OpenRaiser/Intern-Atlas huggingface dataset:
Are influential writers more objective?: an analysis of emotionality in review comments
Authors: Lionel Martin, Valentina Sintsova, Pearl Pu
Venue: WWW
Year: 2014

People increasingly rely on other consumers' opinion to make online purchase decisions. Amazon alone provides access to millions of reviews, risking to cause information overload to an average user. Recent research has thus aimed at understanding and identifying reviews that are considered helpful. Most of such works analyzed the structure and connectivity of social networks to identify influential users. We believe that insight about influence can be gained from analyzing the affective content of the text as well as affect intensity. We employ text mining to extract the emotionality of 68,049 hotel reviews in order to investigate how those influencers behave, especially their choice of words. We analyze whether texts with words and phrases indicative of a writer's emotions, moods, and attitudes are more likely to trigger a genuine interest compared to more neutral texts. Our initial hypothesis was that influential writers are more likely to refrain themselves from expressing their sentiments in order to achieve a more perceived objectivity. But contrary to this initial assumption, our study shows that they use more affective words, both in terms of emotion variety and intensity. This work describes the first step towards building a helpfulness prediction algorithm using emotion lexicons.
Understanding Twitter influence in the health domain: a social-psychological contribution
Authors: Andrew R. McNeill, Pam Briggs
Venue: WWW
Year: 2014

Twitter can be a powerful tool for the dissemination and discussion of public health information but how can we best describe its influence? In this paper we draw on social-psychological concepts such as social norms, social representations, emotions and rhetoric to explain how influence works both in terms of the spread of information and also its personal impact. Using tweets drawn from a range of health issues, we show that social psychological theory can be used in the qualitative analysis of Twitter data to further our understanding of how health behaviours can be affected by social media discourse.
Automatic detection and correction of web application vulnerabilities using data mining to predict false positives
Authors: Iberia Medeiros, Nuno Ferreira Neves, Miguel Correia
Venue: WWW
Year: 2014

Web application security is an important problem in today's internet. A major cause of this status is that many programmers do not have adequate knowledge about secure coding, so they leave applications with vulnerabilities. An approach to solve this problem is to use source code static analysis to find these bugs, but these tools are known to report many false positives that make hard the task of correcting the application. This paper explores the use of a hybrid of methods to detect vulnerabilities with less false positives. After an initial step that uses taint analysis to flag candidate vulnerabilities, our approach uses data mining to predict the existence of false positives. This approach reaches a trade-off between two apparently opposite approaches: humans coding the knowledge about vulnerabilities (for taint analysis) versus automatically obtaining that knowledge (with machine learning, for data mining). Given this more precise form of detection, we do automatic code correction by inserting fixes in the source code. The approach was implemented in the WAP tool and an experimental evaluation was performed with a large set of open source PHP applications.
Cognitive resource-aware web service selection in mobile computing environments
Authors: Angel Jiménez Molina, In-Young Ko
Venue: WWW
Year: 2014

The proactive and spontaneous delivery of services for mobile users on the move can lead to the depletion of users' mental resources, affecting the normal processes of their physical activities. This is due to the competition for limited mental resources between the human-computer interactions required by services and the users' physical activities. To deal with this problem we propose a service selection method based on two theories from cognitive psychology. This mechanism assesses the degree of demand for mental resources by both the physical activities and the services. Additionally, a service binding and scheduling algorithm ensures less cognitively-taxing mobile service compositions.
Active learning with partially featured data
Authors: Seungwhan Moon, Calvin McCarter, Yu-Hsin Kuo
Venue: WWW
Year: 2014

In this paper, we propose a new active learning algorithm in which the learner chooses the samples to be queried from the unlabeled data points whose attributes are only partially observed. In addition, we propose a cost-driven decision framework where the learner chooses to query either the labels or the missing attributes. This problem statement addresses a common constraint when building large datasets and applying active learning techniques on them, where some of the attributes (including the labels) are significantly harder or more costly to acquire per data point. We take a novel approach to this problem, first by building an imputation model that maps from the partially featured data to the fully featured dimension, and then performing active learning on the projected input space combined with the estimated confidence of inference. We discuss that our approach is flexible and can work with graph mining tasks as well as conventional semi-supervised learning problems. The results suggest that the proposed algorithm facilitates more cost-efficient annotation than the baselines.
When is it biased?: assessing the representativeness of twitter's streaming API
Authors: Fred Morstatter, Jürgen Pfeffer, Huan Liu
Venue: WWW
Year: 2014

Twitter shares a free 1% sample of its tweets through the "Streaming API". Recently, research has pointed to evidence of bias in this source. The methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. We tackle the problem of finding sample bias without costly and restrictive Firehose data. We propose a solution that focuses on using an open data source to find bias in the Streaming API.
Unsupervised approach for shallow domain ontology construction from corpus
Authors: Subhabrata Mukherjee, Jitendra Ajmera, Sachindra Joshi
Venue: WWW
Year: 2014

In this work we propose an unsupervised approach to construct a domain-specific ontology from corpus. It is essential for Information Retrieval systems to identify important domain concepts and relationships between them. We identify important domain terms of which multi-words form an important component. Our approach identifies 40% of the domain terms, compared to 22% identified by WordNet on manually annotated smartphone data. We propose an approach to construct a shallow ontology from discovered domain terms by identifying four domain relations namely, Synonyms ('similar-to'), Type-Of ('is-a'), Action-On ('methods') and Feature-Of ('attributes'), where we achieve an F-Score of 49.14%, 65.5%, 65% and 80% respectively
Help yourself: a virtual self-assist system
Authors: Subhabrata Mukherjee, Sachindra Joshi
Venue: WWW
Year: 2014

In this work, we describe an unsupervised framework for creating self-assist systems which can serve as virtual call center agents to guide the customer in performing different domain-dependent tasks (like troubleshooting a problem, changing settings etc.). We describe a framework for creating an intent graph from a corpus of knowledge articles from a given domain which is used in creating the dialogue system. To the best of our knowledge, this is the first work in creating virtual self-assist agents.
Detecting trending topics using page visitation statistics
Authors: Sayandev Mukherjee, Ronald Sujithan, Pero Subasic
Venue: WWW
Year: 2014

Many applications including realtime recommenders and ad-targeting systems have a need to identify trending concepts to prioritize the information presented to end-users. In this paper, we describe a novel approach that identifies trending concepts using the hourly Wikipedia page visitation statistics freely available for download. We describe a MapReduce framework that analyzes the raw hourly visitation logs and generates a ranked list of trending concepts on a daily basis. We validate this approach by extracting hourly lists of trending news articles, mapping these articles to Wikipedia concepts, and computing the similarity of the two lists according to several commonly used measures.
LSCitter: building social machines by augmenting existing social networks with interaction models
Authors: Dave Murray-Rust, Dave Robertson
Venue: WWW
Year: 2014

We present LSCitter, an implemented framework for supporting human interaction on social networks with formal models of interaction, designed as a generic tool for creating social machines on existing infrastructure. Interaction models can be used to choreograph distributed systems, providing points of coordination and communication between multiple interacting actors. While existing social networks specify how interactions happen---who messages go to and when, the effects of carrying out actions---these are typically implicit, opaque and non user-editable. Treating interaction models as first class objects allows the creation of electronic institutions, on which users can then choose the kinds of interaction they wish to engage in, with protocols which are explicit, visible and modifiable. However, there is typically a cost to users to engage with these institutions. In this paper we introduce the notion of "shadow institutions", where actions on existing social networks are mapped onto formal interaction protocols, allowing participants access to computational intelligence in a seamless, zero-cost manner to carry out computation and store information.
Information network or social network?: the structure of the twitter follow graph
Authors: Seth A. Myers, Aneesh Sharma, Pankaj Gupta, Jimmy Lin
Venue: WWW
Year: 2014

In this paper, we provide a characterization of the topological features of the Twitter follow graph, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and degree assortativity. For each of these properties, we compare and contrast with available data from other social networks. These analyses provide a set of authoritative statistics that the community can reference. In addition, we use these data to investigate an often-posed question: Is Twitter a social network or an information network? The "follow" relationship in Twitter is primarily about information consumption, yet many follows are built on social ties. Not surprisingly, we find that the Twitter follow graph exhibits structural characteristics of both an information network and a social network. Going beyond descriptive characterizations, we hypothesize that from an individual user's perspective, Twitter starts off more like an information network, but evolves to behave more like a social network. We provide preliminary evidence that may serve as a formal model of how a hybrid network like Twitter evolves.
Characterizing high-impact features for content retention in social web applications
Authors: Kaweh Djafari Naini, Ricardo Kawase, Nattiya Kanhabua, Claudia Niederée
Venue: WWW
Year: 2014

One of the core challenges of automatically creating Social Web summaries is to decide which posts to remember, i.e., to consider for summary inclusion and which to forget. Keeping everything would overwhelm the user and would also neglect the often intentionally ephemeral nature of Social Web posts. In this paper, we analyze high-impact features that characterize memorable posts as a first step for this selection process. Our work is based on a user evaluation for discovering human expectations towards content retention.
A new architecture description language for social machines
Authors: Leandro Marques Nascimento, Vanilson André de Arruda Burégio, Vinicius Cardoso Garcia, Silvio R. L. Meira
Venue: WWW
Year: 2014

The term "Social Machine" (SM) has been commonly used as a synonym for what is known as the programmable web or web 3.0. Some definitions of a Social Machine have already been provided and they basically support the notion of relationships between distributed entities. The type of relationship molds which services would be provided or required by each machine, and under certain complex constraints. In order to deal with the complexity of this emerging web, we present a language that can describe networks of Social Machines, named SMADL -- the Social Machine Architecture Description Language. In few words, SMADL is as a relationship-driven language which can be used to describe the interactions between any number of machines in a multitude of ways, as a means to represent real machines interacting in the real web, such as, Twitter running on top of Amazon AWS or mash-ups built upon Google Maps, and obviously, as a means to represent interactions with other social machines too.
Building engagement for MOOC students: introducing support for time management on online learning platforms
Authors: Ilona Nawrot, Antoine Doucet
Venue: WWW
Year: 2014

The main objectives of massive open online courses (MOOC) are to foster knowledge through free high quality learning materials procurement; to create new knowledge through diverse users' interactions with the providing platform; and to empower research on learning. However, MOOC providers are also businesses (either profit or not-for-profit). They are still in the early stages of their development, but sooner or later, in order to secure their existence and assure their longterm growth, they will have to adapt a business model and monetize the services they provide. Nevertheless, despite their popularity MOOCs are characterized by a very high drop-out rate (about 90%), which may turn out to be a problem regardless of the adapted business model. Hence, MOOC providers can either assume the scale benefits to be sufficiently high to ignore the problem of low MOOC completion rate or tackle this problem. In this paper we explore the problem of the high drop-out rate in massive open online courses. First, we identify its main cause by conducting an online survey, namely bad time organization. Secondly, we provide suggestions to reduce the rate. Specifically, we argue that MOOC platforms should not only provide their users with high quality educational materials and interaction facilities. But they should also support and assist the users in their quest for knowledge. Thus, MOOC platforms should provide tools helping them optimize their time usage and subsequently develop metacognitive skills indispensable in proper time management of learning processes.
Stranger danger: exploring the ecosystem of ad-based URL shortening services
Authors: Nick Nikiforakis, Federico Maggi, Gianluca Stringhini, M. Zubair Rafique, Wouter Joosen, Christopher Kruegel, Frank Piessens, Giovanni Vigna, Stefano Zanero
Venue: WWW
Year: 2014

URL shortening services facilitate the need of exchanging long URLs using limited space, by creating compact URL aliases that redirect users to the original URLs when followed. Some of these services show advertisements (ads) to link-clicking users and pay a commission of their advertising earnings to link-shortening users.
An experimental evaluation of bidders' behavior in ad auctions
Authors: Gali Noti, Noam Nisan, Ilan Yaniv
Venue: WWW
Year: 2014

We performed controlled experiments of human participants in a continuous sequence of ad auctions, similar to those used by Internet companies. The goal of the research was to understand users' strategies in making bids. We studied the behavior under two auction types: (1) the Generalized Second-Price (GSP) auction and (2) the Vickrey--Clarke--Groves (VCG) payment rule, and manipulated also the participants' knowledge conditions: (1) explicitly given valuations and (2) payoff information from which valuations could be deduced. We found several interesting behaviors, among them are: No convergence to equilibrium was detected; moreover the frequency with which participants modified their bids increased with time. We can detect explicit "better-response" behavior rather than just mixed bidding. While bidders in GSP auctions do strategically shade their bids, they tend to bid higher than theoretically predicted by the standard VCG-like equilibrium of GSP. Bidders who are not explicitly given their valuations but can only deduce them from their gains behave a little less "precisely" than those with such explicit knowledge, but mostly during an initial learning phase. VCG and GSP yield approximately the same (high) social welfare, but GSP tends to give higher revenue.
Towards scalable X10 based link prediction for large scale social networks
Authors: Hidefumi Ogata, Toyotaro Suzumura
Venue: WWW
Year: 2014

The use of social application such as Twitter or FaceBook becomes popular in recent years. In particular, Twitter increases the number of the users rapidly from 2009 as the place that users can tweet anything in 140 characters. In the area of social network analysis, the user network of Twitter is frequently analyzed. Haewoon, et.al.,[1] analyzed the Twitter user network from various point of view in 2009, and they show that the Twitter user network has some different feature from conventional social networks. Bong- won, et.al., also made a collection of 74 millions tweets in 2010, and investigated the influence that "retweet" gives for diffusion of the information. Such analysis not only reveal the unique characteristics of Twitter user network, but also make some networking service such as finding users who are similar to someone, or the recommendation of commodities by using tweet information. There are some analysis such as clustering which needs entire data of the network. However, since social networks are increasing day by day, it becomes impossible to obtain the entire network by crawling. As a solution of this problem, there is the network analysis called link prediction. This enables to predict true network from a given part of the network. If we use link prediction, we can recover the entire network from the network data which we already obtained, and apply some analysis such as clustering to predicted net- work, then we may get the approximate result of the analysis for the entire network. In our research, we implemented one of the link prediction algorithm named Link Propagation in X10, which is a parallel programming language. And evaluated its scalability and precision with Twitter user network data.
SepaRating: an approach to reputation computation based on rating separation in e-marketplace
Authors: Hyun-Kyo Oh, Yoohan Noh, Sang-Wook Kim, Sunju Park
Venue: WWW
Year: 2014

This paper proposes SepaRating, a novel mechanism that separates a buyer's rating on a transaction into two kinds of scores: seller's score and item's score. SepaRating provides the reputation of sellers correctly based on the seller's score by repetitive separations, which helps potential buyers to find more reliable sellers. We verify the effectiveness of SepaRating via a series of experiments.
Fluctuation and burst response in social media
Authors: Mizuki Oka, Yasuhiro Hashimoto, Takashi Ikegami
Venue: WWW
Year: 2014

A salient dynamic property of social media is bursting be- havior. In this paper, we study bursting behavior in relation to the structure of fluctuation, known as fluctuation-response relation, to reveal the origin of bursts. More specifically, we study the temporal relation between a preceding baseline fluctuation and the successive burst response using a frequency time series of 3,000 keywords on Twitter. We find three types of keyword time series in terms of the fluctuation-response relation. For the first type of keyword, the baseline fluctuation has a positive correlation with the burst size; as the preceding fluctuation increases, the burst size increases. These bursts are caused endogenously as a result of word-of-mouth interactions in a social network; the keyword is sensitive only to the internal context of the system. For the second type, there is a critical threshold in the fluctuation value up to which a positive correlation is observed. Beyond this value, the size of the bursts becomes independent from the fluctuation size. Our analysis shows that this critical threshold emerges because the bursts in the time series are endogenous and exogenous. This type of keyword is sensitive to internal and external stimuli. The third type is mainly bursts caused by exogenous bursts. This type of keyword is mostly sensitive only to external stimuli. These results are useful for characterizing how excitable a keyword is on Twitter and could be used, for example, for marketing purposes.
Crowd vs. experts: nichesourcing for knowledge intensive tasks in cultural heritage
Authors: Jasper Oosterman, Alessandro Bozzon, Geert-Jan Houben, Archana Nottamkandath, Chris Dijkshoorn, Lora Aroyo, Mieke H. R. Leyssen, Myriam C. Traub
Venue: WWW
Year: 2014

The results of our exploratory study provide new insights to crowdsourcing knowledge intensive tasks. We designed and performed an annotation task on a print collection of the Rijksmuseum Amsterdam, involving experts and crowd workers in the domain-specific description of depicted flowers. We created a testbed to collect annotations from flower experts and crowd workers and analyzed these in regard to user agreement. The findings show promising results, demonstrating how, for given categories, nichesourcing can provide useful annotations by connecting crowdsourcing to domain expertise.
Learning to rank for joy
Authors: Claudia Orellana-Rodriguez, Wolfgang Nejdl, Ernesto Diaz-Aviles, Ismail Sengör Altingövde
Venue: WWW
Year: 2014

User-generated content is a growing source of valuable information and its analysis can lead to a better understanding of the users needs and trends. In this paper, we leverage user feedback about YouTube videos for the task of affective video ranking. To this end, we follow a learning to rank approach, which allows us to compare the performance of different sets of features when the ranking task goes beyond mere relevance and requires an affective understanding of the videos. Our results show that, while basic video features, such as title and tags, lead to effective rankings in an affective-less setup, they do not perform as good when dealing with an affective ranking task.
An analysis of duplicate on web extracted objects
Authors: Stefano Ortona
Venue: WWW
Year: 2014

Today the web has become the largest available source of information. The automatic extraction of structured data from web is a challenging problem that has been widely investigated. However, after the extraction process, the problem of identifying duplicates among the extracted web records must be solved in order to present clean data to the final user. This problem, also known as record linkage or record matching, has been of central interest for the database community; however, only few works have addressed this problem in the web context. In this paper we present web object matching, the problem of identifying duplicates among records extracted from the web.