship for sponsored search
Authors: Wei Vivian Zhang, Ye Chen, Mitali Gupta, Swaraj Sett, Tak W. Yan
Venue: WWW
Year: 2013

Click-through rate (CTR) prediction and relevance ranking are two fundamental problems in web advertising. In this study, we address the problem of modeling the relationship between CTR and relevance for sponsored search. We used normalized relevance scores comparable across all queries to represent relevance when modeling with CTR, instead of directly using human judgment labels or relevance scores valid only within same query. We classified clicks by identifying their relevance quality using dwell time and session information, and compared all clicks versus selective clicks effects when modeling relevance.
An error driven approach to query segmentation
Authors: Wei Zhang, Yunbo Cao, Chin-Yew Lin, Jian Su, Chew Lim Tan
Venue: WWW
Year: 2013

Query segmentation is the task of splitting a query into a sequence of non-overlapping segments that completely cover all tokens in the query. The majority of query segmentation methods are unsupervised. In this paper, we propose an error-driven approach to query segmentation (EDQS) with the help of search logs, which enables unsupervised training with guidance from the system-specific errors. In EDQS, we first detect the system's errors by examining the consistency among the segmentations of similar queries. Then, a model is trained by the detected errors to select the correct segmentation of a new query from the top-n outputs of the system. Our evaluation results show that EDQS can significantly boost the performance of state-of-the-art query segmentation methods on a publicly available data set.
TopRec: domain-specific recommendation through community topic mining in social network
Authors: Xi Zhang, Jian Cheng, Ting Yuan, Biao Niu, Hanqing Lu
Venue: WWW
Year: 2013

Traditionally, Collaborative Filtering assumes that similar users have similar responses to similar items. However, human activities exhibit heterogenous features across multiple domains such that users own similar tastes in one domain may behave quite differently in other domains. Moreover, highly sparse data presents crucial challenge in preference prediction. Intuitively, if users' interested domains are captured first, the recommender system is more likely to provide the enjoyed items while filter out those uninterested ones. Therefore, it is necessary to learn preference profiles from the correlated domains instead of the entire user-item matrix. In this paper, we propose a unified framework, TopRec, which detects topical communities to construct interpretable domains for domain-specific collaborative filtering. In order to mine communities as well as the corresponding topics, a semi-supervised probabilistic topic model is utilized by integrating user guidance with social network. Experimental results on real-world data from Epinions and Ciao demonstrate the effectiveness of the proposed framework.
Discovering multilingual concepts from unaligned web documents by exploring associated images
Authors: Xiaochen Zhang, Xiaoming Jin, Lianghao Li, Dou Shen
Venue: WWW
Year: 2013

The Internet is experiencing an explosion of information presented in different languages. Though written in different languages, some articles implicitly share common concepts. In this paper, we propose a novel framework to mine cross-language common concepts from unaligned web documents. Specifically, visual words of images are used to bridge articles in different languages and then common concepts of multiple languages are learned by using an existing topic modeling algorithm. We conduct cross-lingual text classification in a real-world data set using the mined multilingual concepts from our method. The experiment results show that our approach is effective to mine cross-lingual common concepts.
Detecting real-time burst topics in microblog streams: how sentiment can help
Authors: Lumin Zhang, Yan Jia, Bin Zhou, Yi Han
Venue: WWW
Year: 2013

Microblog has become an increasing valuable resource of up-to-date topics about what is happening in the world. In this paper, we propose a novel approach of detecting real-time events in microblog streams based on bursty sentiments detection. Instead of traditional sentiment orientation like positive, negative and neutral, we use sentiment vector as our sentiment model to abstract subjective messages which are then used to detect bursts and clustered into new events. Experimental evaluations show that our approach could perform effectively for online event detection. Although we worked with Chinese in our research, the technique can be used with any other language.
Understanding user spatial behaviors for location-based recommendations
Authors: Jun Zhang, Chun-yuen Teng, Yan Qu
Venue: WWW
Year: 2013

In this paper, we introduce a network-based method to study user spatial behaviors based on check-in histories. The results of this study have direct implications for location-based recommendation systems.