OpenRaiser/Intern-Atlas huggingface dataset:
An analysis of socware cascades in online social networks
Authors: Ting-Kai Huang, Md. Sazzadur Rahman, Harsha V. Madhyastha, Michalis Faloutsos
Venue: WWW
Year: 2013

Online social networks (OSNs) have become a popular new vector for distributing malware and spam, which we refer to as socware. Unlike email spam, which is sent by spammers directly to intended victims, socware cascades through OSNs as compromised users spread it to their friends. In this paper, we analyze data from the walls of roughly 3 million Facebook users over five months, with the goal of developing a better understanding of socware cascades.
Location-based insights from the social web
Authors: Yohei Ikawa, Maja Vukovic, Jakob Rogstadius, Akiko Murakami
Venue: WWW
Year: 2013

Citizens, news reporters, relief organizations, and governments are increasingly relying on the Social Web to report on and respond to disasters as they occur. The capability to rapidly react to important events, which can be identified from high-volume streams even when the sources are unknown, still requires precise localization of the events and verification of the reports. In this paper, we propose a framework for classifying location elements and a method for their extraction from Social Web data. We describe the framework in the context of existing Social Web systems used for disaster management. We present a new location-inferencing architecture and evaluate its performance with a data set from a real-world disaster.
Practical extraction of disaster-relevant information from social media
Authors: Muhammad Imran, Shady Elbassuoni, Carlos Castillo, Fernando Diaz, Patrick Meier
Venue: WWW
Year: 2013

During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general.
An effective general framework for localized content optimization
Authors: Yoshiyuki Inagaki, Jiang Bian, Yi Chang
Venue: WWW
Year: 2013

Local search services have been gaining interests from Web users who seek the information near certain geographical locations. Particularly, those users usually want to find interesting information about what is happening nearby. In this poster, we introduce the localized content optimization problem to provide Web users with authoritative, attractive and fresh information that are really interesting to people around the certain location. To address this problem, we propose a general learning framework and develop a variety of features. Our evaluations based on the data set from a commercial localized Web service demonstrate that our framework is highly effective at providing contents that are more relevant to users' localized information need.
Disasters response using social life networks
Authors: Ramesh C. Jain
Venue: WWW
Year: 2013

Connecting people to required resources efficiently, effectively and promptly is one of the most important challenges for our society. Disasters make it the challenge for life and death. During disasters many normal sources of information to assess situations as well as distributing vital information to individuals break down. Unfortunately, during disastrous situations, most current practices are forced to follow bureaucratic processes and procedures that may delay help in critical life and death moments. Social media brings together different media as well as modes of distribution - focused, narrowcast, and broadcast -- and has revolutionized communication among people. Mobile phones, equipped with myriads of sensors are bringing the next generation of social networks not only to connect people with other people, but also to connect people with other people and essential life resources based on the disaster situation and personal context. We believe that such Social Life Networks (SLN) may play very important role for solving some essential human problems, including providing vital help to people during disasters. We will present early design of such systems and use a few examples of such systems explored in our group during disasters. Focused Micro Blogs (FMBs) will be discussed as an alternative to less noisy and more direct versions of current microblogs, such as Tweets and Status Updates. An important part of our discussion will be to list challenges and opportunities in this area.
Topical organization of user comments and application to content recommendation
Authors: Vidit Jain, Esther Galbrun
Venue: WWW
Year: 2013

On a news website, an article may receive thousands of comments from its readers on a variety of topics. The usual display of these comments in a ranked list, e.g. by popularity, does not allow the user to follow discussions on a particular topic. Organizing them by semantic topics enables the user not only to selectively browse comments on a topic, but also to discover other significant topics of discussion in comments. This topical organization further allows to explicitly capture the immediate interests of the user even when she is not logged in. Here we use this information to recommend content that is relevant in the context of the comments being read by the user. We present an algorithm for building such a topical organization in a practical setting and study different recommendation schemes. In a pilot study, we observe these comments-to-article recommendations to be preferred over the standard article-to-article recommendations.
From health-persona to societal health
Authors: Ramesh C. Jain, Laleh Jalali, Mingming Fan
Venue: WWW
Year: 2013

In this position paper, we propose an approach for Web Observatories that builds on using social media, personal data, and sensors to build Persona for an individual, but also use this data and the concept of Focused Micro Blogs (FMB) for situation detection, helping individual using situation action rules, and finally gaining insights for obtaining insights about society. We demonstrate this in a concrete use case of fitness and health care related sensors for building health persona and using this for understanding societal health issues.
Structural-interaction link prediction in microblogs
Authors: Yantao Jia, Yuanzhuo Wang, Jingyuan Li, Kai Feng, Xueqi Cheng, Jianchen Li
Venue: WWW
Year: 2013

Link prediction in Microblogs by using unsupervised methods aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. In this work, we define the retweet similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments on the real world Twitter data show our model outperforms state-of-the-art methods.
TCRec: product recommendation via exploiting social-trust network and product category information
Authors: Yu Jiang, Jing Liu, Xi Zhang, Zechao Li, Hanqing Lu
Venue: WWW
Year: 2013

In this paper, we develop a novel product recommendation method called TCRec, which takes advantage of consumer rating history record, social-trust network and product category information simultaneously. Compared experiments are conducted on two real-world datasets and outstanding performance is achieved, which demonstrates the effectiveness of TCRec.
CSS browser selector plus: a JavaScript library to support cross-browser responsive design
Authors: Richard Duchatsch Johansen, Talita Cristina Pagani Britto, César Augusto Cusin
Venue: WWW
Year: 2013

Developing websites for multiples devices have been a rough task for the past ten years. Devices features - such as screen size, resolution, internet access, operating system, etc. - change frequently and new devices emerge every day. Since W3C introduced media queries in CSS3, it's possible to developed tailored interfaces for multiple devices using a single HTML document. The approach of Responsive Web Design has been used media queries as support for developing adaptive and flexible layouts, however, it's not supported in legacy browsers. In this paper, we present CSS Browser Selector Plus, a cross-browser alternative method using JavaScript to support CSS3 media queries for developing responsive web considering older browsers.
Participatory disease surveillance in Latin America
Authors: Michael A. Johansson, Oktawia Wojcik, Rumi Chunara, Mark Smolinski, John S. Brownstein
Venue: WWW
Year: 2013

Participatory disease surveillance systems are dynamic, sensitive, and accurate. They also offer an opportunity to directly connect the public to public health. Implementing them in Latin America requires targeting multiple acute febrile illnesses, designing a system that is appropriate and scalable, and developing local strategies for encouraging participation.
Resolving homonymy with correlation clustering in scholarly digital libraries
Authors: Jeongin Ju, Hosung Park, Sue B. Moon
Venue: WWW
Year: 2013

As scholarly data increases rapidly, scholarly digital libraries, supplying publication data through convenient online interfaces, become popular and important tools for researchers. Researchers use SDLs for various purposes, including searching the publications of an author, assessing one's impact by the citations, and identifying one's research topics. However, common names among authors cause difficulties in correctly identifying one's works among a large number of scholarly publications. Abbreviated first and middle names make it even harder to identify and distinguish authors with the same representation (i.e. spelling) of names. Several disambiguation methods have solved the problem under their own assumptions. The assumptions are usually that inputs such as the number of same-named authors, training sets, or rich and clear information about papers are given. Considering the size of scholarship records today and their inconsistent formats, we expect their assumptions be very hard to be met. We use common assumption that coauthors are likely to write more than one paper together and propose an unsupervised approach to group papers from the same author only using the most common information, author lists. We represent each paper as a point in an author name space, take dimension reduction to find author names shown frequently together in papers, and cluster papers with vector similarity measure well fitted for name disambiguation task. The main advantage of our approach is to use only coauthor information as input. We evaluate our method using publication records collected from DBLP, and show that our approach results in better disambiguation compared to other five clustering methods in terms of cluster purity and fragmentation.
Spatio-temporal dynamics of online memes: a study of geo-tagged tweets
Authors: Krishna Yeswanth Kamath, James Caverlee, Kyumin Lee, Zhiyuan Cheng
Venue: WWW
Year: 2013

We conduct a study of the spatio-temporal dynamics of Twitter hashtags through a sample of 2 billion geo-tagged tweets. In our analysis, we (i) examine the impact of location, time, and distance on the adoption of hashtags, which is important for understanding meme diffusion and information propagation; (ii) examine the spatial propagation of hashtags through their focus, entropy, and spread; and (iii) present two methods that leverage the spatio-temporal propagation of hashtags to characterize locations. Based on this study, we find that although hashtags are a global phenomenon, the physical distance between locations is a strong constraint on the adoption of hashtags, both in terms of the hashtags shared between locations and in the timing of when these hashtags are adopted. We find both spatial and temporal locality as most hashtags spread over small geographical areas but at high speeds. We also find that hashtags are mostly a local phenomenon with long-tailed life spans. These (and other) findings have important implications for a variety of systems and applications, including targeted advertising, location-based services, social media search, and content delivery networks.
Board coherence in Pinterest: non-visual aspects of a visual site
Authors: Krishna Yeswanth Kamath, Ana-Maria Popescu, James Caverlee
Venue: WWW
Year: 2013

Pinterest is a fast-growing interest network with significant user engagement and monetization potential. This paper explores quality signals for Pinterest boards, in particular the notion of board coherence. We find that coherence can be assessed with promising results and we explore its relation to quality signals based on social interaction.
OpenScout: harvesting business and management learning objects from the web of data
Authors: Ricardo Kawase, Marco Fisichella, Katja Niemann, Vassilis Pitsilis, Aristides Vidalis, Philipp Holtkamp, Bernardo Pereira Nunes
Venue: WWW
Year: 2013

Already existing open educational resources in the field of Business and Management have a high potential for enterprises to address the increasing training needs of their employees. However, it is difficult to act on OERs as some data is hidden. In the meanwhile, numerous repositories provide Linked Open Data on this field. Though, users have to search a number of repositories with heterogeneous interfaces in order to retrieve the desired content. In this paper, we present the strategies to gather heterogeneous learning objects from the Web of Data, and we provide an overview of the benefits of the OpenScout platform. Despite the fact that not all data repositories strictly follow Linked Data principles, OpenScout addressed individual variations in order to harvest, align, and provide a single end-point. In the end, OpenScout provides a full-fledged environment that leverages on the Linked Open Data available on the Web and additionally exposes it in an homogeneous format.
Search result presentation: supporting post-search navigation by integration of taxonomy data
Authors: Matthias Keller, Patrick Mühlschlegel, Hannes Hartenstein
Venue: WWW
Year: 2013

As a result of additional semantic annotations and novel mining methods, Web site taxonomies are more and more available to machines, including search engines. Recent research shows that after a search result is clicked, users often continue navigating on the destination site because in many cases a single document cannot satisfy the information need. The role Web site taxonomies play in this post-search navigation phase has not yet been researched. In this paper we analyze in an empirical study of three highly-frequented Web sites how Web site taxonomies influence the next browsing steps of users arriving from a search engine. The study reveals that users not randomly explore the destination site, but proceed to the direct child nodes of the landing page with significantly higher frequency compared to the other linked pages. We conclude that the common post-search navigation strategy in taxonomies is to descend towards more specific results. The study has interesting implications for the presentation of search results. Current search engines focus on summarizing the linked document only. In doing so, search engines ignore the fact the linked documents are in many cases just the starting point for further navigation. Based on the observed post-search navigation strategy, we propose to include information about child nodes of linked documents in the presentation of search results. Users would benefit by saving clicks, because they could not only estimate whether the linked document provides useful information, but also whether post-search navigation is promising.
Offering language based services on social media by identifying user's preferred language(s) from romanized text
Authors: Mitesh M. Khapra, Salil Joshi, Ananthakrishnan Ramanathan, Karthik Visweswariah
Venue: WWW
Year: 2013

With the increase of multilingual content and multilingual users on the web, it is prudent to offer personalized services and ads to users based on their language profile (\textit{i.e.}, the list of languages that a user is conversant with). Identifying the language profile of a user is often non-trivial because (i) users often do not specify all the languages known to them while signing up for an online service (ii) users of many languages (especially Indian languages) largely use Latin/Roman script to write content in their native language. This makes it non-trivial for a machine to distinguish the language of one comment from another. This situation presents an opportunity for offering following language based services for romanized content (i) hide romanized comments which belong to a language which is not known to the user (ii) translate romanized comments which belong to a language which is not known to the user (iii) transliterate romanized comments which belong to a language which is known to the user (iv) show language based ads by identifying languages known to a user based on the romanized comments that he wrote/read/liked. We first use a simple bootstrapping based semi-supervised algorithm for identify the language of a romanized comment. We then apply this algorithm to all the comments written/read/liked by a user to build a language profile of the user and propose that this profile can be used to offer the services mentioned above.
Accountable key infrastructure (AKI): a proposal for a public-key validation infrastructure
Authors: Tiffany Hyun-Jin Kim, Lin-Shung Huang, Adrian Perrig, Collin Jackson, Virgil D. Gligor
Venue: WWW
Year: 2013

Recent trends in public-key infrastructure research explore the tradeoff between decreased trust in Certificate Authorities (CAs), resilience against attacks, communication overhead (bandwidth and latency) for setting up an SSL/TLS connection, and availability with respect to verifiability of public key information. In this paper, we propose AKI as a new public-key validation infrastructure, to reduce the level of trust in CAs. AKI integrates an architecture for key revocation of all entities (e.g., CAs, domains) with an architecture for accountability of all infrastructure parties through checks-and-balances. AKI efficiently handles common certification operations, and gracefully handles catastrophic events such as domain key loss or compromise. We propose AKI to make progress towards a public-key validation infrastructure with key revocation that reduces trust in any single entity.
Optimizing RDF(S) queries on cloud platforms
Authors: HyeongSik Kim, Padmashree Ravindra, Kemafor Anyanwu
Venue: WWW
Year: 2013

Scalable processing of Semantic Web queries has become a critical need given the rapid upward trend in availability of Semantic Web data. The MapReduce paradigm is emerging as a platform of choice for large scale data processing and analytics due to its ease of use, cost effectiveness, and potential for unlimited scaling. Processing queries on Semantic Web triple models is a challenge on the mainstream MapReduce platform called Apache Hadoop, and its extensions such as Pig and Hive. This is because such queries require numerous joins which leads to lengthy and expensive MapReduce workflows. Further, in this paradigm, cloud resources are acquired on demand and the traditional join optimization machinery such as statistics and indexes are often absent or not easily supported.
Weaving a safe web of news
Authors: Kanak Biscuitwala, Willem Bult, Mathias Lécuyer, T. J. Purtell, Madeline K. B. Ross, Augustin Chaintreau, Chris Haseman, Monica S. Lam, Susan E. McGregor
Venue: WWW
Year: 2013

The rise of social media and data-capable mobile devices in recent years has transformed the face of global journalism, supplanting the broadcast news anchor with a new source for breaking news: the citizen reporter. Social media's decentralized networks and instant re-broadcasting mechanisms mean that the reach of a single tweet can easily trump that of the most powerful broadcast satellite. Brief, text-based and easy to translate, social messages allow news audiences to skip the middleman and get news "straight from the source."
Context mining and integration into predictive web analytics
Authors: Julia Kiseleva
Venue: WWW
Year: 2013

Predictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ubiquitous and mobile computing, and computational advertising. Within these applications business decisions often rely on two types of predictions: an overall or particular user segment demand predictions and individualised recommendations for visitors. Visitor behaviour is inherently sensitive to the context, which can be defined as a collection of external factors. Context-awareness allows integrating external explanatory information into the learning process and adapting user behaviour accordingly. The importance of context-awareness has been recognised by researchers and practitioners in many disciplines, including recommendation systems, information retrieval, personalisation, data mining, and marketing. We focus on studying ways of context discovery and its integration into predictive analytics.
Discovering temporal hidden contexts in web sessions for user trail prediction
Authors: Julia Kiseleva, Hoang Thanh Lam, Mykola Pechenizkiy, Toon Calders
Venue: WWW
Year: 2013

In many web information systems such as e-shops and information portals, predictive modeling is used to understand user's intentions based on their browsing behaviour. User behavior is inherently sensitive to various hidden contexts. It has been shown in different experimental studies that exploitation of contextual information can help in improving prediction performance significantly. It is reasonable to assume that users may change their intents during one web session and that changes are influenced by some external factors such as switch in temporal context e.g. 'users want to find information about a specific product' and after a while 'they want to buy this product'. A web session can be represented as a sequence of user's actions where actions are ordered by time. The generation of a web session might be influenced by several hidden temporal contexts. Each session can be represented as a concatenation of independent segments, each of which is influenced by one corresponding context. We show how to learn how to apply different predictive models for each segment in this work. We define the problem of discovering temporal hidden contexts in such way that we optimize directly the accuracy of predictive models (e.g. users' trails prediction) during the process of context acquisition. Our empirical study on a real dataset demonstrates the effectiveness of our method.
"the crowd keeps me in shape": social psychology and the present and future of health social machines
Authors: Max Van Kleek, Daniel A. Smith, Wendy Hall, Nigel Shadbolt
Venue: WWW
Year: 2013

Can the Web help people live healthier lives? This paper seeks to answer this question through an examination of sites, apps and online communities designed to help people improve their fitness, better manage their disease(s) and conditions, and to solve the often elusive connections between the symptoms they experience, diseases and treatments. These health social machines employ a combination of both simple and complex social and computational processes to provide such support. We first provide a descriptive classification of the kinds of machines currently available, and the support each class offers. We then describe the limitations exhibited by these systems and potential ways around them, towards the design of more effective machines in the future.