OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of large scale climate data: how well climate change models and data from real sensor networks agree?
Authors: Santiago Augusto Nunes, Luciana A. S. Romani, Ana Maria Heuminski de Ávila, Priscila P. Coltri, Caetano Traina Jr., Robson Leonardo Ferreira Cordeiro, Elaine P. M. de Sousa, Agma J. M. Traina
Venue: WWW
Year: 2013

Research on global warming and climate changes has attracted a huge attention of the scientific community and of the media in general, mainly due to the social and economic impacts they pose over the entire planet. Climate change simulation models have been developed and improved to provide reliable data, which are employed to forecast effects of increasing emissions of greenhouse gases on a future global climate. The data generated by each model simulation amount to Terabytes of data, and demand fast and scalable methods to process them. In this context, we propose a new process of analysis aimed at discriminating between the temporal behavior of the data generated by climate models and the real climate observations gathered from ground-based meteorological station networks. Our approach combines fractal data analysis and the monitoring of real and model-generated data streams to detect deviations on the intrinsic correlation among the time series defined by different climate variables. Our measurements were made using series from a regional climate model and the corresponding real data from a network of sensors from meteorological stations existing in the analyzed region. The results show that our approach can correctly discriminate the data either as real or as simulated, even when statistical tests fail. Those results suggest that there is still room for improvement of the state-of-the-art climate change models, and that the fractal-based concepts may contribute for their improvement, besides being a fast, parallelizable, and scalable approach.
The chrooma+ approach to enrich video content using HTML5
Authors: Philipp Oehme, Michael Krug, Fabian Wiedemann, Martin Gaedke
Venue: WWW
Year: 2013

The Internet has become an important source for media content. Content types are not limited to text and pictures but also include video and audio. Currently audiovisual media is presented as it is. However, these media do not integrate the huge amount of related information, which is available on the Web. In this paper we present the Chrooma+ approach to improve the user experience of media consumption by enriching media content with additional information from various sources in the Web. Our approach focuses on the aggregation and combination of this related information with audiovisual media. This approach involves using new HTML5 technologies and with WebVTT a new annotation format to display relevant information at definite times. Some of the advantages of this approach are the usage of a rich annotation format and extensibility to include heterogeneous information sources.
A probability-based trust prediction model using trust-message passing
Authors: Hyun-Kyo Oh, Jin-Woo Kim, Sang-Wook Kim, Kichun Lee
Venue: WWW
Year: 2013

We propose a probability-based trust prediction model based on trust-message passing which takes advantage of the two kinds of information: an explicit information and an implicit information.
Like prediction: modeling like counts by bridging facebook pages with linked data
Authors: Shohei Ohsawa, Yutaka Matsuo
Venue: WWW
Year: 2013

Recent growth of social media has produced a new market for branding of people and businesses. Facebook provides Facebook Pages (Pages in short) for public figures and businesses (we call entities) to communicate with their fans through a Like button. Because Like counts sometimes reflect the popularity of entities, techniques to increase the Like count can be a matter of interest, and might be known as social media marketing. From an academic perspective, Like counts of Pages depend not only on the popularity of the entity, but also on the popularity of semantically related entities. For example, Lady Gaga's Page has many Likes; her song "Poker Face" does too. We can infer that her next song will acquire many Likes immediately. Important questions are these: How does the Like count of Lady Gaga affect the Like count of her song? Alternatively, how does the Like count of her song constitute some fraction of the Like count of Lady Gaga herself?
A proposal for automatic diagnosis of malaria: extended abstract
Authors: Allisson Dantas Oliveira, Giordano Cabral, Daniel López, Caetano Firmo, Francesc Zarzuela Serrat, Jones Oliveira de Albuquerque
Venue: WWW
Year: 2013

This paper presents a methodology for automatic diagnosis of malaria using computer vision techniques combined with artificial intelligence. We had obtained an accuracy rate of 74% in the detection system.
FS-NER: a lightweight filter-stream approach to named entity recognition on twitter data
Authors: Diego Marinho de Oliveira, Alberto H. F. Laender, Adriano Veloso, Altigran Soares da Silva
Venue: WWW
Year: 2013

Microblog platforms such as Twitter are being increasingly adopted by Web users, yielding an important source of data for web search and mining applications. Tasks such as Named Entity Recognition are at the core of many of these applications, but the effectiveness of existing tools is seriously compromised when applied to Twitter data, since messages are terse, poorly worded and posted in many different languages. Also, Twitter follows a streaming paradigm, imposing that entities must be recognized in real-time. In view of these challenges and the inappropriateness of existing tools, we propose a novel approach for Named Entity Recognition on Twitter data called FS-NER (Filter-Stream Named Entity Recognition). FS-NER is characterized by the use of filters that process unlabeled Twitter messages, being much more practical than existing supervised CRF-based approaches. Such filters can be combined either in sequence or in parallel in a flexible way. Moreover, because these filters are not language dependent, FS-NER can be applied to different languages without requiring a laborious adaptation. Through a systematic evaluation using three Twitter collections and considering seven types of entity, we show that FS-NER performs 3% better than a CRF-based baseline, besides being orders of magnitude faster and much more practical.
Linking and visualizing television heritage: the EUscreen virtual exhibitions and the linked open data pilot
Authors: Johan Oomen, Vassilis Tzouvaras, Kati Hyyppä
Venue: WWW
Year: 2013

The EUscreen initiative represents the European television archives and acts as a domain aggregator for Europeana, Europe's digital library, which provides access to over 20 million digitized cultural objects. The main motivation for the initiative is to provide unified access to a representative collection of television programs, secondary sources and articles, and in this way to allow students, scholars and the general public to study the history of television in its wider context. This paper explores the EUscreen activities related to novel ways to present curated content and publishing EUscreen metadata as Linked Open Data.
Mining emotions in short films: user comments or crowdsourcing?
Authors: Claudia Orellana-Rodriguez, Ernesto Diaz-Aviles, Wolfgang Nejdl
Venue: WWW
Year: 2013

Short films are regarded as an alternative form of artistic creation, and they express, in a few minutes, a whole gamma of different emotions oriented to impact the audience and communicate a story. In this paper, we exploit a multi-modal sentiment analysis approach to extract emotions in short films, based on the film criticism expressed through social comments from the video-sharing platform YouTube. We go beyond the traditional polarity detection (i.e., positive/negative), and extract, for each analyzed film, four opposing pairs of primary emotions: joy-sadness, anger-fear, trust-disgust, and anticipation-surprise. We found that YouTube comments are a valuable source of information for automatic emotion detection when compared to human analysis elicited via crowdsourcing.
SEED: a framework for extracting social events from press news
Authors: Salvatore Orlando, Francesco Pizzolon, Gabriele Tolomei
Venue: WWW
Year: 2013

Everyday people are exchanging a huge amount of data through the Internet. Mostly, such data consist of unstructured texts, which often contain references to structured information (e.g., person names, contact records, etc.). In this work, we propose a novel solution to discover social events from actual press news edited by humans. Concretely, our method is divided in two steps, each one addressing a specific Information Extraction (IE) task: first, we use a technique to automatically recognize four classes of named-entities from press news: DATE, LOCATION, PLACE, and ARTIST. Furthermore, we detect social events by extracting ternary relations between such entities, also exploiting evidence from external sources (i.e., the Web). Finally, we evaluate both stages of our proposed solution on a real-world dataset. Experimental results highlight the quality of our first-step Named-Entity Recognition (NER) approach, which indeed performs consistently with state-of-the-art solutions. Eventually, we show how to precisely select true events from the list of all candidate events (i.e., all the ternary relations), which result from our second-step Relation Extraction (RE) method. Indeed, we discover that true social events can be detected if enough evidence of those is found in the result list of Web search engines.
An effective class-centroid-based dimension reduction method for text classification
Authors: Guansong Pang, Huidong Jin, Shengyi Jiang
Venue: WWW
Year: 2013

Motivated by the effectiveness of centroid-based text classification techniques, we propose a classification-oriented class-centroid-based dimension reduction (DR) method, called CentroidDR. Basically, CentroidDR projects high-dimensional documents into a low-dimensional space spanned by class centroids. On this class-centroid-based space, the centroid-based classifier essentially becomes CentroidDR plus a simple linear classifier. Other classification techniques, such as K-Nearest Neighbor (KNN) classifiers, can be used to replace the simple linear classifier to form much more effective text classification algorithms. Though CentroidDR is simple, non-parametric and runs in linear time, preliminary experimental results show that it can improve the accuracy of the classifiers and perform better than general DR methods such as Latent Semantic Indexing (LSI).
A framework for self-descriptive RESTful services
Authors: Luca Panziera, Flavio De Paoli
Venue: WWW
Year: 2013

REST principles define services as resources that can be manipulated by a set of well-known methods. The same approach is suitable to define service descriptions as resources. In this paper, we try to unify the two concepts (services and their descriptions) by proposing a set of best practices to build self-descriptive RESTful services accessible by both humans and machines. Moreover, to make those practices usable with little manual effort, we provide a software framework that extracts compliant descriptions from documents published on the Web, and makes them available to clients as resources.
Sampling bias in user attribute estimation of OSNs
Authors: Hosung Park, Sue B. Moon
Venue: WWW
Year: 2013

Recent work on unbiased sampling of OSNs has focused on estimation of the network characteristics such as degree distributions and clustering coefficients. In this work we shift the focus to node attributes. We show that existing sampling methods produce biased outputs and need modifications to alleviate the bias.
Scalable k-nearest neighbor graph construction based on greedy filtering
Authors: Youngki Park, Sungchan Park, Sang-goo Lee, Woosung Jung
Venue: WWW
Year: 2013

K-Nearest Neighbor Graph (K-NNG) construction is a primitive operation in the field of Information Retrieval and Recommender Systems. However, existing approaches to K-NNG construction do not perform well as the number of nodes or dimensions scales up. In this paper, we present greedy filtering, an effcient and scalable algorithm for selecting the candidates for nearest neighbors by matching only the dimensions of large values. The experimental results show that our K-NNG construction scheme, based on greedy filtering, guarantees a high recall while also being 5 to 6 times faster than state-of-the-art algorithms for large, high-dimensional data.
How to hack into Facebook without being a hacker
Authors: Tarun Parwani, Ramin Kholoussi, Panagiotis Karras
Venue: WWW
Year: 2013

The proliferation of online social networking services has aroused privacy concerns among the general public. The focus of such concerns has typically revolved around providing explicit privacy guarantees to users and letting users take control of the privacy-threatening aspects of their online behavior, so as to ensure that private personal information and materials are not made available to other parties and not used for unintended purposes without the user's consent. As such protective features are usually opt-in, users have to explicitly opt-in for them in order to avoid compromising their privacy. Besides, third-party applications may acquire a user's personal information, but only after they have been granted consent by the user. If we also consider potential network security attacks that intercept or misdirect a user's online communication, it would appear that the discussion of user vulnerability has accurately delimited the ways in which a user may be exposed to privacy threats.
Predicting group stability in online social networks
Authors: Akshay Patil, Juan Liu, Jie Gao
Venue: WWW
Year: 2013

Social groups often exhibit a high degree of dynamism. Some groups thrive, while many others die over time. Modeling group stability dynamics and understanding whether/when a group will remain stable or shrink over time can be important in a number of social domains. In this paper, we study two different types of social networks as exemplar platforms for modeling and predicting group stability dynamics. We build models to predict if a group is going to remain stable or is likely to shrink over a period of time. We observe that both the level of member diversity and social activities are critical in maintaining the stability of groups. We also find that certain 'prolific' members play a more important role in maintaining the group stability. Our study shows that group stability can be predicted with high accuracy, and feature diversity is critical to prediction performance.
Trustworthiness criteria for supporting users to assess the credibility of web information
Authors: Jarutas Pattanaphanchai, Kieron O&apos, Hara, Wendy Hall
Venue: WWW
Year: 2013

Assessing the quality of information on the Web is a challenging issue for at least two reasons. First, as a decentralized data publishing platform in which anyone can share nearly anything, the Web has no inherent quality control mechanisms to ensure that content published is valid, legitimate, or even just interesting. Second, when assessing the trustworthiness of web pages, users tend to base their judgments upon descriptive criteria such as the visual presentation of the website rather than more robust normative criteria such as the author's reputation and the source's review process. As a result, Web users are liable to make incorrect assessments, particularly when making quick judgments on a large scale. Therefore, Web users need credibility criteria and tools to help them assess the trustworthiness of Web information in order to place trust in it. In this paper, we investigate the criteria that can be used to collect supportive data about a piece of information in order to improve a person's ability to quickly judge the trustworthiness of the information. We propose the normative trustworthiness criteria namely, authority, currency, accuracy and relevance which can be used to support users' assessments of the trustworthiness of Web information. In addition, we validate these criteria using an expert panel. The results show that the proposed criteria are helpful. Moreover, we obtain weighting scores for criteria which can be used to calculate the trustworthiness of information and suggest a piece of information that is more likely to be trustworthy to Web users.
The social meanings of social networks: integrating SNA and ethnography of social networking
Authors: Rogério de Paula
Venue: WWW
Year: 2013

In this talk, I examine the manifest, emic meanings of social networking in the context of social network analysis and it uses this to discuss how the confluence of social science and computational sociology can contribute to a richer understanding of how emerging social technologies shape and are shaped by people's everyday practices.
The atomic web browser
Authors: Cesare Pautasso, Masiar Babazadeh
Venue: WWW
Year: 2013

The Atomic Web Browser achieves atomicity for distributed transactions across multiple RESTful APIs. Assuming that the participant APIs feature support for the Try-Confirm/Cancel pattern, the user may navigate with the Atomic Web Browser among multiple Web sites to perform local resource state transitions (e.g., reservations or bookings). Once the user indicates that the navigation has successfully completed, the Atomic Web browser takes care of confirming the local transitions to achieve the atomicity of the global transaction.
Autonomously reviewing and validating the knowledge base of a never-ending learning system
Authors: Saulo D. de S. Pedro, Ana Paula Appel, Estevam R. Hruschka Jr.
Venue: WWW
Year: 2013

The amount of information available on the Web has been increasing daily. However, how one might know what is right or wrong? Does the Web itself can be used as a source for verification of information? NELL (Never-Ending Language Learner) is a computer system that gathers knowledge from Web. Prophet is a link prediction component on NELL that has been successfully used to help populate its knowledge database. However, during link prediction task performance Prophet classify some edges as misplaced edges, that is, edges that we can not assure if they are right or not. In this paper we use the Web itself, using question answer (QA) systems, as a Prophet extension to validate these edges. This is an important issue when working with a self-supervised system where inserted errors might be propagate and generate dangerous concept drifting.
AELA: an adaptive entity linking approach
Authors: Bianca Pereira, Nitish Aggarwal, Paul Buitelaar
Venue: WWW
Year: 2013

The number of available Linked Data datasets has been increasing over time. Despite this, their use to recognise entities in unstructured plain text (Entity Linking task) is still limited to a small number of datasets. In this paper we propose a framework adaptable to the structure of generic Linked Data datasets. This adaptability allows a broader use of Linked Data datasets for the Entity Linking task.
Radialize: a tool for social listening experience on the web based on radio station programs
Authors: Álvaro R. Pereira Jr., Diego Dutra, Milton Stiilpen Junior, Alex Amorim Dutra, Felipe Martins Melo, Paulo H. C. Mendonça, Ângelo Magno de Jesus, Kledilson Ferreira
Venue: WWW
Year: 2013

Radialize represents a service for listening to music and radio programs through the web. The service allows the discovery of the content being played by radio stations on the web, either by managing explicit information made available by those stations or by means of our technology for automatic recognition of audio content in a stream. Radialize then offers a service in which the user can search, be recommended, and provide feedback on artists and songs being played in traditional radio stations, either explicitly or implicitly, in order to compose an individual profile. The recommender system utilizes every user interaction as a data source, as well as the similarity abstraction extracted out of the radios' musical programs, making use of the wisdom of crowds implicitly present in the radio programs.
On using inter-document relations in microblog retrieval
Authors: Jesus A. Rodriguez Perez, Yashar Moshfeghi, Joemon M. Jose
Venue: WWW
Year: 2013

Microblog Ad-hoc retrieval has received much attention in recent years. As a result of the high vocabulary diversity of the publishing users, a mismatch is formed between the queries being formulated and the tweets representing the actual topics. In this work, we present a re-ranking approach relying on inter-document relations, which attempts to bridge this gap. Experiments with TREC's Microblog 2012 collection show that including such information in the retrieval process, statistically significantly improves retrieval effectiveness in terms of Precision and MAP, when the baseline performs well as a starting point.
Russian web spam evolution: yandex experience
Authors: Sergey Pevtsov, Sergey Volkov
Venue: WWW
Year: 2013

Web spam has a negative impact on the search quality and users' satisfaction and forces search engines to waste resources to crawl, index, and rank it. Thus search engines are compelled to make significant efforts in order to fight web spam. Traffic from search engines plays a great role in online economics. It causes a tough competition for high positions in search results and increases the motivation of spammers to invent new spam techniques. At the same time, ranking algorithms become more complicated, as well as web spam detection methods. So, web spam constantly evolves which makes the problem of web spam detection always relevant and challenging.
EventShop: recognizing situations in web data streams
Authors: Siripen Pongpaichet, Vivek K. Singh, Mingyan Gao, Ramesh C. Jain
Venue: WWW
Year: 2013

Web Observatories must address fundamental societal challenges using enormous volumes of data being created due to the significant progress in technology. The proliferation of heterogeneous data streams generated by social media, sensor networks, internet of things, and digitalization of transactions in all aspect of humans? life presents an opportunity to establish a new era of networks called Social Life Networks (SLN). The main goal of SLN is to connect People to Resources effectively, efficiently, and promptly in given Situations. Towards this goal, we present a computing framework, called EventShop, to recognize evolving situations from massive web streams in real-time. These web streams can be fundamentally considered as spatio-temporal-thematic streams and can be combined using a set of generic spatio-temporal analysis operators to recognize evolving situations. Based on the detected situations, the relevant information and alerts can be provided to both individuals and organizations. Several examples from the real world problems have been developed to test the efficacy of EventShop framework.
Information verification during natural disasters
Authors: Abdulfatai Popoola, Dmytro Krasnoshtan, Attila-Péter Tóth, Victor Naroditskiy, Carlos Castillo, Patrick Meier, Iyad Rahwan
Venue: WWW
Year: 2013

Large amounts of unverified and at times contradictory information often appear on social media following natural disasters. Timely verification of this information can be crucial to saving lives and for coordinating relief efforts. Our goal is to enable this verification by developing an online platform that involves ordinary citizens in the evidence gathering and evaluation process. The output of this platform will provide reliable information to humanitarian organizations, journalists, and decision makers involved in relief efforts.