OpenRaiser/Intern-Atlas huggingface dataset:
Large scale microblog mining using distributed MB-LDA
Authors: Chenyi Zhang, Jianling Sun
Venue: WWW
Year: 2012

In the information explosion era, large scale data processing and mining is a hot issue. As microblog grows more popular, microblog services have become information provider on a web scale, so researches on microblog begin to focus more on its content mining than solely user's relationship analysis before. Although traditional text mining methods have been studied well, no algorithm is designed specially for microblog data, which contain structured information on social network besides plain text. In this paper, we introduce a novel probabilistic generative model MicroBlog-Latent Dirichlet Allocation (MB-LDA), which takes both contactor relevance relation and document relevance relation into consideration to improve topic mining in microblogs. Through Gibbs sampling for approximate inference of our model, MB-LDA can discover not only the topics of microblogs, but also the topics focused by contactors. When faced with large datasets, traditional techniques on single node become less practical within limited resources. So we present distributed MB-LDA in MapReduce framework in order to process large scale microblogs with high scalability. Furthermore, we apply a performance model to optimize the execution time by tuning the number of mappers and reducers. Experimental results on actual dataset show MB-LDA outperforms the baseline of LDA and distributed MB-LDA offers an effective solution to topic mining for large scale microblogs.
Exploiting shopping and reviewing behavior to re-score online evaluations
Authors: Rong Zhang, Chaofeng Sha, Minqi Zhou, Aoying Zhou
Venue: WWW
Year: 2012

Analysis to product reviews has attracted great attention from both academia and industry. Generally the evaluation scores of reviews are used to generate the average scores of products and shops for future potential users. However, in the real world, there is the inconsistency problem between the evaluation scores and review content, and some customers do not give out fair reviews. In this work, we focus on detecting the credibility of customers by analyzing online shopping and review behaviors, and then we re-score the reviews for products and shops. In the end, we evaluate our algorithm based on the real data set from Taobao, the biggest E-commerce site in China.
Online modeling of proactive moderation system for auction fraud detection
Authors: Liang Zhang, Jie Yang, Belle L. Tseng
Venue: WWW
Year: 2012

We consider the problem of building online machine-learned models for detecting auction frauds in e-commence web sites. Since the emergence of the world wide web, online shopping and online auction have gained more and more popularity. While people are enjoying the benefits from online trading, criminals are also taking advantages to conduct fraudulent activities against honest parties to obtain illegal profit. Hence proactive fraud-detection moderation systems are commonly applied in practice to detect and prevent such illegal and fraud activities. Machine-learned models, especially those that are learned online, are able to catch frauds more efficiently and quickly than human-tuned rule-based systems. In this paper, we propose an online probit model framework which takes online feature selection, coefficient bounds from human knowledge and multiple instance learning into account simultaneously. By empirical experiments on a real-world online auction fraud detection data we show that this model can potentially detect more frauds and significantly reduce customer complaints compared to several baseline models and the human-tuned rule-based system.
Data extraction from web pages based on structural-semantic entropy
Authors: Xiaoqing Zheng, Yiling Gu, Yinsheng Li
Venue: WWW
Year: 2012

Most of today's web content is designed for human consumption, which makes it difficult for software tools to access them readily. Even web content that is automatically generated from back-end databases is usually presented without the original structural information. In this paper, we present an automated information extraction algorithm that can extract the relevant attribute-value pairs from product descriptions across different sites. A notion, called structural-semantic entropy, is used to locate the data of interest on web pages, which measures the density of occurrence of relevant information on the DOM tree representation of web pages. Our approach is less labor-intensive and insensitive to changes in web-page format. Experimental results on a large number of real-life web page collections are encouraging and confirm the feasibility of the approach, which has been successfully applied to detect false drug advertisements on the web due to its capacity in associating the attributes of records with their respective values.
Exploiting user profile information for answer ranking in cQA
Authors: Zhi-Min Zhou, Man Lan, Zheng-Yu Niu, Yue Lu
Venue: WWW
Year: 2012

Answer ranking is very important for cQA services due to the high variance in the quality of answers. Most existing works in this area focus on using various features or employing machine learning techniques to address this problem. Only a few of them noticed and involved user profile information in this particular task. In this work, we assume the close relationship between user profile information and the quality of their answers under the ground truth that user information records the user behaviors and histories as a summary. Thus, we exploited the effectiveness of three categories of user profile information, i.e. engagement-related, authority-related and level-related, on answer ranking in cQA. Different from previous work, we only employed the information which is easy to extract without any limitations, such as user privacy. Experimental results on Yahoo! Answers manner questions showed that our system by using the user profile information achieved comparable or even better results over the state-of-the-art baseline system. Moreover, we found that the picture existence of a user in cQA community contributed more than other information in the answer ranking task.
Cost-effective node monitoring for online hot eventdetection in sina weibo microblogging
Authors: Kai Chen, Yi Zhou, Hongyuan Zha, Jianhua He, Pei Shen, Xiaokang Yang
Venue: WWW
Year: 2013

We propose a cost-effective hot event detection system over Sina Weibo platform, currently the dominant microblogging service provider in China. The problem of finding a proper subset of microbloggers under resource constraints is formulated as a mixed-integer problem for which heuristic algorithms are developed to compute approximate solution. Preliminary results show that by tracking about 500 out of 1.6 million candidate microbloggers and processing 15,000 microposts daily, 62% of the hot events can be detected five hours on average earlier than they are published by Weibo.
Animated CAPTCHAs and games for advertising
Authors: Suhas Aggarwal
Venue: WWW
Year: 2013

In this paper, we discuss animated captcha systems which can be very useful for advertising. They are hardly any Animated Advertisement Captchas, available these days which are more secure than single image based Captchas and more fun as well. Some solutions are available such as yo!Captcha, NLPcaptcha. Solve Media TYPE-IN Captchas. These Captchas are single image based Captchas which ask users to type in Brand message to solve Captcha for brand recall. In this paper, we discuss some more appealing media which can be used for Captcha advertising. We also present Interactive Environment/Game Captcha which provide a more powerful medium for advertising. Finally, we showcase a Game with a purpose, named 'Pick Brands' which promote advertising and and can be used to obtain feedback/reviews, collect user questions concerning products/Advertisements.
Detection of spam tipping behaviour on foursquare
Authors: Anupama Aggarwal, Jussara M. Almeida, Ponnurangam Kumaraguru
Venue: WWW
Year: 2013

In Foursquare, one of the currently most popular online location based social networking sites (LBSNs), users may not only check-in at specific venues but also post comments (or tips), sharing their opinions and previous experiences at the corresponding physical places. Foursquare tips, which are visible to everyone, provide venue owners with valuable user feedback besides helping other users to make an opinion about the specific venue. However, they have been the target of spamming activity by users who exploit this feature to spread tips with unrelated content.
Towards a development process for geospatial information retrieval and search
Authors: Dirk Ahlers
Venue: WWW
Year: 2013

Geospatial search as a special type of vertical search has specific requirements and challenges. While the general principle of resource discovery, extraction, indexing, and search holds, geospatial search systems are tailored to the specific use case at hand with many individual adaptations. In this short overview, we aim to collect and organize the main organizing principles for the multitude of challenges and adaptations to be considered within the development process to work towards a more formal description.
A CRM system for social media: challenges and experiences
Authors: Jitendra Ajmera, Hyung-Il Ahn, Meena Nagarajan, Ashish Verma, Danish Contractor, Stephen Dill, Matthew Denesuk
Venue: WWW
Year: 2013

The social Customer Relationship Management (CRM) landscape is attracting significant attention from customers and enterprises alike as a sustainable channel for tracking, managing and improving customer relations. Enterprises are taking a hard look at this open, unmediated platform because the community effect generated on this channel can have a telling effect on their brand image, potential market opportunity and customer loyalty. In this work we present our experiences in building a system that mines conversations on social platforms to identify and prioritize those posts and messages that are relevant to enterprises. The system presented in this work aims to empower an agent or a representative in an enterprise to monitor, track and respond to customer communication while also encouraging community participation.
Here's my cert, so trust me, maybe?: understanding TLS errors on the web
Authors: Devdatta Akhawe, B. Amann, Matthias Vallentin, Robin Sommer
Venue: WWW
Year: 2013

When browsers report TLS errors, they cannot distinguish between attacks and harmless server misconfigurations; hence they leave it to the user to decide whether continuing is safe. However, actual attacks remain rare. As a result, users quickly become used to "false positives" that deplete their attention span, making it unlikely that they will pay sufficient scrutiny when a real attack comes along. Consequently, browser vendors should aim to minimize the number of low-risk warnings they report. To guide that process, we perform a large-scale measurement study of common TLS warnings. Using a set of passive network monitors located at different sites, we identify the prevalence of warnings for a total population of about 300,000 users over a nine-month period. We identify low-risk scenarios that consume a large chunk of the user attention budget and make concrete recommendations to browser vendors that will help maintain user attention in high-risk situations. We study the impact on end users with a data set much larger in scale than the data sets used in previous TLS measurement studies. A key novelty of our approach involves the use of internal browser code instead of generic TLS libraries for analysis, providing more accurate and representative results.
Attributing authorship of revisioned content
Authors: Luca de Alfaro, Michael Shavlovsky
Venue: WWW
Year: 2013

A considerable portion of web content, from wikis to collaboratively edited documents, to code posted online, is revisioned. We consider the problem of attributing authorship to such revisioned content, and we develop scalable attribution algorithms that can be applied to very large bodies of revisioned content, such as the English Wikipedia.
Exploring very large data sets from online social networks
Authors: Virgílio A. F. Almeida
Venue: WWW
Year: 2013

The explosion in the volume of digital data currently available in social networks has created new opportunities for scientific discoveries in the realm of social media. In particular, I show our recent progress in user preference understanding, data mining, summarization and explorative analysis of very large data sets. In information networks where users send messages to one another, the issue of information overload naturally arises: which are the most important messages? Based on a very large dataset with more 54 million user accounts and with all tweets ever posted by the collected users - more than 1.7 billion tweets, I discuss the problem of understanding the importance of messages in Twitter.
Stuff happens continuously: exploring web contents with temporal information
Authors: Omar Alonso
Venue: WWW
Year: 2013

In the last few years there has been an increased interest from researchers and practitioners in exploring time as a dimension that can benefit several information retrieval tasks. There is exciting work in analyzing and exploiting temporal information embedded in documents as relevance cues for the presentation, organization, and the exploration of search results in a temporal context.
Archival HTTP redirection retrieval policies
Authors: Ahmed Alsum, Michael L. Nelson, Robert Sanderson, Herbert Van de Sompel
Venue: WWW
Year: 2013

When retrieving archived copies of web resources (mementos) from web archives, the original resource's URI-R is typically used as the lookup key in the web archive. This is straightforward until the resource on the live web issues a redirect: R ->R`. Then it is not clear if R or R` should be used as the lookup key to the web archive. In this paper, we report on a quantitative study to evaluate a set of policies to help the client discover the correct memento when faced with redirection. We studied the stability of 10,000 resources and found that 48% of the sample URIs tested were not stable, with respect to their status and redirection location. 27% of the resources were not perfectly reliable in terms of the number of mementos of successful responses over the total number of mementos, and 2% had a reliability score of less than 0.5. We tested two retrieval policies. The first policy covered the resources which currently issue redirects and successfully resolved 17 out of 77 URIs that did not have mementos of the original URI, but did of the resource that was being redirected to. The second policy covered archived copies with HTTP redirection and helped the client in 58% of the cases tested to discover the nearest memento to the requested datetime.
The role of research leaders on the evolution of scientific communities
Authors: Bruno Leite Alves, Fabrício Benevenuto, Alberto H. F. Laender
Venue: WWW
Year: 2013

There have been considerable efforts in the literature towards understanding and modeling dynamic aspects of scientific communities. Despite the great interest, little is known about the role that different members play in the formation of the underlying network structure of such communities. In this paper, we provide a wide investigation of the roles that members of the core of scientific communities play in the collaboration network structure formation and evolution. To do that, we define a community core based on an individual metric, core score, which is an h-index derived metric that captures both, the prolificness and the involvement of researchers in a community. Our results provide a number of key observations related to community formation and evolving patterns. Particularly, we show that members of the community core work as bridges that connect smaller clustered research groups. Furthermore, these members are responsible for an increase in the average degree of the whole community underlying network and a decrease on the overall network assortativeness. More important, we note that variations on the members of the community core tend to be strongly correlated with variations on these metrics. We argue that our observations are important for shedding a light on the role of key members on community formation and structure.
Linking inside a video collection: what and how to measure?
Authors: Robin Aly, Roeland Ordelman, Maria Eskevich, Gareth J. F. Jones, Shu Chen
Venue: WWW
Year: 2013

Although linking video to additional information sources seems to be a sensible approach to satisfy information needs of user, the perspective of users is not yet analyzed on a fundamental level in real-life scenarios. However, a better understanding of the motivation of users to follow links in video, which anchors users prefer to link from within a video, and what type of link targets users are typically interested in, is important to be able to model automatic linking of audiovisual content appropriately. In this paper we report on our methodology towards eliciting user requirements with respect to video linking in the course of a broader study on user requirements in searching and a series of benchmark evaluations on searching and linking.
The anatomy of LDNS clusters: findings and implications for web content delivery
Authors: Hussein A. Alzoubi, Michael Rabinovich, Oliver Spatscheck
Venue: WWW
Year: 2013

We present a large-scale measurement of clusters of hosts sharing the same local DNS servers. We analyze properties of these "LDNS clusters" from the perspective of content delivery networks, which commonly use DNS for load distribution. We found that not only LDNS clusters differ widely in terms of their size and geographical compactness but that the largest clusters are actually extremely compact. This suggests potential benefits of a load distribution strategy with nuanced treatment of different LDNS clusters based on the combination of their size and compactness. We further observed interesting variations in LDNS setups including a wide use of "LDNS pools" (which as we explain in the paper are different from setups where end-hosts simply utilize multiple resolvers).
Traffic quality based pricing in paid search using two-stage regression
Authors: Rouben Amirbekian, Ye Chen, Alan Lu, Tak W. Yan, Liangzhong Yin
Venue: WWW
Year: 2013

While the cost-per-click (CPC) pricing model is main stream in sponsored search, the quality of clicks with respect to conversion rates and hence their values to advertisers may vary considerably from publisher to publisher in a large syndication network. Traffic quality shall be used to establish price discounts for clicks from different publishers. These discounts are intended to maintain incentives for high-quality online traffic and to make it easier for advertisers to maintain long-term bid stability. Conversion signal is noisy as each advertiser defines conversion in their own way. It is also very sparse. Traditional way of overcoming signal sparseness is to allow for longer time in accumulating modeling data. However, due to fast-changing conversion trends, such longer time leads to deterioration of the precision in measuring quality. To allow models to adjust to fast-changing trends with sufficient speed, we had to limit time-window for conversion data collection and make it much shorter than the several weeks window commonly used. Such shorter time makes conversions in the training set extremely sparse. To overcome resulting obstacles, we used two-stage regression similar to hurdle regression. First we employed logistic regression to predict zero conversion outcomes. Next, conditioned on non-zero outcomes, we used random forest regression to predict the value of the quotient of two conversion rates. Two-stage model accounts for the zero inflation due to the sparseness of the conversion signal. The combined model maintains good precision and allows faster reaction to the temporal changes in traffic quality including changes due to certain actions by publishers that may lead to click-price inflation.
Fragmented social media: a look into selective exposure to political news
Authors: Jisun An, Daniele Quercia, Jon Crowcroft
Venue: WWW
Year: 2013

The hypothesis of selective exposure assumes that people crave like-minded information and eschew information that conflicts with their beliefs, and that has negative consequences on political life. Yet, despite decades of research, this hypothesis remains theoretically promising but empirically difficult to test. We look into news articles shared on Facebook and examine whether selective exposure exists or not in social media. We find a concrete evidence for a tendency that users predominantly share like-minded news articles and avoid conflicting ones, and partisans are more likely to do that. Building tools to counter partisanship on social media would require the ability to identify partisan users first. We will show that those users cannot be distinguished from the average user as the two subgroups do not show any demographic difference.
Visually extracting data records from the deep web
Authors: Neil Anderson, Jun Hong
Venue: WWW
Year: 2013

Web sites that rely on databases for their content are now ubiquitous. Query result pages are dynamically generated from these databases in response to user-submitted queries. Automatically extracting structured data from query result pages is a challenging problem, as the structure of the data is not explicitly represented. While humans have shown good intuition in visually understanding data records on a query result page as displayed by a web browser, no existing approach to data record extraction has made full use of this intuition. We propose a novel approach, in which we make use of the common sources of evidence that humans use to understand data records on a displayed query result page. These include structural regularity, and visual and content similarity between data records displayed on a query result page. Based on these observations we propose new techniques that can identify each data record individually, while ignoring noise items, such as navigation bars and adverts. We have implemented these techniques in a software prototype, rExtractor, and tested it using two datasets. Our experimental results show that our approach achieves significantly higher accuracy than previous approaches. Furthermore, it establishes the case for use of vision-based algorithms in the context of data extraction from web sites.
Measuring web quality
Authors: Ricardo Baeza-Yates
Venue: WWW
Year: 2013

Measuring the quality of web content, either at page level or website level, is at the heart of several key challenges in the Web. Without doubt, the main one is web search, to be able to rank results. However, there are other important problems such as web reputation or trust, and web spam detection and filtering. However, measuring intrinsic web quality is a hard problem, because of our limited (automatic) understanding of text semantics, which is even worse for other media. Hence, similarly to human trust assessing, where we use past actions, face expressions, body language, etc; in the Web we need to use indirect signals that serve as surrogates for web quality. In this keynote we attempt to present the most important signals as well as new signals that are or can be used to measure quality in the Web. We divide them using the traditional web content, structure, and usage trilogy. We also characterize them according to how easy is to measure these signals, who can measure them, and how well they scale to the whole Web.