Proceedings of the 2017 ACM on Web Science Conference最新文献_第3页

Using Twitter Data to Estimate the Relationship between Short-term Mobility and Long-term Migration 利用Twitter数据估计短期流动与长期迁移的关系

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091496

L. Fiorio, G. Abel, Jixuan Cai, E. Zagheni, Ingmar Weber, Guillermo Vinué

{"title":"Using Twitter Data to Estimate the Relationship between Short-term Mobility and Long-term Migration","authors":"L. Fiorio, G. Abel, Jixuan Cai, E. Zagheni, Ingmar Weber, Guillermo Vinué","doi":"10.1145/3091478.3091496","DOIUrl":"https://doi.org/10.1145/3091478.3091496","url":null,"abstract":"Migration estimates are sensitive to definitions of time interval and duration. For example, when does a tourist become a migrant? As a result, harmonizing across different kinds of estimates or data sources can be difficult. Moreover in countries like the United States, that do not have a national registry system, estimates of internal migration typically rely on survey data that can require over a year from data collection to publication. In addition, each survey can ask only a limited set questions about migration (e.g., where did you live a year ago? where did you live five years ago?). We leverage a sample of geo-referenced Twitter tweets for about 62,000 users, spanning the period between 2010 and 2016, to estimate a series of US internal migration flows under varying time intervals and durations. Our findings, expressed in terms of 'migration curves', document, for the first time, the relationships between short-term mobility and long-term migration. The results open new avenues for demographic research. More specifically, future directions include the use of migration curves to produce probabilistic estimates of long-term migration from short-term (and vice versa) and to nowcast mobility rates at different levels of spatial and temporal granularity using a combination of previously published American Community Survey data and up-to-date data from a panel of Twitter users.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130072858","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}

引用次数: 42

Predicting Trust Relations Within a Social Network: A Case Study on Emergency Response 社会网络中的信任关系预测:以应急响应为例

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091494

Nikhita Vedula, S. Parthasarathy, V. Shalin

引用次数: 11

A Large Labeled Corpus for Online Harassment Research 面向网络骚扰研究的大型标注语料库

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091509

J. Golbeck, Zahra Ashktorab, Rashad O. Banjo, Alexandra Berlinger, Siddharth Bhagwan, C. Buntain, Paul Cheakalos, Alicia A. Geller, Quint Gergory, R. Gnanasekaran, Raja Rajan Gunasekaran, K. Hoffman, Jenny Hottle, Vichita Jienjitlert, Shivika Khare, Ryan Lau, Marianna J. Martindale, Shalmali Naik, Heather L. Nixon, P. Ramachandran, Kristine M. Rogers, Lisa Rogers, Meghna Sardana Sarin, Gaurav Shahane, Jayanee Thanki, Priyanka Vengataraman, Zijian Wan, D. Wu

引用次数: 170

Developing Ontologies and Web-based Data Management System for Additive Manufacturing Processes 为增材制造过程开发本体和基于web的数据管理系统

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3098863

Congrui Li, B. Chu, Charles Parslow, J. Samuel, P. Fox

引用次数: 3

Algorithmic Fairness in Online Information Mediating Systems 网络信息中介系统中的算法公平性

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3098864

A. Koene, Elvira Perez, S. Ceppi, Michael Rovatsos, Helena Webb, Menisha Patel, M. Jirotka, Giles Lane

引用次数: 9

An Ontology for a Polymer Nanocomposite Community Data Resource 聚合物纳米复合材料社区数据资源本体

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3098866

Vipula Rawte, Jamie McCusker, He Zhao, L. Brinson, Wei Chen, L. Schadler, D. McGuinness

引用次数: 0

Studying Personality through the Content of Posted and Liked Images on Twitter 通过推特上发布和喜欢的图片内容来研究个性

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091522

Sharath Chandra Guntuku, Weisi Lin, J. Carpenter, W. Ng, L. Ungar, Daniel Preotiuc-Pietro

引用次数: 48

Exploring Web Archives Through Temporal Anchor Texts 通过时间锚文本探索网络档案

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091500

Helge Holzmann, W. Nejdl, Avishek Anand

{"title":"Exploring Web Archives Through Temporal Anchor Texts","authors":"Helge Holzmann, W. Nejdl, Avishek Anand","doi":"10.1145/3091478.3091500","DOIUrl":"https://doi.org/10.1145/3091478.3091500","url":null,"abstract":"Web archives have been instrumental in digital preservation of the Web and provide great opportunity for the study of the societal past and evolution. These Web archives are massive collections, typically in the order of terabytes and petabytes. Due to this, search and exploration of archives has been limited as full-text indexing is both resource and computationally expensive. We identify that for typical access methods to archives, which are navigational and temporal in nature, we do not always require indexing full-text. Instead, meaningful text surrogates like anchor texts already go a long way in providing meaningful solutions and can act as reasonable entry points to exploring Web archives. In this paper, we present a new approach to searching Web archives based on temporal link graphs and corresponding anchor texts. Departing from traditional informational intents, we show how temporal anchor texts can be effective in answering queries beyond purely navigational intents, like finding the most central webpages of an entity in a given time period. We propose indexing methods and a temporal retrieval model based on anchor texts. Further, we discuss several interesting search results as well as one experiment in which we demonstrate how such results can be integrated in a data processing workflow to scale up to thousands of pages. In this analysis we were able to replicate results reported by an offline study, showing that restaurant prices indeed increased in Germany when the Euro was introduced as Europe's currency.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122481576","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}

引用次数: 25

Analyzing the Keystroke Dynamics of Web Identifiers Web标识符的击键动力学分析

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091482

Andrew G. West

{"title":"Analyzing the Keystroke Dynamics of Web Identifiers","authors":"Andrew G. West","doi":"10.1145/3091478.3091482","DOIUrl":"https://doi.org/10.1145/3091478.3091482","url":null,"abstract":"Web identifiers such as usernames, hashtags, and domain names serve important roles in online navigation, communication, and community building. Therefore the entities that choose such names must ensure that end-users are able to quickly and accurately enter them in applications. Uniqueness requirements, a desire for short strings, and an absence of delimiters often constrain this name selection process. To gain perspective on the speed and correctness of name entry, we crowdsource the typing of 51,000+ web identifiers. Surface level analysis reveals, for example, that typing speed is generally a linear function of identifier length. Examining keystroke dynamics at finer granularity proves more interesting. First, we identify features predictive of typing time/accuracy, finding: (1) the commonality of character bi-grams inside a name, and (2) the degree of ambiguity when tokenizing a name - to be most indicative. A machine-learning model built over 10 such features exhibits moderate predictive capability. Second, we evaluate our hypothesis that users subconsciously insert pauses in their typing cadence where text delimiters (e.g., spaces) would exist, if permitted. The data generally supports this claim, suggesting its application alongside algorithmic tokenization methods, and possibly in name suggestion frameworks.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122769215","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}

引用次数: 2

Characterizing Regional and Behavioral Device Variations Across the Twitter Timeline: A Longitudinal Study 在推特时间轴上表征区域和行为设备差异:一项纵向研究

Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-06-25 DOI: 10.1145/3091478.3091498

Laura Cruz-Albrecht, Jiejun Xu, Kang-Yu Ni, Tsai-Ching Lu

{"title":"Characterizing Regional and Behavioral Device Variations Across the Twitter Timeline: A Longitudinal Study","authors":"Laura Cruz-Albrecht, Jiejun Xu, Kang-Yu Ni, Tsai-Ching Lu","doi":"10.1145/3091478.3091498","DOIUrl":"https://doi.org/10.1145/3091478.3091498","url":null,"abstract":"Physical devices, such as smartphones and laptops, provide the key interface through which users engage with the social media world. Yet despite the broad range of devices used on social media platforms, relatively little is known about how usage varies on a device to device level. In this work, we use a 10 sample of Twitter data spanning two consecutive years and encompassing 365.98 million users to perform a longitudinal, measurement-driven analysis of five prevalent device types - Android, iPhone-iOS, BlackBerry, other mobile devices, and nonmobile devices - across the time period. We study the global and regional usage patterns, as well as the regional distribution, of devices; investigate differences and similarities in behavioral patterns across devices with respect to tweet sentiment, daytime usage patterns, and feature usage (such as mentions, URLs, hashtags) over time; and quantify the level of \"device homophily\" (i.e., assortativity) within the Twitter device network. Our results reveal that key variations exist among these device groups, in addition to notable similarities. To the best of our knowledge, this is the first large-scale longitudinal analysis of various distinct Twitter devices.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126138663","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}

引用次数: 1