The Googlization of Everything
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4€€€€An Encyclopedia with Breaking News
Wikipedia @ 20 • ::Wikipedia @ 20 4 An Encyclopedia with Breaking News Brian Keegan Published on: Oct 15, 2020 Updated on: Nov 16, 2020 License: Creative Commons Attribution 4.0 International License (CC-BY 4.0) Wikipedia @ 20 • ::Wikipedia @ 20 4 An Encyclopedia with Breaking News Wikipedia’s response to the September 11 attacks profoundly shaped its rules and identity and illuminated a new strategy for growing the project by coupling the supply and demand for information about news. Wikipedia’s breaking news collaborations offer lessons for hardening other online platforms against polarization, disinformation, and other sociotechnical sludge. The web was a very different place for news in the United States between 2001 and 2006. The hanging chads from the 2000 presidential election, the spectacular calamity of 9/11, the unrepentant lies around Operation Iraqi Freedom, and the campy reality television featuring Donald Trump were all from this time. The burst of the dot-com bubble and corporate malfeasance of companies like Enron dampened entrepreneurial spirits, news publishers were optimistically sharing their stories online without paywalls, and blogging was heralded as the future of technology-mediated accountability and participatory democracy. “You” was Time Magazine’s Person of the Year in 2006 because “Web 2.0” platforms like YouTube, MySpace, and Second Life had become tools for “bringing together the small contributions of millions of people and making them matter.”1 Wikipedia was a part of this primordial soup, predating news-feed-mediated engagement, recommender-driven polarization, politicized content moderation, and geopolitical disinformation campaigns. From very early in its history, Wikipedia leveraged the supply and demand for information about breaking news and current events into strategies that continue to sustain this radical experiment in online peer production. -
2020 Vision: Info Pro Skills for a New Decade
2020 Vision: Info Pro Skills for a New Decade Search Skills for Today’s Info Pros and Thriving in the New Information Landscape Presented by: Mary Ellen Bates Bates Information Services BatesInfo.com Presented for: Initiative Fortbildung e.V. 9 and 10 May 2019 2020 VISION DAY 1: Search Skills for Today’s Info Pros INSIDE A SEARCHER’S MIND: BRINGING THE DETECTIVE TO THE SEARCH ..........................................1 TECHNIQUES OF A DETECTIVE ......................................................................................................................2 DIFFERENT SEARCH APPROACHES.................................................................................................................3 GETTING CREATIVE....................................................................................................................................5 WHAT’S NEW (OR AT LEAST USEFUL) WITH GOOGLE: TIPS AND TOOLS FOR TODAY’S GOOGLE .........6 GOOGLE TRICKS........................................................................................................................................6 SEARCHING THE DEEP WEB / GREY LITERATURE ................................................................................8 SEARCH STRATEGIES FOR GREY LITERATURE....................................................................................................9 SOME GREY LIT/DEEP WEB TOOLS.............................................................................................................10 GLEANING INSIGHT FROM SOCIAL MEDIA........................................................................................12 -
Modeling and Analyzing Latency in the Memcached System
Modeling and Analyzing Latency in the Memcached system Wenxue Cheng1, Fengyuan Ren1, Wanchun Jiang2, Tong Zhang1 1Tsinghua National Laboratory for Information Science and Technology, Beijing, China 1Department of Computer Science and Technology, Tsinghua University, Beijing, China 2School of Information Science and Engineering, Central South University, Changsha, China March 27, 2017 abstract Memcached is a widely used in-memory caching solution in large-scale searching scenarios. The most pivotal performance metric in Memcached is latency, which is affected by various factors including the workload pattern, the service rate, the unbalanced load distribution and the cache miss ratio. To quantitate the impact of each factor on latency, we establish a theoretical model for the Memcached system. Specially, we formulate the unbalanced load distribution among Memcached servers by a set of probabilities, capture the burst and concurrent key arrivals at Memcached servers in form of batching blocks, and add a cache miss processing stage. Based on this model, algebraic derivations are conducted to estimate latency in Memcached. The latency estimation is validated by intensive experiments. Moreover, we obtain a quantitative understanding of how much improvement of latency performance can be achieved by optimizing each factor and provide several useful recommendations to optimal latency in Memcached. Keywords Memcached, Latency, Modeling, Quantitative Analysis 1 Introduction Memcached [1] has been adopted in many large-scale websites, including Facebook, LiveJournal, Wikipedia, Flickr, Twitter and Youtube. In Memcached, a web request will generate hundreds of Memcached keys that will be further processed in the memory of parallel Memcached servers. With this parallel in-memory processing method, Memcached can extensively speed up and scale up searching applications [2]. -
Ciência De Dados Na Ciência Da Informação
Ciência da Informação v. 49 n.3 set./dez. 2020 ISSN 0100-1965 eISSN 1518-8353 Edição especial temática Special thematic issue / Edición temática especial Ciência de dados na ciência da informação Data science in Information Sience Ciencia de datos en la Ciencia de la Información Instituto Brasileiro de Informação em Ciência e Tecnologia (Ibict) Diretoria Indexação Cecília Leite Oliveira Ciência da Informação tem seus artigos indexados ou resumidos. Coordenação-Geral de Pesquisa e Desenvolvimento de Novos Produtos (CGNP) Bases Internacionais Anderson Luis Cambraia Itaborahy Directory of Open Access Journals - DOAJ. Paschal Thema: Science de L’Information, Documentation. Library and Coordenação-Geral de Pesquisa e Manutenção de Produtos Consolidados (CGPC) Information Science Abstracts. PAIS Foreign Language Bianca Amaro Index. Information Science Abstracts. Library and Literature. Páginas de Contenido: Ciências de la Información. Coordenação-Geral de Tecnologias de Informação e Informática EDUCACCION: Notícias de Educación, Ciencia y Cultura (CGTI) Tiago Emmanuel Nunes Braga Iberoamericanas. Referativnyi Zhurnal: Informatika. ISTA Information Science & Technology Abstracts. LISTA Library, Coordenação de Ensino e Pesquisa, Ciência e Tecnologia da Information Science & Technology Abstracts. SciELO Informação (COEPPE) Scientific Electronic Library On-line. Latindex – Sistema Gustavo Saldanha Regional de Información em Línea para Revistas Científicas Coordenação de Planejamento, Acompanhamento e Avaliação de América Latina el Caribe, España y Portugal, México. (COPAV) INFOBILA: Información Bibliotecológica Latinoamericana. José Luis dos Santos Nascimento Indexação em Bases de Dados Nacionais Coordenação de Administração (COADM) Reginaldo de Araújo Silva Portal de Periódicos LivRe – Portal de Periódicos de Livre Acesso. Comissão Divisão de Editoração Científica Nacional de Energia Nuclear (Cnen). Portal Periódicos Ramón Martins Sodoma da Fonseca da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes). -
A Longitudinal Study of Google Play App Actual Installations
1 A Longitudinal Study of Google Play Rahul Potharaju∗, Mizanur Rahman†, Bogdan Carbunar† ∗Cloud and Information Services Lab, Microsoft †Florida International University Abstract—The difficulty of large scale monitoring of app to use app markets as a launch pad for their malware [26], markets affects our understanding of their dynamics. This is [28], [33], [20]. particularly true for dimensions such as app update frequency, Contributions. In this article we seek to shed light on the control and pricing, the impact of developer actions on app popularity, as well as coveted membership in top app lists. In this dynamics of Google Play, the most popular Android app paper we perform a detailed temporal analysis on two datasets market. We report results from one of the first characteristic we have collected from the Google Play Store, one consisting studies on Google Play, using real-world time series data. To of 160,000 apps and the other of 87,223 newly released apps. this end, we have developed iMarket, a prototype app market We have monitored and collected data about these apps over crawling system. We have used iMarket to collect data from more than 6 months. Our results show that a high number of these apps have not been updated over the monitoring interval. more than 470,000 Google Play apps, and daily monitor more Moreover, these apps are controlled by a few developers that than 160,000 apps, over more than 6 months. dominate the total number of app downloads. We observe that We use this data to study two key aspects of Google Play. -
Getting the Most out of Information Systems: a Manager's Guide (V
Getting the Most Out of Information Systems A Manager's Guide v. 1.0 This is the book Getting the Most Out of Information Systems: A Manager's Guide (v. 1.0). This book is licensed under a Creative Commons by-nc-sa 3.0 (http://creativecommons.org/licenses/by-nc-sa/ 3.0/) license. See the license for more details, but that basically means you can share this book as long as you credit the author (but see below), don't make money from it, and do make it available to everyone else under the same terms. This book was accessible as of December 29, 2012, and it was downloaded then by Andy Schmitz (http://lardbucket.org) in an effort to preserve the availability of this book. Normally, the author and publisher would be credited here. However, the publisher has asked for the customary Creative Commons attribution to the original publisher, authors, title, and book URI to be removed. Additionally, per the publisher's request, their name has been removed in some passages. More information is available on this project's attribution page (http://2012books.lardbucket.org/attribution.html?utm_source=header). For more information on the source of this book, or why it is available for free, please see the project's home page (http://2012books.lardbucket.org/). You can browse or download additional books there. ii Table of Contents About the Author .................................................................................................................. 1 Acknowledgments................................................................................................................ -
Corpora: Google Ngram Viewer and the Corpus of Historical American English
EuroAmerican Journal of Applied Linguistics and Languages E JournALL Volume 1, Issue 1, November 2014, pages 48 68 ISSN 2376 905X DOI - - www.e journall.org- http://dx.doi.org/10.21283/2376905X.1.4 - Exploring mega-corpora: Google Ngram Viewer and the Corpus of Historical American English ERIC FRIGINALa1, MARSHA WALKERb, JANET BETH RANDALLc aDepartment of Applied Linguistics and ESL, Georgia State University bLanguage Institute, Georgia Institute of Technology cAmerican Language Institute, New York University, Tokyo Received 10 December 2013; received in revised form 17 May 2014; accepted 8 August 2014 ABSTRACT EN The creation of internet-based mega-corpora such as the Corpus of Contemporary American English (COCA), the Corpus of Historical American English (COHA) (Davies, 2011a) and the Google Ngram Viewer (Cohen, 2010) signals a new phase in corpus-based research that provides both novice and expert researchers immediate access to a variety of online texts and time-coded data. This paper explores the applications of these corpora in the analysis of academic word lists, in particular, Coxhead’s (2000) Academic Word List (AWL). Coxhead (2011) has called for further research on the AWL with larger corpora, noting that learners’ use of academic vocabulary needs to address for the AWL to be useful in various contexts. Results show that words on the AWL are declining in overall frequency from 1990 to the present. Implications about the AWL and future directions in corpus-based research utilizing mega-corpora are discussed. Keywords: GOOGLE N-GRAM VIEWER, CORPUS OF HISTORICAL AMERICAN ENGLISH, MEGA-CORPORA, TREND STUDIES. ES La creación de megacorpus basados en Internet, tales como el Corpus of Contemporary American English (COCA), el Corpus of Historical American English (COHA) (Davies, 2011a) y el Visor de Ngramas de Google (Cohen, 2010), anuncian una nueva fase en la investigación basada en corpus, pues proporcionan, tanto a investigadores noveles como a expertos, un acceso inmediato a una gran diversidad de textos online y datos codificados con time-code. -
The Ongoing Evolution of Search Engines There’S More to Web Searches Than Google
TTeecchhnnololooggyy The Ongoing Evolution of Search Engines There’s More to Web Searches than Google BY DAN GIANCATERINO ast autumn I wrote an article for on the subject have been posted since you Wolfram|Alpha a local newspaper discussing a submitted your original query. crop of a dozen next-generation Everything about Wolfram|Alpha Web search engines. Given that Twitter Search has a raw, unfiltered feeling (www.wolframalpha.com) is different, from GoogleL handles about two-thirds of search about it. It’s a great service to use if you the spelling of its name – in computer-speak, queries in the U.S., I’m always amazed that want to get information quickly on a devel- that line in the middle is called a “pipe” – to the people even bother trying to compete with oping story. Twitter engineers tell how one syntax it uses and the data it searches. It isn’t them. More new search tools have launched day they saw a large uptick in tweets about even a search engine; it calls itself a “Compu- in the intervening nine months. I want to an earthquake seconds before their office tational Knowledge Engine.” You can’t use cover four of them in this article: Twitter started shaking. For them, Twitter became the site to find pictures of adorable puppies, Search, Wolfram|Alpha, Microsoft’s Bing, an early-warning service. Recently I used or the latest news on your favorite celebrity, and Hunch. Twitter Search to confirm a rumor I heard or even cheap flights. Wolfram|Alpha’s all via e-mail that local sportscaster Gary Papa about crunching the numbers. -
From Notice-And-Takedown to Notice-And-Delist: Implementing Google Spain
FINAL KUCZERAWY AND AUSLOOS 4.5.16 (DO NOT DELETE) 5/9/16 11:51 AM FROM NOTICE-AND-TAKEDOWN TO NOTICE-AND-DELIST: IMPLEMENTING GOOGLE SPAIN ALEKSANDRA KUCZERAWY AND JEF AUSLOOS* INTRODUCTION ...................................................................................... 220 A. Media Storm ........................................................................... 220 B. Internet Law Crossroads ........................................................ 221 I. HOW DID WE GET HERE? .............................................................. 223 A. Google Spain .......................................................................... 223 1. Facts of the Case ............................................................... 223 2. Scope ............................................................................... 224 3. Right to be Delisted .......................................................... 224 4. Balancing .......................................................................... 225 B. Implementation of Google Spain ............................................ 226 1. Online Form ...................................................................... 226 2. Advisory Council .............................................................. 227 3. Article 29 Working Party ................................................. 228 4. Emerging Body of Case Law ........................................... 228 II. HAVE WE MET BEFORE? ............................................................... 229 A. Dazed and Confused: Criticism of Google -
Copyright Reform in the EU: Grappling with the Google Effect
Copyright Reform in the EU: Grappling with the Google Effect Annemarie Bridy Sweeping changes are coming to copyright law in the European Union. Following four years of negotiations, the European Parliament in April 2019 approved the final text of the Digital Single Market Directive (DSMD).1 EU member states now have two years to transpose its provisions intodomestic law. The new directive, which is the most substantial change to EU copyright law in a generation, contains provisions for enhancing cross-border access to content available through digital subscription services, enabling new uses of copyrighted works for education and research, and, most controversially, ‘clarifying’ the role of online services in the distribution of copyrighted works. The provisions associated with the last of these goals—Article 15 (the ‘link tax’) and Article 17 (‘upload filters’) take aim directly at two services operated by Google: Google News and YouTube. Article 15 is intended to provide remuneration for press publishers when snippets of their articles are displayed by search engines and news aggregators.2 Article 17, which this article takes for its subject, is intended to address the so-called ‘value gap’—the music industry’s longstanding complaint that YouTube undercompensates music rightholders for streams of user videos containing claimed copyrighted content.3 The text of the DSMD nowhere mentions YouTube, but anyone versed in the political economy of digital copyright knows that Article 17 was purpose-built to make YouTube pay. The important questions to ask in the wake of Article 17 are who else will pay—and in what ways. This article offers a focused examination of Article 17 as public law created to settle a private score between the music industry and YouTube. -
The State of the News: Texas
THE STATE OF THE NEWS: TEXAS GOOGLE’S NEGATIVE IMPACT ON THE JOURNALISM INDUSTRY #SaveJournalism #SaveJournalism EXECUTIVE SUMMARY Antitrust investigators are finally focusing on the anticompetitive practices of Google. Both the Department of Justice and a coalition of attorneys general from 48 states and the District of Columbia and Puerto Rico now have the tech behemoth squarely in their sights. Yet, while Google’s dominance of the digital advertising marketplace is certainly on the agenda of investigators, it is not clear that the needs of one of the primary victims of that dominance—the journalism industry—are being considered. That must change and change quickly because Google is destroying the business model of the journalism industry. As Google has come to dominate the digital advertising marketplace, it has siphoned off advertising revenue that used to go to news publishers. The numbers are staggering. News publishers’ advertising revenue is down by nearly 50 percent over $120B the last seven years, to $14.3 billion, $100B while Google’s has nearly tripled $80B to $116.3 billion. If ad revenue for $60B news publishers declines in the $40B next seven years at the same rate $20B as the last seven, there will be $0B practically no ad revenue left and the journalism industry will likely 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 disappear along with it. The revenue crisis has forced more than 1,700 newspapers to close or merge, the end of daily news coverage in 2,000 counties across the country, and the loss of nearly 40,000 jobs in America’s newsrooms. -
Natural Language Processing 2018 Highlights
NLP 2018 Highlights By Elvis Saravia 1 Table of Contents Introduction ............................................................................................................................................ 4 Research ................................................................................................................................................. 5 Reinforcement Learning ...................................................................................................................... 5 Sentiment Analysis and Related Topics ................................................................................................ 7 AI Ethics and Security .......................................................................................................................... 9 Clinical NLP and ML ........................................................................................................................... 12 Computer Vision ................................................................................................................................ 15 Deep Learning and Optimization ........................................................................................................ 17 Transfer Learning for NLP .................................................................................................................. 19 AI Generalization ............................................................................................................................... 20 Explainability and Interpretability