OpenRaiser/Intern-Atlas huggingface dataset:
Extracting implicit features in online customer reviews for opinion mining
Authors: Yu Zhang, Weixiang Zhu
Venue: WWW
Year: 2013

As the number of customer reviews grows very rapidly, it is essential to summarize useful opinions for buyers, sellers and producers. One key step of opinion mining is feature extraction. Most existing research focus on finding explicit features, only a few attempts have been made to extract implicit features. Nearly all existing research only concentrate on product features, few has paid attention to other features that relates to sellers, services and logistics. Therefore in this paper, we propose a novel co-occurrence association-based method, which aims to extract implicit features in customer reviews and provide more comprehensive and fine-grained mining results.
Questions about questions: an empirical analysis of information needs on Twitter
Authors: Zhe Zhao, Qiaozhu Mei
Venue: WWW
Year: 2013

Conventional studies of online information seeking behavior usually focus on the use of search engines or question answering (Q&A) websites. Recently, the fast growth of online social platforms such as Twitter and Facebook has made it possible for people to utilize them for information seeking by asking questions to their friends or followers. We anticipate a better understanding of Web users' information needs by investigating research questions about these questions. How are they distinctive from daily tweeted conversations? How are they related to search queries? Can users' information needs on one platform predict those on the other?
Cross-region collaborative filtering for new point-of-interest recommendation
Authors: Ning Zheng, Xiaoming Jin, Lianghao Li
Venue: WWW
Year: 2013

With the rapid growth of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation is in increasingly higher demand these years. In this paper, our aim is to recommend new POIs to a user in regions where he has rarely been before. Different from the classical memory-based recommendation algorithms using user rating data to compute similarity between users or items to make recommendation, we propose a cross-region collaborative filtering method based on hidden topics mined from user check-in records to recommend new POIs. Experimental results on a real-world LBSNs dataset show that our method consistently outperforms naive CF method.
To follow or not to follow: a feature evaluation
Authors: Yanan Zhu, Nazli Goharian
Venue: WWW
Year: 2013

The features available in Twitter provide meaningful information that can be harvested to provide a ranked list of followees to each user. We hypothesize that retweet and mention features can be further enriched by incorporating both temporal and additional/indirect links from within user's community. Our empirical results provide insights into the effectiveness of each feature, and evaluate our proposed similarity measures in ranking the followees. Utilizing temporal information and indirect links improves the effectiveness of retweet and mention features in terms of nDCG.
Security implications of password discretization for click-based graphical passwords
Authors: Bin B. Zhu, Dongchen Wei, Maowei Yang, Jeff Yan
Venue: WWW
Year: 2013

Discretization is a standard technique used in click-based graphical passwords for tolerating input variance so that approximately correct passwords are accepted by the system. In this paper, we show for the first time that two representative discretization schemes leak a significant amount of password information, undermining the security of such graphical passwords. We exploit such information leakage for successful dictionary attacks on Persuasive Cued Click Points (PCCP), which is to date the most secure click-based graphical password scheme and was considered to be resistant to such attacks. In our experiments, our purely automated attack successfully guessed 69.2% of the passwords when Centered Discretization was used to implement PCCP, and 39.4% of the passwords when Robust Discretization was used. Each attack dictionary we used was of approximately 235 entries, whereas the full password space was of 243 entries. For Centered Discretization, our attack still successfully guessed 50% of the passwords when the dictionary size was reduced to approximately 230 entries. Our attack is also applicable to common implementations of other click-based graphical password systems such as PassPoints and Cued Click Points -- both have been extensively studied in the research communities.
Introducing search behavior into browsing based models of page's importance
Authors: Maxim Zhukovskiy, Andrei Khropov, Gleb Gusev, Pavel Serdyukov
Venue: WWW
Year: 2013

BrowseRank algorithm and its modifications are based on analyzing users' browsing trails. Our paper proposes a new method for computing page importance using a more realistic and effective search-aware model of user browsing behavior than the one used in BrowseRank.
Newspaper editors vs the crowd: on the appropriateness of front page news selection
Authors: Arkaitz Zubiaga
Venue: WWW
Year: 2013

The front page is the showcase that might condition whether one buys a newspaper, and so editors carefully select the news of the day that they believe will attract as many readers as possible. Little is known about the extent to which editors' criteria for front page news selection are appropriate so as to matching the actual interests of the crowd. In this paper, we compare the news stories in The New York Times over the period of a year to their popularity on Twitter and Facebook. Our study questions the current news selection criteria, revealing that while editors focus on picking hard news such as politics for the front page, social media users are rather into soft news such as science and fashion.
Harnessing web page directories for large-scale classification of tweets
Authors: Arkaitz Zubiaga, Heng Ji
Venue: WWW
Year: 2013

Classification is paramount for an optimal processing of tweets, albeit performance of classifiers is hindered by the need of large sets of training data to encompass the diversity of contents one can find on Twitter. In this paper, we introduce an inexpensive way of labeling large sets of tweets, which can be easily regenerated or updated when needed. We use human-edited web page directories to infer categories from URLs contained in tweets. By experimenting with a large set of more than 5 million tweets categorized accordingly, we show that our proposed model for tweet classification can achieve 82% in accuracy, performing only 12.2% worse than for web page classification.
RESTful open workflows for data provenance and reuse
Authors: Kai Eckert, Dominique Ritze, Konstantin Baierer, Christian Bizer
Venue: WWW
Year: 2014

In this paper, we present a workflow model together with an implementation following the Linked Data principles and the principles for RESTful web services. By means of RDF-based specifications of web services, workflows, and runtime information, we establish a full provenance chain for all resources created within these workflows.
How effectively can we form opinions?
Authors: AmirMahdi Ahmadinejad, Sina Dehghani, MohammadTaghi Hajiaghayi, Hamid Mahini, Saeed Seddighin, Sadra Yazdanbod
Venue: WWW
Year: 2014

People make decisions and express their opinions according to their communities. An appropriate idea for controlling the diffusion of an opinion is to find influential people, and employ them to spread the desired opinion. We investigate an influencing problem when individuals' opinions are affected by their friends due to the model of Friedkin and Johnsen [4]. Our goal is to design efficient algorithms for finding opinion leaders such that changing their opinions has great impact on the overall opinion of the society.
ComPAS: maximizing data availability with replication in ad-hoc social networks
Authors: Ahmedin Mohammed Ahmed, Qiuyuan Yang, Nana Yaw Asabere, Tie Qiu, Feng Xia
Venue: WWW
Year: 2014

Although existing replica allocation protocols perform well in most cases, some challenges still need to be addressed to further improve their performance. The success of such protocols for Ad-hoc Social Networks (ASNETs) depends on the performance of data accessibility and on the easy consistency management of available replica. We contribute to this line of research with replication protocol for maximizing availability of a data. Essentially, we propose ComPAS, a community-partitioning aware replica allocation method. Its goals include integration of social relationship for placing copy of the data in the community to achieve better efficiency and consistency by keeping the replica read cost, relocation cost and traffic as low as possible.
Pruned labeling algorithms: fast, exact, dynamic, simple and general indexing scheme for shortest-path queries
Authors: Takuya Akiba
Venue: WWW
Year: 2014

Shortest-paths and distances are two of the most fundamental notions for pairs of nodes on a network, and thus they play an important role in a wide range of applications such as network analysis and network-aware search. In this talk, I will introduce our indexing method for efficiently answering shortest-paths, referred to as pruned landmark labeling (SIGMOD'13). In spite of its simplicity, it significantly outperforms previous indexing methods in both scalability and query time. Moreover, interestingly, it turned out that the algorithm automatically exploits the common structures of real networks. We also briefly mention its variants: pruned path labeling (CIKM'13), pruned highway labeling (ALENEX'14) and historical pruned landmark labeling (WWW'14).
Dynamic and historical shortest-path distance queries on large evolving networks by pruned landmark labeling
Authors: Takuya Akiba, Yoichi Iwata, Yuichi Yoshida
Venue: WWW
Year: 2014

We propose two dynamic indexing schemes for shortest-path and distance queries on large time-evolving graphs, which are useful in a wide range of important applications such as real-time network-aware search and network evolution analysis. To the best of our knowledge, these methods are the first practical exact indexing methods to efficiently process distance queries and dynamic graph updates. We first propose a dynamic indexing scheme for queries on the last snapshot. The scalability and efficiency of its offline indexing algorithm and query algorithm are competitive even with previous static methods. Meanwhile, the method is dynamic, that is, it can incrementally update indices as the graph changes over time. Then, we further design another dynamic indexing scheme that can also answer two kinds of historical queries with regard to not only the latest snapshot but also previous snapshots.
Evaluation of information extraction techniques to label extracted data from e-commerce web pages
Authors: Neil Anderson, Jun Hong
Venue: WWW
Year: 2014

Automatically determining and assigning shared and meaningful text labels to data extracted from an e-Commerce web page is a challenging problem. An e-Commerce web page can display a list of data records, each of which can contain a combination of data items (e.g. product name and price) and explicit labels, which describe some of these data items. Recent advances in extraction techniques have made it much easier to precisely extract individual data items and labels from a web page, however, there are two open problems: 1. assigning an explicit label to a data item, and 2. determining labels for the remaining data items. Furthermore, improvements in the availability and coverage of vocabularies, especially in the context of e-Commerce web sites, means that we now have access to a bank of relevant, meaningful and shared labels which can be assigned to extracted data items.
Discovering and learning sensational episodes of news events
Authors: Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He, Zhongzhi Shi
Venue: WWW
Year: 2014

This paper studies the problem of discovering and learning sensational 2-episodes, i.e., pairs of co-occurring news events. To find all frequent episodes, we propose an efficient algorithm, MEELO, which significantly outperforms conventional methods. Given many frequent episodes, we rank them by their sensational effect. Instead of limiting ourselves to any individual subjective measure of sensational effect, we propose a learning-to-rank approach that exploits multiple features to capture the sensational effect of an episode from various aspects. An experimental study on real data verified our approach's efficiency and effectiveness.
Semantic mashup with the online IDE WikiNEXT
Authors: Pavel Arapov, Michel Buffa, Amel Ben Othmane
Venue: WWW
Year: 2014

The proposed demonstration requests DBPedia.org, gets the results and uses them to populate wiki pages with semantic annotations using RDFaLite. These annotations are persisted in a RDF store and we will show how this data can be reused by other applications, e.g. for a semantic mashup that displays all collected metadata about cities on a single map page. It has been developed using WikiNEXT, a mix between a semantic wiki and a web-based IDE. The tool is online , open source ; screencasts are available on YouTube (look for "WikiNext").
A wiki way of programming for the web of data
Authors: Pavel Arapov, Michel Buffa, Amel Ben Othmane
Venue: WWW
Year: 2014

WikiNEXT is a wiki engine that enables users to write rapidly applications directly from the browser, in particular applications that can exploit the web of data. WikiNEXT relies on semantic web formalisms and technologies (RDF/RDFa lite) to describe wiki page content and embedded metadata, and to manipulate them (for example, using the SPARQL language). WikiNEXT is a mix between a web-based IDE (Integrated Development Environment) and a semantic wiki. It embeds several editors (a WYSIWYG editor, and an HTML/JavaScript editor + a JavaScript library manager) for coding in the browser, provides an API for exploiting semantic metadata, and uses a graph based data store and an object oriented database for persistence on the server side. It has been specially designed for writing online programming tutorials (i.e. an HTML5 tutorial, a semantic web tutorial on how to consume linked data, etc.), or more generally for developing web applications that can be mixed with more classical wiki documents (in fact all WikiNEXT pages are web applications). The tool is online, open source ; screencasts are available on YouTube (look for 'WikiNEXT').
Developing web of data applications from the browser
Authors: Pavel Arapov, Michel Buffa, Amel Ben Othmane
Venue: WWW
Year: 2014

WikiNEXT is a wiki engine 100% written in JavaScript that relies on recent APIs and frameworks. It has been designed to author web applications directly in a web browser, which can exploit the web of data. It combines the functionalities of a semantic wiki with those of a Web-based IDE (Integrated Development Environment) in order to develop web applications in addition to writing classic documents. It gives developers a rich internal API (Application Programming Interface) and provides several functionalities to exploit the web of data. Our approach uses templates, a special type of wiki pages that represent the semantic data model. Templates generate wiki pages with semantic annotations that are stored as quadruplets in a triple store engine. To query this semantic data, we provide a SPARQL endpoint. Screencasts are available on YouTube (look for WikiNEXT).
Towards semantic faceted search
Authors: Marcelo Arenas, Bernardo Cuenca Grau, Evgeny Kharlamov, Sarunas Marciuska, Dmitriy Zheleznyakov
Venue: WWW
Year: 2014

In this paper we present limitations of conventional faceted search in the way data, facets, and queries are modelled. We discuss how these limitations can be addressed with Semantic Web technologies such as RDF, OWL 2, and SPARQL 1.1. We also present a system, SemFacet, that is a proof-of-concept prototype of our approach implemented on top of Yago knowledge base, powered by the OWL 2 RL triple store RDFox, and the full text search engine Lucene.
SemFacet: semantic faceted search over yago
Authors: Marcelo Arenas, Bernardo Cuenca Grau, Evgeny Kharlamov, Sarunas Marciuska, Dmitriy Zheleznyakov, Ernesto Jiménez-Ruiz
Venue: WWW
Year: 2014

In this paper we demonstrate a system SemFacet, that is a proof of concept prototype for our semantic faceted search approach. SemFacet is implemented on top of the Yago knowledge base, powered by the OWL 2 RL triple store RDFox, and the full text search engine Lucene. SemFacet has provided very encouraging results. Via logical reasoning SemFacet can automatically (i) extract facets, (ii) update the faceted query interface with facets relevant for the current stage of the users query construction's session. SemFacet supports faceted queries that are much more expressive than the ones of traditional faceted search applications; in particular SemFacet allows to (i) relate several collections of documents, and (ii) change the focus of queries (and, thus, SemFacet provides control over the documents in the query output to be displayed on the screen). Our approach is fully declarative: the same backend implementation can be used to power faceted search over any application, provided that metadata and knowledge are represented in RDF and OWL 2.
Efficient network generation under general preferential attachment
Authors: James Atwood, Bruno F. Ribeiro, Don Towsley
Venue: WWW
Year: 2014

Preferential attachment (PA) models of network structure are widely used due to their explanatory power and conceptual simplicity. PA models are able to account for the scale-free degree distributions observed in many real-world large networks by sequentially introducing nodes that attach preferentially to existing nodes with high degree. The ability to efficiently generate instances from PA models is a key asset in understanding both the models themselves and the real networks that they represent. Surprisingly, little attention has been paid to the problem of efficient instance generation. In this paper, we show that the complexity of generating network instances from a PA model depends on the preference function of the model, provides efficient data structures that work under any preference function, and presents empirical results from an implementation based on these data structures. We demonstrate that, by indexing growing networks with a simple augmented heap, we can implement a network generator which scales many orders of magnitude beyond existing capabilities (106 to 108 nodes). We show the utility of an efficient and general PA network generator by investigating the consequences of varying the preference functions of an existing model. We also provide ‘quicknet,’ a freely available open-source implementation of the methods described in this work.
On the evolution of social groups during coffee breaks
Authors: Martin Atzmueller, Andreas Ernst, Friedrich Krebs, Christoph Scholz, Gerd Stumme
Venue: WWW
Year: 2014

This paper focuses on the analysis of group evolution events in networks of face-to-face proximity. First, we analyze statistical properties of group evolution, e.g., individual activity and typical group sizes. Furthermore, we define a set of specific group evolution events. We analyze these using real-world data collected at the LWA 2010 conference using the Conferator system, and discuss patterns according to different phases of the conference.
Towards web intelligence through the crowdsourcing of semantics
Authors: Sören Auer, Dimitris Kontokostas
Venue: WWW
Year: 2014

A key success factor for the Web as a whole was and is its participatory nature. We discuss strategies for engaging human-intelligence to make the Web more semantic.
Scalability and efficiency challenges in large-scale web search engines
Authors: Ricardo Baeza-Yates, Berkant Barla Cambazoglu
Venue: WWW
Year: 2014

The main goals of a web search engine are quality, efficiency, and scalability. In this tutorial, we focus on the last two goals, providing a fairly comprehensive overview of the scalability and efficiency challenges in large-scale web search engines. In particular, the tutorial provides an in-depth architectural overview of a web search engine, mainly focusing on the web crawling, indexing, and query processing components. The scalability and efficiency issues encountered in these components are presented at four different granularities: at the level of a single computer, a cluster of computers, a single data center, and a multi-center search engine. The tutorial also points at open research problems and provides recommendations to researchers who are new to the field.
Metadata-driven hypertext content publishing and styling
Authors: Xi Bai, Armin Haller, Ewan Klein, Dave Robertson
Venue: WWW
Year: 2014

A growing number of approaches and tools have been utilised attempting at generating hypertext content with embedded metadata. However, little work has been carried out on finding a generic solution for publishing and styling Web pages with annotations derived from existing RDF data sets available in various formats. This paper proposes a metadata-driven publishing framework assisting publishers or webmasters in generating semantically-enriched content (HTML pages or snippets) by harnessing distributed RDF(a) documents or repositories with little human intervention. This framework also helps users to create and share so-called micro-themes, which is applicable to the above generated content for the purpose of page styling and also highly reusable thanks to the adopted semantic attribute selectors.
Demographics, weather and online reviews: a study of restaurant recommendations
Authors: Saeideh Bakhshi, Partha Kanuparthy, Eric Gilbert
Venue: WWW
Year: 2014

Online recommendation sites are valuable information sources that people contribute to, and often use to choose restaurants. However, little is known about the dynamics behind participation in these online communities and how the recommendations in these communities are formed. In this work, we take a first look at online restaurant recommendation communities to study what endogenous (i.e., related to entities being reviewed) and exogenous factors influence people's participation in the communities, and to what extent. We analyze an online community corpus of 840K restaurants and their 1.1M associated reviews from 2002 to 2011, spread across every U.S. state. We construct models for number of reviews and ratings by community members, based on several dimensions of endogenous and exogenous factors. We find that while endogenous factors such as restaurant attributes (e.g., meal, price, service) affect recommendations, surprisingly, exogenous factors such as demographics (e.g., neighborhood diversity, education) and weather (e.g., temperature, rain, snow, season) also exert a significant effect on reviews. We find that many of the effects in online communities can be explained using offline theories from experimental psychology. Our study is the first to look at exogenous factors and how it related to online online restaurant reviews. It has implications for designing online recommendation sites, and in general, social media and online communities.
Analyzing temporal characteristics of check-in data
Authors: Sushma Bannur, Omar Alonso
Venue: WWW
Year: 2014

There is a surge in the use of location activity in social media, in particular to broadcast the change of physical whereabouts. We are interested in analyzing the temporal characteristics of check-ins data from the user's perspective and also at the aggregate level for detecting patterns. In this paper we conduct a large study using check-in data from Facebook to analyze different temporal characteristics in four venue categories (restaurants, movies, shopping, and get-away). We present the results of such study and outline application areas where the conjunction of location and temporal-aware data can help in new search scenarios.