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Googlization of everything
In the beginning, the World Wide Web was exciting and open to the point of anarchy, a vast and intimidating repository of unindexed confusion. Into this creative chaos came Google with its dazzling mission--\"To organize the world's information and make it universally accessible\"--and its much-quoted motto, \"Don't be Evil.\" In this provocative book, Siva Vaidhyanathan examines the ways we have used and embraced Google--and the growing resistance to its expansion across the globe. He exposes the dark side of our Google fantasies, raising red flags about issues of intellectual property and the much-touted Google Book Search. He assesses Google's global impact, particularly in China, and explains the insidious effect of Googlization on the way we think. Finally, Vaidhyanathan proposes the construction of an Internet ecosystem designed to benefit the whole world and keep one brilliant and powerful company from falling into the \"evil\" it pledged to avoid.
eBook
Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model
In this article, we use Google Flu Trends data together with a sequential surveillance model based on state-space methodology to track the evolution of an epidemic process over time. We embed a classical mathematical epidemiology model [a susceptible-exposed-infected-recovered (SEIR) model] within the state-space framework, thereby extending the SEIR dynamics to allow changes through time. The implementation of this model is based on a particle filtering algorithm, which learns about the epidemic process sequentially through time and provides updated estimated odds of a pandemic with each new surveillance data point. We show how our approach, in combination with sequential Bayes factors, can serve as an online diagnostic tool for influenza pandemic. We take a close look at the Google Flu Trends data describing the spread of flu in the United States during 2003–2009 and in nine separate U.S. states chosen to represent a wide range of health care and emergency system strengths and weaknesses. This article has online supplementary materials.
Journal Article
Google Cloud platform in action
Cloud services make it easy to get infrastructure in a flexible and on-demand way. While there are many cloud services to choose from, Google Cloud Platform offers unique services that let you focus on building powerful applications. Google Cloud Services in Action teaches readers to build and launch web applications that scale while leveraging the Google Cloud Platform. Readers begin with the basics, learning how cloud services work, and the specifics of the Google Cloud Platform. The book includes hands-on step-by-step instruction on deploying applications, handling large amounts of data, and much more. By the end, readers will know how to build, leverage, and deploy cloud-based applications so web applications get started more quickly, suffer fewer disasters, and require less maintenance.
Book
Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations
New sources of citation data have recently become available, such as Microsoft Academic, Dimensions, and the OpenCitations Index of CrossRef open DOI-to-DOI citations (COCI). Although these have been compared to the Web of Science Core Collection (WoS), Scopus, or Google Scholar, there is no systematic evidence of their differences across subject categories. In response, this paper investigates 3,073,351 citations found by these six data sources to 2,515 English-language highly-cited documents published in 2006 from 252 subject categories, expanding and updating the largest previous study. Google Scholar found 88% of all citations, many of which were not found by the other sources, and nearly all citations found by the remaining sources (89–94%). A similar pattern held within most subject categories. Microsoft Academic is the second largest overall (60% of all citations), including 82% of Scopus citations and 86% of WoS citations. In most categories, Microsoft Academic found more citations than Scopus and WoS (182 and 223 subject categories, respectively), but had coverage gaps in some areas, such as Physics and some Humanities categories. After Scopus, Dimensions is fourth largest (54% of all citations), including 84% of Scopus citations and 88% of WoS citations. It found more citations than Scopus in 36 categories, more than WoS in 185, and displays some coverage gaps, especially in the Humanities. Following WoS, COCI is the smallest, with 28% of all citations. Google Scholar is still the most comprehensive source. In many subject categories Microsoft Academic and Dimensions are good alternatives to Scopus and WoS in terms of coverage.
Journal Article
Chromebook for dummies
Overwhelmed by your Google Chromebook and its capabilities? LaFay takes the intimidation out of the technology, explains how to maximize the performance of your Chromebook, and helps you focus on having fun with your new device.
Book
Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition
Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.
Journal Article
COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study
Since the beginning of the novel coronavirus disease (COVID-19) outbreak, fake news and misleading information have circulated worldwide, which can profoundly affect public health communication.
We investigated online search behavior related to the COVID-19 outbreak and the attitudes of \"infodemic monikers\" (ie, erroneous information that gives rise to interpretative mistakes, fake news, episodes of racism, etc) circulating in Italy.
By using Google Trends to explore the internet search activity related to COVID-19 from January to March 2020, article titles from the most read newspapers and government websites were mined to investigate the attitudes of infodemic monikers circulating across various regions and cities in Italy. Search volume values and average peak comparison (APC) values were used to analyze the results.
Keywords such as \"novel coronavirus,\" \"China coronavirus,\" \"COVID-19,\" \"2019-nCOV,\" and \"SARS-COV-2\" were the top infodemic and scientific COVID-19 terms trending in Italy. The top five searches related to health were \"face masks,\" \"amuchina\" (disinfectant), \"symptoms of the novel coronavirus,\" \"health bulletin,\" and \"vaccines for coronavirus.\" The regions of Umbria and Basilicata recorded a high number of infodemic monikers (APC weighted total >140). Misinformation was widely circulated in the Campania region, and racism-related information was widespread in Umbria and Basilicata. These monikers were frequently searched (APC weighted total >100) in more than 10 major cities in Italy, including Rome.
We identified a growing regional and population-level interest in COVID-19 in Italy. The majority of searches were related to amuchina, face masks, health bulletins, and COVID-19 symptoms. Since a large number of infodemic monikers were observed across Italy, we recommend that health agencies use Google Trends to predict human behavior as well as to manage misinformation circulation in Italy.
Journal Article