Companion Proceedings of the The Web Conference 2018最新文献

{"title":"SoBigData: Social Mining & Big Data Ecosystem","authors":"F. Giannotti, R. Trasarti, Kalina Bontcheva, Valerio Grossi","doi":"10.1145/3184558.3186205","DOIUrl":"https://doi.org/10.1145/3184558.3186205","url":null,"abstract":"One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by \"big data\". The research community uses the SoBigData facilities as a \"secure digital wind-tunnel\" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: (i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; (ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; (iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121534196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"P3RPQ: Pregel-Based Parallel Provenance-Aware Regular Path Query Processing on Large RDF Graphs","authors":"Yueqi Xin, Bingyi Zhang, Xin Wang, Qiang Xu, Zhiyong Feng","doi":"10.1145/3184558.3186908","DOIUrl":"https://doi.org/10.1145/3184558.3186908","url":null,"abstract":"This paper proposes a novel method for answering Pregel-based Parallel Provenance-aware Regular Path Queries (P3RPQ) on large RDF graphs. Our method is developed using the Pregel framework, which utilizes Glushkov automata to keep track of the matching process of RPQs in parallel. Meanwhile, four optimization strategies are devised, which can reduce the response time of the basic algorithm dramatically and overcome the counting paths problem to some extent. The experiments are conducted to verify the performance of our algorithms on both synthetic and real-world datasets.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122950126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Hierarchical Table of Contents Generation for Educational Videos","authors":"Debabrata Mahapatra, Ragunathan Mariappan, Vaibhav Rajan","doi":"10.1145/3184558.3186336","DOIUrl":"https://doi.org/10.1145/3184558.3186336","url":null,"abstract":"The number of freely available online educational videos from universities and other organizations is growing rapidly. Accurate indexing and summarization are essential for efficient search, recommendation and effective consumption of videos. In this paper, we describe a new method of automatically creating a hierarchical table of contents for a video. It provides a summary of the video content along with a textbook--like facility for nonlinear navigation and search through the video. Our multimodal approach combines new methods for shot level video segmentation and for hierarchical summarization. Empirical results demonstrate the efficacy of our approach on many educational videos.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121637919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterising Dataset Search Queries","authors":"Emilia Kacprzak, Laura M. Koesten, J. Tennison, E. Simperl","doi":"10.1145/3184558.3191597","DOIUrl":"https://doi.org/10.1145/3184558.3191597","url":null,"abstract":"The amount of data generated and published on the web is increasing rapidly, but search for structured data on the web still presents challenges. In this paper we explore dataset search by analysing queries specifically generated for this work through a crowdsourcing experiment and comparing them to a search log analysis of queries on data portals. The change in search environment together with the task we gave people altered the generated queries. We found that queries issued in our experiment were much longer than search queries for datasets on data portals. They further contained seven times more mentions of geospatial and of temporal information and are more likely to be structured as questions. These insights can be used to tailor search functionalities to the particular information needs and characteristics of dataset search.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132556646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MusicLynx: Exploring Music Through Artist Similarity Graphs","authors":"Alo Allik, F. Thalmann, M. Sandler","doi":"10.1145/3184558.3186970","DOIUrl":"https://doi.org/10.1145/3184558.3186970","url":null,"abstract":"MusicLynx is a web application for music discovery that enables users to explore an artist similarity graph constructed by linking together various open public data sources. It provides a multifaceted browsing platform that strives for an alternative, graph-based representation of artist connections to the grid-like conventions of traditional recommendation systems. Bipartite graph filtering of the Linked Data cloud, content-based music information retrieval, machine learning on crowd-sourced information and Semantic Web technologies are combined to analyze existing and create new categories of music artists through which they are connected. The categories can uncover similarities between artists who otherwise may not be immediately associated: for example, they may share ethnic background or nationality, common musical style or be signed to the same record label, come from the same geographic origin, share a fate or an affliction, or have made similar lifestyle choices. They may also prefer similar musical keys, instrumentation, rhythmic attributes, or even moods their music evokes. This demonstration is primarily meant to showcase the graph-based artist discovery interface of MusicLynx: how artists are connected through various categories, how the different graph filtering methods affect the topology and geometry of linked artists graphs, and ways in which users can connect to external services for additional content and information about objects of their interest.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"91 28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132925592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to Assess and Rank User-Generated Content on Web","authors":"Elaheh Momeni, Claire Cardie, N. Diakopoulos","doi":"10.1145/3184558.3186239","DOIUrl":"https://doi.org/10.1145/3184558.3186239","url":null,"abstract":"User-generated content (UGC) on the Web, especially on social media platforms, facilitates the association of additional information with digital resources and online social topics and it can provide valuable supplementary content. However, UGC varies in quality and, consequently, raises the challenge of how to maximize its utility for a variety of end-users, in particular in the age of misinformation. This study aims to provide researchers and Web data curators with answers to the following questions: (1) What are the existing approaches and methods for assessing and ranking UGC (2) What features and metrics have been used successfully to assess and predict UGC value across a range of application domains This survey is composed of a systematic review of approaches for assessing and ranking UGC: results obtained by identifying and comparing methodologies within the context of short text-based UGC on the Web. This survey categorizes existing assessment and ranking approaches into four framework types and discusses the main contributions and considerations of each type. Furthermore, it suggests a need for further experimentation and encourages the development of new approaches for the assessment and ranking.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133185666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SenHint: A Joint Framework for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints","authors":"Yanyan Wang, Qun Chen, Xin Liu, Ahmed Murtadha, Zhanhuai Li, Wei Pan, Hailong Liu","doi":"10.1145/3184558.3186980","DOIUrl":"https://doi.org/10.1145/3184558.3186980","url":null,"abstract":"The state-of-the-art techniques for aspect-level sentiment analysis focus on feature modeling using a variety of deep neural networks (DNN). Unfortunately, their practical performance may fall short of expectations due to semantic complexity of natural languages. Motivated by the observation that linguistic hints (e.g. explicit sentiment words and shift words) can be strong indicators of sentiment, we present a joint framework, SenHint, which integrates the output of deep neural networks and the implication of linguistic hints into a coherent reasoning model based on Markov Logic Network (MLN). In SenHint, linguistic hints are used in two ways: (1) to identify easy instances, whose sentiment can be automatically determined by machine with high accuracy; (2) to capture implicit relations between aspect polarities. We also empirically evaluate the performance of SenHint on both English and Chinese benchmark datasets. Our experimental results show that SenHint can effectively improve accuracy compared with the state-of-the-art alternatives.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133448122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling Formation of Online Temporal Communities","authors":"Isa Inuwa-Dutse","doi":"10.1145/3178876.3186577","DOIUrl":"https://doi.org/10.1145/3178876.3186577","url":null,"abstract":"Contemporary social media networks can be viewed as a break to the early two-step flow model in which influential individuals act as intermediaries between the media and the public for information diffusion. Today's social media platforms enable users to both generate and consume online contents. Users continuously engage and disengage in discussions with varying degrees of interaction leading to formation of distinct online communities. Such communities are often formed at high-level either based on metadata, such as hashtags on Twitter, or popular content triggered by few influential users. These online communities often do not reflect true connectivity and lack the cohesiveness of traditional communities. In this study, we investigate real-time formation of temporal communities on Twitter. We aim at defining both high and low levels connections and to reveal the magnitude of clustering cohesion on temporal basis. Inspired by a real-life event center sitting arrangement scenario, the proposed method aims to cluster users into distinct and cohesive online temporal communities. Membership to a community relies on intrinsic tweet properties to define similarity as the basis for interaction networks. The proposed method can be useful for local event monitoring and clique-based marketing among other applications.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133729866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satire or Fake News: Social Media Consumers' Socio-Demographics Decide","authors":"Michele Bedard, Chianna Schoenthaler","doi":"10.1145/3184558.3188732","DOIUrl":"https://doi.org/10.1145/3184558.3188732","url":null,"abstract":"Ever since the surprising results from the 2016 U.S. presidential race, the subject of Fake News in our worldwide media consumption has grown steadily. On a smaller scale, mainstream media have taken a closer look at the relatively narrow genre of satirical news content. Ed Koltonski of Kent State, defines satirical news as designed specifically to entertain the reader, usually with irony or wit, to critique society or a social figure and invoke change or reform. Using field experiment, survey and focus group methods we sought to determine if media consumers' ability to differentiate between satirical news and fake news is tied to socio-demographic factors. We found that age, education, sex, and political affiliation predict understanding of \"fake news\" and satire. Furthermore, the ability to identify different types of misinformation when presented with screen shots from social media posts appears to be related to these variables. Focus group comments were also analyzed to gain a richer perspective on how participants interpreted the SMS screen shots. Using our primary research, we seek to determine if there is a correlation between social media consumers understanding of the difference between satirical news versus fake news and their varying socio-demographic factors","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134363519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural Novelty and Diversity in Link Prediction","authors":"Javier Sanz-Cruzado, Sofía M. Pepa, P. Castells","doi":"10.1145/3184558.3191576","DOIUrl":"https://doi.org/10.1145/3184558.3191576","url":null,"abstract":"Link prediction has mainly been addressed as an accuracy-targeting problem in the social networks field. We discuss different perspectives on the problem considering other dimensions and effects that the link prediction methods may have on the social network where they are applied. Specifically, we consider the structural effects the prediction can have if the predicted links are added to the network. We consider further utility dimensions beyond prediction accuracy, namely novelty and diversity. We discuss the adaptation, for this purpose, of specific network, novelty and diversity metrics from social network analysis, recommender systems, and information retrieval.","PeriodicalId":235572,"journal":{"name":"Companion Proceedings of the The Web Conference 2018","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134106570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}