OpenRaiser/Intern-Atlas huggingface dataset:
Distinguishing topical and social groups based on common identity and bond theory
Authors: Przemyslaw A. Grabowicz, Luca Maria Aiello, Víctor M. Eguíluz, Alejandro Jaimes
Venue: WSDM
Year: 2013

Social groups play a crucial role in social media platforms because they form the basis for user participation and engagement. Groups are created explicitly by members of the community, but also form organically as members interact. Due to their importance, they have been studied widely (e.g., community detection, evolution, activity, etc.). One of the key questions for understanding how such groups evolve is whether there are different types of groups and how they differ. In Sociology, theories have been proposed to help explain how such groups form. In particular, the common identity and common bond theory states that people join groups based on identity (i.e., interest in the topics discussed) or bond attachment (i.e., social relationships). The theory has been applied qualitatively to small groups to classify them as either topical or social. We use the identity and bond theory to define a set of features to classify groups into those two categories. Using a dataset from Flickr, we extract user-defined groups and automatically-detected groups, obtained from a community detection algorithm. We discuss the process of manual labeling of groups into social or topical and present results of predicting the group label based on the defined features. We directly validate the predictions of the theory showing that the metrics are able to forecast the group type with high accuracy. In addition, we present a comparison between declared and detected groups along topicality and sociality dimensions.
Towards web-scale structured web data extraction
Authors: Tomas Grigalis
Venue: WSDM
Year: 2013

In this paper we present an ongoing PhD research on unsupervised and domain-independent structured data extraction from the Web. We propose a novel method to extract structured data records from template-generated Web pages. The method is based on clustering visually similar Web page elements by exploiting their visual formatting and HTML structural features. Tag paths of clustered Web page elements are then employed to derive extraction rules. These rules, called wrappers, can be later reused on thousands of same template-generated Web pages. This opens the possibility for the proposed method to be deployed in Web-Scale structured data extraction systems.
Crawling deep web entity pages
Authors: Yeye He, Dong Xin, Venkatesh Ganti, Sriram Rajaraman, Nirav Shah
Venue: WSDM
Year: 2013

Deep-web crawl is concerned with the problem of surfacing hidden content behind search interfaces on the Web. While many deep-web sites maintain document-oriented textual content (e.g., Wikipedia, PubMed, Twitter, etc.), which has traditionally been the focus of the deep-web literature, we observe that a significant portion of deep-web sites, including almost all online shopping sites, curate structured entities as opposed to text documents. Although crawling such entity-oriented content is clearly useful for a variety of purposes, existing crawling techniques optimized for document oriented content are not best suited for entity-oriented sites. In this work, we describe a prototype system we have built that specializes in crawling entity-oriented deep-web sites. We propose techniques tailored to tackle important subproblems including query generation, empty page filtering and URL deduplication in the specific context of entity oriented deep-web sites. These techniques are experimentally evaluated and shown to be effective.
Reusing historical interaction data for faster online learning to rank for IR
Authors: Katja Hofmann, Anne Schuth, Shimon Whiteson, Maarten de Rijke
Venue: WSDM
Year: 2013

Online learning to rank for information retrieval (IR) holds promise for allowing the development of "self-learning" search engines that can automatically adjust to their users. With the large amount of e.g., click data that can be collected in web search settings, such techniques could enable highly scalable ranking optimization. However, feedback obtained from user interactions is noisy, and developing approaches that can learn from this feedback quickly and reliably is a major challenge.
Exploiting social relations for sentiment analysis in microblogging
Authors: Xia Hu, Lei Tang, Jiliang Tang, Huan Liu
Venue: WSDM
Year: 2013

Microblogging, like Twitter and Sina Weibo, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiment and opinions on various topics. Existing sentiment analysis approaches often assume that texts are independent and identically distributed (i.i.d.), usually focusing on building a sophisticated feature space to handle noisy and short texts, without taking advantage of the fact that the microblogs are networked data. Inspired by the social sciences findings that sentiment consistency and emotional contagion are observed in social networks, we investigate whether social relations can help sentiment analysis by proposing a Sociological Approach to handling Noisy and short Texts (SANT) for sentiment classification. In particular, we present a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process; and utilize sparse learning to tackle noisy texts in microblogging. An empirical study of two real-world Twitter datasets shows the superior performance of our framework in handling noisy and short tweets.
Identifying users' topical tasks in web search
Authors: Wen Hua, Yangqiu Song, Haixun Wang, Xiaofang Zhou
Venue: WSDM
Year: 2013

A search task represents an atomic information need of a user in web search. Tasks consist of queries and their reformulations, and identifying tasks is important for search engines since they provide valuable information for determining user satisfaction with search results, predicting user search intent, and suggesting queries to the user. Traditional approaches to identifying tasks exploit either temporal or lexical features of queries. However, many query refinements are topical, which means that a query and its refinements may not be similar on the lexical level. Furthermore, multiple tasks in the same search session may interleave, which means we cannot simply order the searches by their timestamps and divide the session into multiple tasks. Thus, in order to identify tasks correctly, we need to be able to compare two queries at the semantic level. In this paper, we use a knowledgebase known as Probase to infer the conceptual meanings of queries, and automatically identify the topical query refinements in the tasks. Experimental results on real search log data demonstrate that Probase can indeed help estimate the topical affinity between queries, and thus enable us to merge queries that are topically related but dissimilar at the lexical level.
Wiki3C: exploiting wikipedia for context-aware concept categorization
Authors: Peng Jiang, Huiman Hou, Lijiang Chen, Shimin Chen, Conglei Yao, Chengkai Li, Min Wang
Venue: WSDM
Year: 2013

Wikipedia is an important human generated knowledge base containing over 21 million articles organized by millions of categories. In this paper, we exploit Wikipedia for a new task of text mining: Context-aware Concept Categorization. In the task, we focus on categorizing concepts according to their context. We exploit article link feature and category structure in Wikipedia, followed by introducing Wiki3C, an unsupervised and domain independent concept categorization approach based on context. In the approach, we investigate two strategies to select and filter Wikipedia articles for the category representation. Besides, a probabilistic model is employed to compute the semantic relatedness between two concepts in Wikipedia. Experimental evaluation using manually labeled ground truth shows that our proposed Wiki3C can achieve a noticeable improvement over the baselines without considering contextual information.
Playing by the rules: mining query associations to predict search performance
Authors: Youngho Kim, Ahmed Hassan Awadallah, Ryen W. White, Yi-Min Wang
Venue: WSDM
Year: 2013

Understanding the characteristics of queries where a search engine is failing is important for improving engine performance. Previous work largely relies on user-interaction features (e.g., clickthrough statistics) to identify such underperforming queries. However, relying on interaction behavior means that searchers need to become dissatisfied and need to exhibit that in their search behavior, by which point it may be too late to help them. In this paper, we propose a method to generate underperforming query identification rules instantly using topical and lexical attributes. The method first generates query attributes using sources such as topics, concepts (entities), and keywords in queries. Then, association rules are learned by exploiting the FP-growth algorithm and decision trees using underperforming query examples. We develop a query classification model capable of accurately estimating dissatisfaction using the generated rules, and demonstrate significant performance gains over state-of-the-art query performance prediction models.
Time-sensitive web image ranking and retrieval via dynamic multi-task regression
Authors: Gunhee Kim, Eric P. Xing
Venue: WSDM
Year: 2013

In this paper, we investigate a time-sensitive image retrieval problem, in which given a query keyword, a query time point, and optionally user information, we retrieve the most relevant and temporally suitable images from the database. Inspired by recently emerging interests on query dynamics in information retrieval research, our time-sensitive image retrieval algorithm can infer users' implicit search intent better and provide more engaging and diverse search results according to temporal trends of Web user photos. We model observed image streams as instances of multivariate point processes represented by several different descriptors, and develop a regularized multi-task regression framework that automatically selects and learns stochastic parametric models to solve the relations between image occurrence probabilities and various temporal factors that influence them. Using Flickr datasets of more than seven million images of 30 topics, our experimental results show that the proposed algorithm is more successful in time-sensitive image retrieval than other candidate methods, including ranking SVM, a PageRank-based image ranking, and a generative temporal topic model.
Have you done anything like that?: predicting performance using inter-category reputation
Authors: Marios Kokkodis, Panagiotis G. Ipeirotis
Venue: WSDM
Year: 2013

Online labor markets such as oDesk and Amazon Mechanical Turk have been growing in importance over the last few years. In these markets, employers post tasks on which remote contractors work and deliver the product of their work. As in most online marketplaces, reputation mechanisms play a very important role in facilitating transactions, since they instill trust and are often predictive of the future satisfaction of the employer. However, labor markets are usually highly heterogeneous in terms of available task categories; in such scenarios, past performance may not be a representative signal of future performance. To account for this heterogeneity, in our work, we build models that predict the performance of a worker based on prior, category-specific feedback. Our models assume that each worker has a category-specific quality, which is latent and not directly observable; what is observable, though, is the set of feedback ratings of the worker and of other contractors with similar work histories. Based on this information, we build a multi-level, hierarchical scheme that deals effectively with the data sparseness, which is inherent in many cases of interest (i.e., contractors with relatively brief work histories). We evaluate our models on a large corpus of real transactional data from oDesk, an online labor market with hundreds of millions of dollars in transaction volume. Our results show an improved accuracy of up to 47% compared to the existing baseline.
Rank quantization
Authors: Ravi Kumar, Ronny Lempel, Roy Schwartz, Sergei Vassilvitskii
Venue: WSDM
Year: 2013

We study the problem of aggregating and summarizing partial orders, on a large scale. Our motivation is two-fold: to discover elements at similar preference levels and to reduce the number of bits needed to store an element's position in a full ranking.We proceed in two steps: first, we find a total order by linearizing the rankings induced by the multiple partial orders and removing potentially inconsistent pairwise preferences. Next, given a total order, we introduce and formalize the rank quantization problem, which intuitively aims to bucketize the total order in a manner that mostly preserves the relations appearing in the partial orders. We show an exact quadratic-time quantization algorithm, as well as a greedy 2/3-approximation algorithm whose running is substantially faster on sparse instances. As an application, we aggregate rankings of top-10 search results over millions of search engine queries, approximately reproducing and then efficiently encoding the underlying static ranks used by the engine. We evaluate the performance of our algorithms on a web dataset of 12 million(2^{23.5}) unique pages and show that we can quantize the pages' static ranks using as few as eight bits, with only a minor degradation in search quality.
Geo topic model: joint modeling of user's activity area and interests for location recommendation
Authors: Takeshi Kurashima, Tomoharu Iwata, Takahide Hoshide, Noriko Takaya, Ko Fujimura
Venue: WSDM
Year: 2013

This paper proposes a method that analyzes the location log data of multiple users to recommend locations to be visited. The method uses our new topic model, called Geo Topic Model, that can jointly estimate both the user's interests and activity area hosting the user's home, office and other personal places. By explicitly modeling geographical features of locations and users, the user's interests in other features of locations, which we call latent topics, can be inferred effectively. The topic interests estimated by our model 1) lead to high accuracy in predicting visit behavior as driven by personal interests, 2) make possible the generation of recommendations when the user is in an unfamiliar area (e.g. sightseeing), and 3) enable the recommender system to suggest an interpretable representation of the user profile that can be customized by the user. Experiments are conducted using real location logs of landmark and restaurant visits to evaluate the recommendation performance of the proposed method in terms of the accuracy of predicting visit selections. We also show that our model can estimate latent features of locations such as art, nature and atmosphere as latent topics, and describe each user's preference based on them.
Building user profiles to improve user experience in recommender systems
Authors: Anísio Lacerda, Nivio Ziviani
Venue: WSDM
Year: 2013

Recommender systems are quickly becoming ubiquitous in many Web applications, including e-commerce, social media channels, content providers, among others. These systems act as an enabling mechanism designed to overcome the information overload problem by improving browsing and consumption experience. Crucial to the performance of a recommender system is the accuracy of the user profiles used to represent the interests of the users. In this proposal, we analyze three different aspects of user profiling: (i) selecting the most informative events from the interaction between users and the system, (ii) combining different recommendation algorithms to (iii) including trust-aware information in user profiles to improve the accuracy of recommender systems.
Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships
Authors: Yanhua Li, Wei Chen, Yajun Wang, Zhi-Li Zhang
Venue: WSDM
Year: 2013

Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied because of their impacts on enabling effective online viral marketing. Existing studies focus on social networks with only friendship relations, whereas the foe or enemy relations that commonly exist in many OSNs, e.g., Epinions and Slashdot, are completely ignored. In this paper, we make the first attempt to investigate the influence diffusion and influence maximization in OSNs with both friend and foe relations, which are modeled using positive and negative edges on signed networks. In particular, we extend the classic voter model to signed networks and analyze the dynamics of influence diffusion of two opposite opinions. We first provide systematic characterization of both short-term and long-term dynamics of influence diffusion in this model, and illustrate that the steady state behaviors of the dynamics depend on three types of graph structures, which we refer to as balanced graphs, anti-balanced graphs, and strictly unbalanced graphs. We then apply our results to solve the influence maximization problem and develop efficient algorithms to select initial seeds of one opinion that maximize either its short-term influence coverage or long-term steady state influence coverage. Extensive simulation results on both synthetic and real-world networks, such as Epinions and Slashdot, confirm our theoretical analysis on influence diffusion dynamics, and demonstrate that our influence maximization algorithms perform consistently better than other heuristic algorithms.
What's in a name?: an unsupervised approach to link users across communities
Authors: Jing Liu, Fan Zhang, Xinying Song, Young-In Song, Chin-Yew Lin, Hsiao-Wuen Hon
Venue: WSDM
Year: 2013

In this paper, we consider the problem of linking users across multiple online communities. Specifically, we focus on the alias-disambiguation step of this user linking task, which is meant to differentiate users with the same usernames. We start quantitatively analyzing the importance of the alias-disambiguation step by conducting a survey on 153 volunteers and an experimental analysis on a large dataset of About.me (75,472 users). The analysis shows that the alias-disambiguation solution can address a major part of the user linking problem in terms of the coverage of true pairwise decisions (46.8%). To the best of our knowledge, this is the first study on human behaviors with regards to the usages of online usernames. We then cast the alias-disambiguation step as a pairwise classification problem and propose a novel unsupervised approach. The key idea of our approach is to automatically label training instances based on two observations: (a) rare usernames are likely owned by a single natural person, e.g. pennystar88 as a positive instance; (b) common usernames are likely owned by different natural persons, e.g. tank as a negative instance. We propose using the n-gram probabilities of usernames to estimate the rareness or commonness of usernames. Moreover, these two observations are verified by using the dataset of Yahoo! Answers. The empirical evaluations on 53 forums verify: (a) the effectiveness of the classifiers with the automatically generated training data and (b) that the rareness and commonness of usernames can help user linking. We also analyze the cases where the classifiers fail.
TYPiMatch: type-specific unsupervised learning of keys and key values for heterogeneous web data integration
Authors: Yongtao Ma, Thanh Tran
Venue: WSDM
Year: 2013

Instance matching and blocking, a preprocessing step used for selecting candidate matches, require determining the most representative attributes of instances called keys, based on which similarities between instances are computed. We show that for the problem of learning blocking keys and key values, both generic techniques that do not exploit type information and supervised learning techniques optimized for one single predefined type of instances do not perform well on heterogeneous Web data capturing instances for which the predefined type is too general. That is, they actually belong to some subtypes that are not explicitly specified in the data. We propose an unsupervised approach for learning these subtypes and the subtype-specific blocking keys and key values. Compared to state-of-the-art supervised and unsupervised learning approaches that are optimized for one single type, our approach improves efficiency as well as result quality. In particular, we show that the proposed strategy of learning subtype-specific blocking keys and key values improves both blocking and instance matching results.
GOP primary season on twitter: "popular" political sentiment in social media
Authors: Yelena Mejova, Padmini Srinivasan, Bob Boynton
Venue: WSDM
Year: 2013

As mainstream news media and political campaigns start to pay attention to the political discourse online, a systematic analysis of political speech in social media becomes more critical. What exactly do people say on these sites, and how useful is this data in estimating political popularity? In this study we examine Twitter discussions surrounding seven US Republican politicians who were running for the US Presidential nomination in 2011. We show this largely negative rhetoric to be laced with sarcasm and humor and dominated by a small portion of users. Furthermore, we show that using out-of-the-box classification tools results in a poor performance, and instead develop a highly optimized multi-stage approach designed for general-purpose political sentiment classification. Finally, we compare the change in sentiment detected in our dataset before and after 19 Republican debates, concluding that, at least in this case, the Twitter political chatter is not indicative of national political polls.
Web usage mining for enhancing search-result delivery and helping users to find interesting web content
Authors: Ida Mele
Venue: WSDM
Year: 2013

Web usage mining is the application of data mining techniques to the data generated by the interactions of users with web servers. This kind of data, stored in server logs, represents a valuable source of information, which can be exploited to optimize the document-retrieval task, or to better understand, and thus, satisfy user needs.
Search and exploration of X-Rated information (SEXI 2013)
Authors: Vanessa Murdock, Charles L. A. Clarke, Jaap Kamps, Jussi Karlgren
Venue: WSDM
Year: 2013

Adult content is pervasive on the Web, has been a driving factor in the adoption of the Internet medium. It is responsible for a significant fraction of traffic and revenues, yet rarely attracts attention in research. We propose that the research questions surrounding adult content access behaviors are unique, and we believe interesting and valuable research in this area can be done ethically. The workshop on Search and Exploration of X-Rated Information (SEXI) addresses these issues for information access tasks related to adult content.