OpenRaiser/Intern-Atlas huggingface dataset:
News Feature Extraction for Events on Social Network Platforms
Authors: Peiquan Jin, Lin Mu, Lizhou Zheng, Jie Zhao, Lihua Yue
Venue: WWW
Year: 2017

Microblog-based social network platforms like Twitter and Sina Weibo have been important sources for news event extraction. However, existing works on microblog event extraction, which usually use keywords, entities, or selected microblogs to represent events, are not able to extract details of an event. Based on the view of news report, an event should present detailed news features, i.e., when, where, who, whom, and what. Such news features are helpful for conducting deeply data analysis on microblogs, e.g., competitor monitoring and public crisis discovery. However, the challenge is that the news features of an event on microblogs are usually distributed among different posts because of the short-text property of microblogs. This is much different from extracting news events from Web news pages that usually contain most details of an event. In this paper, we propose a new framework to extract events together with their news features from microblogs. We first extract a set of events from microblogs. Each event is represented as a distribution over four kinds of named entities including location, person name, organization, and time. In addition, the type of each event, i.e., location-related, person-related, or organization-related, is determined by a machine-learning method. In order to obtain the news features of an event, we propose an event-clustering approach that puts together all the relevant events into a cluster. For each cluster, we propose different algorithms to extract the news features of the event reported in the cluster. We conduct experiments on two microblog datasets crawled from a commercial microblogging platform to evaluate the performance of the proposed framework. The results suggest the effectiveness of our proposal.
Taming the Data Deluge to Unravel the Mysteries of the Universe
Authors: Melanie Johnston-Hollitt
Venue: WWW
Year: 2017

Modern Astrophysics is one of the most data intensive research fields in the world and is driving many of the required innovations in the "big data" space. Foremost in astronomy in terms of data generation is radio astronomy, and in the last decade an increase in global interest and investment in the field had led to a large number of new or upgraded facilities which are each currently generating petabytes of data per annum. The peak of this so-called 'radio renaissance' will be the Square Kilometre Array (SKA) -- a global observatory designed to uncover the mysteries of the Universe. The SKA will create the highest resolution, fastest frame rate movie of the evolving Universe ever and in doing so will generate 160 terrabytes of data a day, or close to 5 zettabytes of data per annum.
Risk Quantification of Metabolic Syndrome with Quantum Particle Swarm Optimisation
Authors: Habeebah A. Kakudi, Chu Kiong Loo, Kitsuchart Pasupa
Venue: WWW
Year: 2017

Metabolic syndrome (MetS) is a combination of interrelated risk factors associated with an increased risk of developing type II diabetes Mellitus (T2DM), stroke and cardiovascular diseases (CVD). The economic, social and medical burden coupled with increased morbidity of the aforementioned diseases makes their prevention an active research area. Currently, the traditional method of MetS diagnosis is based on dichotomised definitions provided by various expert health organisations. However, this method is laced with the indetermination of MetS in individuals with borderline risk factor values due to a binary diagnosis and the assumption of equal weighting for all risk factors during diagnosis. The purpose of this paper is to examine the use of the MetS areal similarity degree risk analysis based on weighted radar charts comprising of diagnostic thresholds and risk factor results of an individual. We further enhance this risk quantification method by applying quantum particle swarm optimization to derive the weights. The proposed risk quantification was carried out using a sample of 528 individuals from an examination survey conducted between 2007 and 2014 in Serbia. The results are evaluated with the traditional dichotomised method of MetS diagnosis, in this case the joint interim statement (JIS). The results obtained showed that the proposed risk quantification method outperformed the dichotomised method at diagnosing MetS even in individuals who present risk factor examination values at the threshold borderlines.
PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data
Authors: Maulik R. Kamdar, Mark A. Musen
Venue: WWW
Year: 2017

Integrated approaches for pharmacology are required for the mechanism-based predictions of adverse drug reactions that manifest due to concomitant intake of multiple drugs. These approaches require the integration and analysis of biomedical data and knowledge from multiple, heterogeneous sources with varying schemas, entity notations, and formats. To tackle these integrative challenges, the Semantic Web community has published and linked several datasets in the Life Sciences Linked Open Data (LSLOD) cloud using established W3C standards. We present the PhLeGrA platform for Linked Graph Analytics in Pharmacology in this paper. Through query federation, we integrate four sources from the LSLOD cloud and extract a drug-reaction network, composed of distinct entities. We represent this graph as a hidden conditional random field (HCRF), a discriminative latent variable model that is used for structured output predictions. We calculate the underlying probability distributions in the drug-reaction HCRF using the datasets from the U.S. Food and Drug Administration's Adverse Event Reporting System. We predict the occurrence of 146 adverse reactions due to multiple drug intake with an AUROC statistic greater than 0.75. The PhLeGrA platform can be extended to incorporate other sources published using Semantic Web technologies, as well as to discover other types of pharmacological associations.
I Would Not Plant Apple Trees If the World Will Be Wiped: Analyzing Hundreds of Millions of Behavioral Records of Players During an MMORPG Beta Test
Authors: Ah Reum Kang, Jeremy Blackburn, Haewoon Kwak, Huy Kang Kim
Venue: WWW
Year: 2017

In this work, we use player behavior during the closed beta test of the MMORPG ArcheAge as a proxy for an extreme situation: at the end of the closed beta test, all user data is deleted, and thus, the outcome (or penalty) of players' in-game behaviors in the last few days loses its meaning. We analyzed 270 million records of player behavior in the 4th closed beta test of ArcheAge. Our findings show that there are no apparent pandemic behavior changes, but some outlierswere more likely to exhibit anti-social behavior (e.g., player killing). We also found that contrary to the reassuring adage that ``Even if I knew the world would go to pieces tomorrow, I would still plant my apple tree,'' players abandoned character progression, showing a drastic decrease in quest completion, leveling, and ability changes at the end of the beta test.
Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling
Authors: Vibhor Kanojia, Hideyuki Maeda, Riku Togashi, Sumio Fujita
Venue: WWW
Year: 2017

Link Prediction using Knowledge graph embedding projects symbolic entities and relations into low dimensional vector space, thereby learning the semantic relations between entities.
J-Force: Forced Execution on JavaScript
Authors: Kyungtae Kim, I Luk Kim, Chung Hwan Kim, Yonghwi Kwon, Yunhui Zheng, Xiangyu Zhang, Dongyan Xu
Venue: WWW
Year: 2017

Web-based malware equipped with stealthy cloaking and obfuscation techniques is becoming more sophisticated nowadays. In this paper, we propose J-FORCE, a crash-free forced JavaScript execution engine to systematically explore possible execution paths and reveal malicious behaviors in such malware. In particular, J-FORCE records branch outcomes and mutates them for further explorations. J-FORCE inspects function parameter values that may reveal malicious intentions and expose suspicious DOM injections. We addressed a number of technical challenges encountered. For instance, we keep track of missing objects and DOM elements, and create them on demand. To verify the efficacy of our techniques, we apply J-FORCE to detect Exploit Kit (EK) attacks and malicious Chrome extensions. We observe that J-FORCE is more effective compared to the existing tools.
Twitter Content Eliciting User Engagement: A Case Study on Australian Organisations
Authors: Sunghwan Mac Kim, Kyo Kageura, James McHugh, Surya Nepal, Cécile Paris, Bella Robinson, Ross Sparks, Stephen Wan
Venue: WWW
Year: 2017

Social media has become an important communication platform for all kinds of organisations, ranging from government departments to companies. When using social media, organisations are often keen to maximise engagements from their target audiences, that is, create posts to which their audience will react by, for example, replying, retweeting or liking. In this paper, we investigate the factors that characterise the posts with which an audience engages. While other work has looked at such factors for Twitter posts at large, we account for the effect of the organisation type. We find that the type of organisation (e.g., financial vs. telecommunications) has an impact on the characteristics of the posts associated with audience engagement. This is important as it can provide guidance to organisations on content creation strategies to maximise engagement.
Probabilistic Visitor Stitching on Cross-Device Web Logs
Authors: Sungchul Kim, Nikhil Kini, Jay Pujara, Eunyee Koh, Lise Getoor
Venue: WWW
Year: 2017

Personalization -- the customization of experiences, interfaces, and content to individual users -- has catalyzed user growth and engagement for many web services. A critical prerequisite to personalization is establishing user identity. However the variety of devices, including mobile phones, appliances, and smart watches, from which users access web services from both anonymous and logged-in sessions poses a significant obstacle to user identification. The resulting entity resolution task of establishing user identity across devices and sessions is commonly referred to as ``visitor stitching.'' We introduce a general, probabilistic approach to visitor stitching using features and attributes commonly contained in web logs. Using web logs from two real-world corporate websites, we motivate the need for probabilistic models by quantifying the difficulties posed by noise, ambiguity, and missing information in deployment. Next, we introduce our approach using probabilistic soft logic (PSL), a statistical relational learning framework capable of capturing similarities across many sessions and enforcing transitivity. We present a detailed description of model features and design choices relevant to the visitor stitching problem. Finally, we evaluate our PSL model on binary classification performance for two real-world visitor stitching datasets. Our model demonstrates significantly better performance than several state-of-the-art classifiers, and we show how this advantage results from collective reasoning across sessions.
Constructing and Evaluating a Novel Crowdsourcing-based Paraphrased Opinion Spam Dataset
Authors: Seongsoon Kim, Seongwoon Lee, Donghyeon Park, Jaewoo Kang
Venue: WWW
Year: 2017

Opinion spam, intentionally written by spammers who do not have actual experience with services or products, has recently become a factor that undermines the credibility of information online. In recent years, studies have attempted to detect opinion spam using machine learning algorithms. However, limitations of gold-standard spam datasets still prove to be a major obstacle in opinion spam research. In this paper, we introduce a novel dataset called Paraphrased OPinion Spam (POPS), which contains a new type of review spam that imitates real human opinions using crowdsourcing. To create such a seemingly truthful review spam dataset, we asked task participants to paraphrase truthful reviews, and include factual information and domain knowledge in their reviews. The classification experiments and semantic analysis results show that our POPS dataset most linguistically and semantically resembles truthful reviews. We believe that our new deceptive opinion spam dataset will help advance opinion spam research.
WimNet: Vision Search for Web Logs
Authors: Sungchul Kim, Sana Malik, Nedim Lipka, Eunyee Koh
Venue: WWW
Year: 2017

With the growing popularity of mobile devices, user web logs are more heterogeneous than ever, across an increased number of devices and websites. As a result, identifying users with similar usage patterns within these large sets of web logs is increasingly challenging and critical for personalization and user experience in many areas, from recommender systems to digital marketing.
Type-based Semantic Optimization for Scalable RDF Graph Pattern Matching
Authors: HyeongSik Kim, Padmashree Ravindra, Kemafor Anyanwu
Venue: WWW
Year: 2017

Scalable query processing relies on early and aggressive determination and pruning of query-irrelevant data. Besides the traditional space-pruning techniques such as indexing, type-based optimizations that exploit integrity constraints defined on the types can be used to rewrite queries into more efficient ones. However, such optimizations are only applicable in strongly-typed data and query models which make it a challenge for semi-structured models such as RDF. Consequently, developing techniques for enabling typebased query optimizations will contribute new insight to improving the scalability of RDF processing systems.
Evolution of Smart Homes for the Elderly
Authors: Bethany Kon, Alex Lam, Jonathan H. Chan
Venue: WWW
Year: 2017

Smart home technology provides benefits for the elderly in six primary categories: safety, health and nutrition, physical activity, personal hygiene and care, social engagement, and leisure. Safety is about detecting and mitigating, if not removing, hazards from the user's environment. Social engagement relates to the smart home functions that allow the elderly to combat social isolation, such as by connecting the elderly with friends and family. Leisure activities are about how a smart home can allow users to spend their free time. Physical Activity relates to the concept of movement from the user, such as having them engage in non-sedentary activities. Nutrition and Health is related to the monitoring of a user's state of health. Personal hygiene and care encompasses the ways that a smart home can improve the user's well-being and assist in his/her daily activities. This workshop paper will present existing technologies in the aforementioned fields and highlight areas where development is lacking. In addition, an evaluation on past smart home designs is conducted to determine whether they fulfill the six proposed primary categories.
Exploring the Impact of Trolls on Activity Dynamics in Real-World Collaboration Networks
Authors: Philipp Koncar, Simon Walk, Denis Helic, Markus Strohmaier
Venue: WWW
Year: 2017

When new users join social networking websites, they often form collaboration ties with existing users, which in turn may result in some level of activity on the site. However, for various reasons, new users often fail to create such ties and their contributions to the system's overall activity remain insignificant. For example, on Question and Answering portals, such as the StackExchange network, users collaborate to find the best answers for a given set of questions. However, the intentions of new users are highly diverse. While the contributions of most users positively impact the evolution of a community, other participants might just try to steer discussions off-topic or purposely generate discord. To better understand such malicious behavior, it is important to model and quantify the impact of such users on the overall activity in collaboration networks. In this paper we simulate and investigate the influence of trolls---users who intentionally contribute detrimental content---on the total activity of several different StackExchange instances, Semantic MediaWikis and Subreddits. The contributions of this paper are three-fold. First, we simulate activity dynamics in the context of trolls in online collaboration networks. Second, we analyze and quantify the impact of trolls on the levels of activity in these networks. Third, we discuss our results and put them into a real-world context.
Random Walk-based Beneficial Collaborators Recommendation Exploiting Dynamic Research Interests and Academic Influence
Authors: Xiangjie Kong, Huizhen Jiang, Teshome Megersa Bekele, Wei Wang, Zhenzhen Xu
Venue: WWW
Year: 2017

It is laborious for researchers to find proper collaborators who can provide researching guidance besides collaborating. Beneficial Collaborators (BCs), researchers who have a high academic level and relevant topics, can genuinely help researchers to enrich their research. Though many efforts have made to develop collaborator recommendation, most of existing works have mainly focused on recommending most possible collaborators with no intention to recommend specifically the BCs. In this paper, we propose the Beneficial Collaborator Recommendation (BCR) model that considers the dynamic research interest of researcher's and academic level of collaborators to recommend the BCs. First, we run the LDA model on the abstract of researchers' publications in each year for topic clustering. Second, we fix generated topic distribution matrix by a time function to fit interest dynamic transformation. Third, we compute the similarity between the collaboration candidate's feature matrix and the target researcher. Finally, we combine the similarity and influence in collaborators network to fix rank score and mine the candidates with high academic level and academic social impact. BCR generates the topN BCs recommendation. Extensive experiments on a dataset with citation network demonstrate that BCR performs better in terms of precision, recall, F1 score and the recommendation quality compared to baseline methods.
Distillation of Knowledge from the Research Literature on Alzheimer's Dementia
Authors: Wutthipong Kongburan, Mark H. Chignell, Jonathan H. Chan
Venue: WWW
Year: 2017

Many countries are aging societies. Since abilities generally deteriorate with age, technologies can assist older adults in their daily life. Loss of cognitive status is particularly severe in cases of dementia, with around 70% (according to Alzheimers.net) of dementia cases involving Alzheimer's Dementia (AD), a progressive and currently incurable disease. There is considerable research on AD with thousands of relevant publications being added to the PubMed online database every year. The knowledge incorporated in this large body of work is spread across hundreds of thousands of pages of text, making it difficult to distill and mobilize that knowledge in terms of treatments and guidelines. Text mining technology may assist in distilling knowledge from the vast corpus of research literature on Alzheimer's dementia. In this paper, we apply the Named Entity Recognition (NER) system, a text mining (TM) method used to group words into classes, in order to extract useful information from free texts. We present findings concerning how well NER can extract information from a corpus of AD research publications.
Analyzing Uber's Ride-sharing Economy
Authors: Farshad Kooti, Mihajlo Grbovic, Luca Maria Aiello, Nemanja Djuric, Vladan Radosavljevic, Kristina Lerman
Venue: WWW
Year: 2017

Uber is a popular ride-sharing application that matches people who need a ride (or riders) with drivers who are willing to provide it using their personal vehicles. Despite its growing popularity, there exist few studies that examine large-scale Uber data, or in general the factors affecting user participation in the sharing economy. We address this gap through a study of the Uber market that analyzes large-scale data covering 59 million rides which spans a period of 7 months. The data were extracted from email receipts sent by Uber collected on Yahoo servers, allowing us to examine the role of demographics (e.g., age and gender) on participation in the ride-sharing economy. In addition, we evaluate the impact of dynamic pricing (i.e., surge pricing) and income on both rider and driver behavior. We find that the surge pricing does not bias Uber use towards higher income riders. Moreover, we show that more homophilous matches (e.g., riders to drivers of a similar age) can result in higher driver ratings. Finally, we focus on factors that affect retention and use information from earlier rides to accurately predict which riders or drivers will become active Uber users.
Understanding Short-term Changes in Online Activity Sessions
Authors: Farshad Kooti, Karthik Subbian, Winter A. Mason, Lada A. Adamic, Kristina Lerman
Venue: WWW
Year: 2017

Online activity is characterized by regularities such as diurnal and weekly patterns, reflecting human circadian rhythms and work and leisure schedules. Using data from the online social networking site Facebook, we uncover temporal patterns at a much smaller time scale: within individual sessions. Longer sessions have different characteristics than shorter ones, and this distinction is already visible in the first minute of a person's session activity. This allows us to predict the ultimate length of his or her session and how much content the person will see. The length of the session and other factors are in turn predictive of when the individual will return. Within a session, the amount of time a person spends on different kinds of content depends on both the person's demographic attributes, such as age and the number of Facebook friends, and the length of the time elapsed since the start of the session. We also find that liking and commenting is very non-uniformly distributed between sessions. Predictions of session duration and activity can potentially be leveraged to more efficiently cache content, especially to mobile devices in places with poor communications infrastructure, in order to improve user online experience.
Chromium based Framework to Include Gaze Interaction in Web Browser
Authors: Chandan Kumar, Raphael Menges, Daniel Müller, Steffen Staab
Venue: WWW
Year: 2017

Enabling Web interaction by non-conventional input sources like eyes has great potential to enhance Web accessibility. In this paper, we present a Chromium based inclusive framework to adapt eye gaze events in Web interfaces. The framework provides more utility and control to develop a full-featured interactive browser, compared to the related approaches of gaze-based mouse and keyboard emulation or browser extensions. We demonstrate the framework through a sophisticated gaze driven Web browser, which effectively supports all browsing operations like search, navigation, bookmarks, and tab management.
Using Retweet Information as a Feature to Classify Messages Contents
Authors: David Burth Kurka, Alan Godoy, Fernando J. Von Zuben
Venue: WWW
Year: 2017

We investigate the use of machine learning algorithms to classify content published in Online Social Networks using as input solely user interaction data, instead of the actual message content. During a period of six months, we monitored and gathered data from users interacting with news messages on Twitter, creating thousands of information diffusion processes. The data set presented regular patterns on how messages were spread over the network by users, depending on its content, so we could build classifiers to predict the topic of a message using as input only information of how it was diffused. Thus, we demonstrate the explanatory power of user behavior data on identifying content present in Social Networks, proposing techniques for topic classification that can be used to assist traditional content identification strategies (such as natural language or image processing) in challenging contexts, or be applied in scenarios with limited information access.