OpenRaiser/Intern-Atlas huggingface dataset:
Modeling/predicting the evolution trend of osn-based applications
Authors: Han Liu, Atif Nazir, Jinoo Joung, Chen-Nee Chuah
Venue: WWW
Year: 2013

While various models have been proposed for generating social/friendship network graphs, the dynamics of user interactions through online social network (OSN) based applications remain largely unexplored. We previously developed a growth model to capture static weekly snapshots of user activity graphs (UAGs) using data from popular Facebook gifting applications. This paper presents a new continuous graph evolution model aimed to capture microscopic user-level behaviors that govern the growth of the UAG and collectively define the overall graph structure. We demonstrate the utility of our model by applying it to forecast the number of active users over time as the application transitions from initial growth to peak/mature and decline/fatique phase. Using empirical evaluations, we show that our model can accurately reproduce the evolution trend of active user population for gifting applications, or other OSN applications that employ similar growth mechanisms. We also demonstrate that the predictions from our model can guide the generation of synthetic graphs that accurately represent empirical UAG snapshots sampled at different evolution stages.
Defending imitating attacks in web credibility evaluation systems
Authors: Xin Liu, Radoslaw Nielek, Adam Wierzbicki, Karl Aberer
Venue: WWW
Year: 2013

Unlike traditional media such as television and newspapers, web contents are relatively easy to be published without being rigorously fact-checked. This seriously influences people's daily life if non-credible web contents are utilized for decision making. Recently, web credibility evaluation systems have emerged where web credibility is derived by aggregating ratings from the community (e.g., MyWOT). In this paper, We focus on the robustness of such systems by identifying a new type of attack scenario where an attacker imitates the behavior of trustworthy experts by copying system's credibility ratings to quickly build high reputation and then attack certain web contents. In order to defend this attack, we propose a two-stage defence algorithm. At stage 1, our algorithm applies supervised learning algorithm to predict the credibility of a web content and compare it with a user's rating to estimate whether this user is malicious or not. In case the user's maliciousness can not be determined with high confidence, the algorithm goes to stage 2 where we investigate users' past rating patterns and detect the malicious one by applying hierarchical clustering algorithm. Evaluation using real datasets demonstrates the efficacy of our approach.
Information current in Twitter: which brings hot events to the world
Authors: Peilei Liu, Jintao Tang, Ting Wang
Venue: WWW
Year: 2013

In this paper we investigate information propagation in Twitter from the geographical view on the global scale. An information propagation phenomenon what we call "information current" has been discovered. According to this phenomenon, we propose a hypothesis that changes of information flows may be related to real-time events. Through analysis of retweets, we show that our hypothesis is supported by experiment results. Moreover, it is discovered that the retweet texts are more effective than common tweet texts for real-time event detection. This means that Twitter could be a good filter of texts for event detection.
Co-training and visualizing sentiment evolvement for tweet events
Authors: Shenghua Liu, Wenjun Zhu, Ning Xu, Fangtao Li, Xueqi Cheng, Yue Liu, Yuanzhuo Wang
Venue: WWW
Year: 2013

Sentiment classification on tweet events attracts more interest in recent years. The large tweet stream stops people reading the whole classified list to understand the insights. We employ the co-training framework in the proposed algorithm. Features are split into text view features and non-text view features. Two Random Forest (RF) classifiers are trained with the common labeled data on the two views of features separately. Then for each specific event, they collaboratively and periodically train together to boost the classification performance. At last, we propose a "river" graph to visualize the intensity and evolvement of sentiment on an event, which demonstrates the intensity by both color gradient and opinion labels, and the ups and downs of confronting opinions by the river flow. Comparing with the well-known sentiment classifiers, our algorithm achieves consistent increases in accuracy on the tweet events from TREC 2011 Microblogging and our database. The visualization helps people recognize turning and bursting patterns, and predict sentiment trend in an intuitive way.
Imagen: runtime migration of browser sessions for javascript web applications
Authors: James Teng Kin Lo, Eric Wohlstadter, Ali Mesbah
Venue: WWW
Year: 2013

Due to the increasing complexity of web applications and emerging HTML5 standards, a large amount of runtime state is created and managed in the user's browser. While such complexity is desirable for user experience, it makes it hard for developers to implement mechanisms that provide users ubiquitous access to the data they create during application use. This paper presents our research into browser session migration for JavaScript-based web applications. Session migration is the act of transferring a session between browsers at runtime. Without burden to developers, our system allows users to create a snapshot image that captures all runtime state needed to resume the session elsewhere. Our system works completely in the JavaScript layer and thus snapshots can be transfered between different browser vendors and hardware devices. We report on performance metrics of the system using five applications, four different browsers, and three different devices.
Live migration of JavaScript web apps
Authors: James Teng Kin Lo, Eric Wohlstadter, Ali Mesbah
Venue: WWW
Year: 2013

Due to the increasing complexity of web applications and emerging HTML5 standards, a large amount of runtime state is created and managed in the user's browser. While such complexity is desirable for user experience, it makes it hard for developers to implement mechanisms that provide users ubiquitous access to the data they create during application use. This work showcases Imagen, our implemented platform for browser session migration of JavaScript-based web applications. Session migration is the act of transferring a session between browsers at runtime. Without burden to developers, Imagen allows users to create a snapshot image that captures the runtime state needed to resume the session elsewhere. Our approach works completely in the JavaScript layer and we demonstrate that snapshots can be transferred between different browser vendors and hardware devices. The demo will illustrate our system's performance and interoperability using two HTML5 apps, four different browsers and three different devices.
Gender swapping and user behaviors in online social games
Authors: Jing-Kai Lou, Kunwoo Park, Meeyoung Cha, Juyong Park, Chin-Laung Lei, Kuan-Ta Chen
Venue: WWW
Year: 2013

Modern Massively Multiplayer Online Role-Playing Games (MMORPGs) provide lifelike virtual environments in which play-ers can conduct a variety of activities including combat, trade, and chat with other players. While the game world and the available ac-tions therein are inspired by their offline counterparts, the games’ popularity and dedicated fan base are testaments to the allure of novel social interactions granted to people by allowing them an alternative life as a new character and persona. In this paper we investigate the phenomenon of “gender swapping, ” which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online, a globally serviced MMORPG, during social interactions when playing as in-game avatars of their own real gender or gender-swapped. We also discuss the effect of gender role and self-image in virtual social situations and the potential of our study for improv-ing MMORPG quality and detecting online identity frauds.
Mining structural hole spanners through information diffusion in social networks
Authors: Tiancheng Lou, Jie Tang
Venue: WWW
Year: 2013

The theory of structural holes suggests that individuals would benefit from filling the "holes" (called as structural hole spanners) between people or groups that are otherwise disconnected. A few empirical studies have verified that structural hole spanners play a key role in the information diffusion. However, there is still lack of a principled methodology to detect structural hole spanners from a given social network.
Two years of short URLs internet measurement: security threats and countermeasures
Authors: Federico Maggi, Alessandro Frossi, Stefano Zanero, Gianluca Stringhini, Brett Stone-Gross, Christopher Kruegel, Giovanni Vigna
Venue: WWW
Year: 2013

URL shortening services have become extremely popular. However, it is still unclear whether they are an effective and reliable tool that can be leveraged to hide malicious URLs, and to what extent these abuses can impact the end users. With these questions in mind, we first analyzed existing countermeasures adopted by popular shortening services. Surprisingly, we found such countermeasures to be ineffective and trivial to bypass. This first measurement motivated us to proceed further with a large-scale collection of the HTTP interactions that originate when web users access live pages that contain short URLs. To this end, we monitored 622 distinct URL shortening services between March 2010 and April 2012, and collected 24,953,881 distinct short URLs. With this large dataset, we studied the abuse of short URLs. Despite short URLs are a significant, new security risk, in accordance with the reports resulting from the observation of the overall phishing and spamming activity, we found that only a relatively small fraction of users ever encountered malicious short URLs. Interestingly, during the second year of measurement, we noticed an increased percentage of short URLs being abused for drive-by download campaigns and a decreased percentage of short URLs being abused for spam campaigns. In addition to these security-related findings, our unique monitoring infrastructure and large dataset allowed us to complement previous research on short URLs and analyze these web services from the user's perspective.
Crowdsourced risk factors of influenza-like-illness in Mexico
Authors: Natalia Barbara Mantilla-Beniers, Rocio Rodríguez-Ramírez, Christopher R. Stephens
Venue: WWW
Year: 2013

Monitoring of influenza like illnesses (ILI) using the Internet has become more common since its beginnings nearly a decade ago. The initial project of Der Grote Griep Meting was launched in 2003 in the Netherlands and Belgium. It was designed as a means of engaging people in matters of scientific and public health importance, and indeed attracted participation from over 30,000 people in its first year. Its success thus gathered a wealth of potentially valuable epidemiological data complementary to those obtained through the established disease surveillance networks, and linked to rich background information on each participant. Since then, there has been an accelerated increase in the number of countries hosting similar websites, and many of these have generated rather promising results
Improving consensus clustering of texts using interactive feature selection
Authors: Ricardo M. Marcacini, Marcos Aurélio Domingues, Solange Oliveira Rezende
Venue: WWW
Year: 2013

Consensus clustering and interactive feature selection are very useful methods to extract and manage knowledge from texts. While consensus clustering allows the aggregation of different clustering solutions into a single robust clustering solution, the interactive feature selection facilitates the incorporation of the users experience in text clustering tasks by selecting a set of high-level features. In this paper, we propose an approach to improve the robustness of consensus clustering using interactive feature selection. We have reported some experimental results on real-world datasets that show the effectiveness of our approach.
Saving, reusing, and remixing web video: using attitudes and practices to reveal social norms
Authors: Catherine C. Marshall, Frank M. Shipman III
Venue: WWW
Year: 2013

The growth of online videos has spurred a concomitant increase in the storage, reuse, and remix of this content. As we gain more experience with video content, social norms about ownership have evolved accordingly, spelling out what people think is appropriate use of content that is not necessarily their own. We use a series of three studies, each centering on a different genre of recordings, to probe 634 participants' attitudes toward video storage, reuse, and remix; we also question participants about their own experiences with online video. The results allow us to characterize current practice and emerging social norms and to establish the relationship between the two. Hypotheticals borrowed from legal research are used as the primary vehicle for testing attitudes, and for identifying boundaries between socially acceptable and unacceptable behavior.
Design and prototyping of a social media observatory
Authors: Karissa McKelvey, Filippo Menczer
Venue: WWW
Year: 2013

The broad adoption of online social networking platforms has made it possible to study communication networks at an unprecedented scale. With social media and micro-blogging platforms such as Twitter, we can observe high-volume data streams of online discourse. However, it is a challenge to collect, manage, analyze, visualize, and deliver large amounts of data, even by experts in the computational sciences. In this paper, we describe our recent extensions to Truthy, a social media observatory that collects and analyzes discourse on Twitter dating from August 2010. We introduce several interactive visualizations and analytical tools with the goal of enabling researchers to study online social networks with mixed methods at multiple scales. We present design considerations and a prototype for integrating social media observatories as important components of a web observatory framework.
DataConf: enriching conference publications with a mobile mashup application
Authors: Lionel Médini, Florian Bâcle, Hoang Duy Tan Nguyen
Venue: WWW
Year: 2013

This paper describes a mobile Web application that allows browsing conference publications, their authors, authors' organizations, and even authors' other publications or publications related to the same keywords. It queries a main SPARQL endpoint that serves the conference metadata set, as well as other endpoints to enrich and explore data. It provides extra functions, such as flashing a publication QR code from the Web browser, accessing external resources about the publications, and it can be linked to external Web services. This application exploits the Linked Data paradigm and performs client-side reasoning. It follows recent W3C technical advances and as a mashup, requires few server resources. It can easily be deployed for any conference with available metadata on the Web.
Searching for interestingness in Wikipedia and Yahoo!: answers
Authors: Yelena Mejova, Ilaria Bordino, Mounia Lalmas, Aristides Gionis
Venue: WWW
Year: 2013

In many cases, when browsing the Web, users are searching for specific information. Sometimes, though, users are also looking for something interesting, surprising, or entertaining. Serendipitous search puts interestingness on par with relevance. We investigate how interesting are the results one can obtain via serendipitous search, and what makes them so, by comparing entity networks extracted from two prominent social media sites, Wikipedia and Yahoo! Answers.
The self-feeding process: a unifying model for communication dynamics in the web
Authors: Pedro Olmo Stancioli Vaz de Melo, Christos Faloutsos, Renato M. Assunção, Antonio Alfredo Ferreira Loureiro
Venue: WWW
Year: 2013

How often do individuals perform a given communication activity in the Web, such as posting comments on blogs or news? Could we have a generative model to create communication events with realistic inter-event time distributions (IEDs)? Which properties should we strive to match? Current literature has seemingly contradictory results for IED: some studies claim good fits with power laws; others with non-homogeneous Poisson processes. Given these two approaches, we ask: which is the correct one? Can we reconcile them all? We show here that, surprisingly, both approaches are correct, being corner cases of the proposed Self-Feeding Process (SFP). We show that the SFP (a) exhibits a unifying power, which generates power law tails (including the so-called "top-concavity" that real data exhibits), as well as short-term Poisson behavior; (b) avoids the "i.i.d. fallacy", which none of the prevailing models have studied before; and (c) is extremely parsimonious, requiring usually only one, and in general, at most two parameters. Experiments conducted on eight large, diverse real datasets (e.g., Youtube and blog comments, e-mails, SMSs, etc) reveal that the SFP mimics their properties very well.
Weighted slope one predictors revisited
Authors: Danilo Menezes, Anísio Lacerda, Leila Silva, Adriano Veloso, Nivio Ziviani
Venue: WWW
Year: 2013

Recommender systems are used to help people in specific life choices, like what items to buy, what news to read or what movies to watch. A relevant work in this context is the Slope One algorithm, which is based on the concept of differential popularity between items (i.e., how much better one item is liked than another). This paper proposes new approaches to extend Slope One based predictors for collaborative filtering, in which the predictions are weighted based on the number of users that co-rated items. We propose to improve collaborative filtering by exploiting the web of trust concept, as well as an item utility measure based on the error of predictions based on specific items to specific users. We performed experiments using three application scenarios, namely Movielens, Epinions, and Flixter. Our results demonstrate that, in most cases, exploiting the web of trust is benefitial to prediction performance, and improvements are reported when comparing the proposed approaches against the original Weighted Slope One algorithm.
Domain-sensitive opinion leader mining from online review communities
Authors: Qingliang Miao, Shu Zhang, Yao Meng, Hao Yu
Venue: WWW
Year: 2013

In this paper, we investigate how to identify domain-sensitive opinion leaders in online review communities, and present a model to rank domain-sensitive opinion leaders. To evaluate the effectiveness of the proposed model, we conduct preliminary experiments on a real-world dataset from Amazon.com. Experimental results indicate that the proposed model is effective in identifying domain-sensitive opinion leaders.
Entity search on the web
Authors: Peter Mika
Venue: WWW
Year: 2013

More than the half of queries in the logs of a web search engine refer directly to a single named entity or a named set of entities [1]. To support entity search queries, search engines have begun developing targeted functionality, such as rich displays of factual information, question-answering and related entity recommendations. In this talk, we will provide an overview of recent work in the field of entity search, illustrated by the example of the Spark system, a large-scale system currently in use at Yahoo! for related entity recommendations in web search. Spark combines various knowledge bases and collects evidence from query logs and social media to provide the most relevant related entities for every web query with an entity intent. We discuss the methods used in Spark as well as how the system is evaluated in daily use.
Interactive learning resources and linked data for online scientific experimentation
Authors: Alexander Mikroyannidis, John Domingue
Venue: WWW
Year: 2013

There is currently a huge potential for eLearning in several new online learning initiatives like Massive Open Online Courses (MOOCs) and Open Educational Resources (OERs). These initiatives enable learners to self-regulate their learning by providing them with an abundant amount of free learning materials of high quality. This paper presents FORGE, a new European initiative for online learning using Future Internet Research and Experimentation (FIRE) facilities. FORGE is a step towards turning FIRE into a pan-European educational platform for Future Internet through Linked Data. This will benefit learners and educators by giving them both access to world class facilities in order to carry out experiments on e.g. new internet protocols. In turn, this supports constructivist and self-regulated learning approaches, through the use of interactive learning resources, such as eBooks.
Real time discussion retrieval from twitter
Authors: Dmitrijs Milajevs, Gosse Bouma
Venue: WWW
Year: 2013

While social media receive a lot of attention from the scientific community in general, there is little work on high recall retrieval of messages relevant to a discussion. Hash tag based search is widely used for data retrieval from social media. This work shows limitations of this approach, because the majority of the relevant messages do not even contain any hash tag, and unpredictable hash tags are used as the conversation evolves in time. To overcome these limitations, we propose an alternative retrieval method. Given an input stream of messages as an example of the discussion, our method extracts the most relevant words from it and queries the social network for more messages with these words. Our method filters messages that do not belong to the discussion using an LDA topic model. We demonstrate this concept on manually built collections of tweets about major sport and music events.
Live topic generation from event streams
Authors: Vuk Milicic, Giuseppe Rizzo, José Luis Redondo García, Raphaël Troncy, Thomas Steiner
Venue: WWW
Year: 2013

Social platforms constantly record streams of heterogeneous data about human's activities, feelings, emotions and conversations opening a window to the world in real-time. Trends can be computed but making sense out of them is an extremely challenging task due to the heterogeneity of the data and its dynamics making often short-lived phenomena. We develop a framework which collects microposts shared on social platforms that contain media items as a result of a query, for example a trending event. It automatically creates different visual storyboards that reflect what users have shared about this particular event. More precisely it leverages on: (i) visual features from media items for near-deduplication, and (ii) textual features from status updates to interpret, cluster, and visualize media items. A screencast showing an example of these functionalities is published at: http://youtu.be/8iRiwz7cDYY while the prototype is publicly available at http://mediafinder.eurecom.fr.