I do not have a lot of publications yet, but I hope that the ones I have right now are of interest for you. If you have any questions or are interested in commenting my work I will be happy to receive any comments.
Los principales objetivos de este trabajo son determinar el grado de internacionalización de la producción científica española sobre vino y comprobar si la aplicación de métricas alternativas ofrece una visión más o menos internacional que la proporcionada por las métricas tradicionales. A tal fin se han identificado y analizado 551 documentos indizados en Scopus relativos a la industrial de vino, de los que 118 presentan evidencias de impacto alternativas. Se concluye que el nivel de internacionalización del impacto de la producción española sobre vino en Scopus es bajo (solamente el 9.3% de los documentos citantes no tiene un co-autor con filiación española). Twitter muestra un nivel mayor de internacionalización, donde España representa sólo el 12.7% del total de Tweets con menciones a alguno de los documentos producidos.
Keywords: Industria del vino; Bibliometría; Impacto; Altmetrics; Scopus
The wine market has become globalized, and the increase and diversification of supply increases the demand for information by consumers. Due to the size of its market and the capacity of its companies, the United States has a growing weight in the global wine business, and critics and specialized American media have a great influence on consumers and producers around the world. Therefore, the main objective of this paper is to determine, through a set of web metrics, the influence model of the main wine critics in the United States. Twitter has become one of the most influential networks; therefore, to perform the analysis data is extracted from the Twitter accounts of Randall Grahm (@RandallGrahm), Jon Thorsen (@ReverseWineSnob), Amy Lieberfarb (@amylieberfarb), Kelly Mitchell (@KellyMitchell), and Eric Asimov (@EricAsimov). The tools used to extract data have been the Twitter API, FollowerWonk, Klout and Majestic. The data collected for each account is their age, followers, friends, number of tweets, retweets and likes, and was gathered during the period June – September of 2018. Once collected, the data is cleaned and prepared for the analysis.
Keywords: Wine, Twitter, Influence, Networks
This work introduces a new procedure to typify the behaviour of Twitter accounts belonging to Triple-Helix institutions through the identification and classification of users mentioned over time. As a case study, this presentation covers 3 institutions (1 university, 1 government body, and 1 private company) in a regional environment (Catalonia) along 7 months of Twitter activity. Users mentioned by each account where classified according to its nature (Govern, University, Private Company, Communication company, Individual account, Events, and Others). The results show little interrelation between user categories, with a very unalike productivity and a differentiate rate of users mentioned by published tweets. Consequently, each Twitter account displays a very characteristic pattern. Further research is being carried out in order to compile more institutions, improve the users’ taxonomy and gather more information on each Tweet published.
Keywords: Triple Helix, Twitter, Mentions, Networks, Spain, Corporate Communications
The aim of this work is to determine to what extent Robert Parker has lost his influence as a prescriber in the world of wine through a webometric analysis based on a comparative analysis of Parker’s web influence and that of a competitor who represents an anthitetical vision of the world of wine (Alice Feiring). To do this, we carried out a comparative analysis for Parker’s (@wine_advocate) and Alice Feiring’s (@alicefeiring) official Twitter accounts, including a broad set of metrics (productivity, age, Social Activity, number of followees, etc.), paying special attention to specific followers’ features (age, gender, location, and bios text). The results show that Parker’s twitter profile exhibits an overall higher impact, which denotes not only a different online strategy but also a high level of engagement and popularity. The low level of shared followers by Parker and Feiring (1,898 users) offer prima facie evidence of an online gap between these followers, which can indicate the existence of a divided group of supporters corresponding with the visions that Parker and Feiring represent. Finally, special features are notice for Feiring in gender (more women followers), language (more English-speaking followers) and country (more followers from the United States).
Keywords: Robert Parker, Wine industry, Wine prescriptor, Webmetrics, Web data analysis, Twitter data.
Purpose – The purpose of this paper is to determine the effect of the chosen search engine results page (SERP) on the website-specific hit count estimation indicator.
Design/methodology/approach – A sample of 100 Spanish rare disease association websites is analysed, obtaining the website-specific hit count estimation for the first and last SERPs in two search engines (Google and Bing) at two different periods in time (2016 and 2017).
Findings – It has been empirically demonstrated that there are differences between the number of hits returned on the first and last SERP in both Google and Bing. These differences are significant when they exceed a threshold value on the first SERP.
Research limitations/implications – Future studies considering other samples, more SERPs and generating different queries other than website page count (<site>) would be desirable to draw more general conclusions on the nature of quantitative data provided by general search engines.
Practical implications – Selecting a wrong SERP to calculate some metrics (in this case, website-specific hit count estimation) might provide misleading results, comparisons and performance rankings. The empirical data suggest that the first SERP captures the differences between websites better because it has a greater discriminating power and is more appropriate for webometric longitudinal studies.
Social implications – The findings allow improving future quantitative webometric analyses based on website-specific hit count estimation metrics in general search engines.
Originality/value – The website-specific hit count estimation variability between SERPs has been empirically analysed, considering two different search engines (Google and Bing), a set of 100 websites focussed on a similar market (Spanish rare diseases associations), and two annual samples, making this study the most exhaustive on this issue to date.
Keywords: Google, Search engines, Bing, Hit count estimates, Rare diseases, Website page count
The high degree of datification and connectivity embedded in a Smart City demands tools and mechanisms for data manipulation, knowledge extraction and representation that facilitate the extraction of meaningful insights. Clearly, Data Science can make enormous contributions to the development of Smart Cities, especially when it comes to gather and process information, combined with the capabilities of machine learning. In this regard, this paper discusses the use of Data Science methodologies and machine learning techniques to Smart City management aspects such as infrastructures, public safety and health, citizens’ empowerment, transportation, etc. and presents a number of practical cases in the context of Smart Cities in València, Spain.
Keywords: Data Science, Machine Learning, Open Data, Smart Cities
In publishing their education background together with the professional experience, users make LinkedIn a privileged web source for understanding “University-Industry” connections. Precisely, the main goal of this study is to test LinkedIn as a valid source for analyses oriented to the quantification of the university-industry interactions. To this end, the authors propose two different procedures (method A: direct through the URL mentions between LinkedIn profiles; and Method B: indirect through the information from LinkedIn University Pages), comparing them against the direct procedure based on URL mentions between official websites (Method C). To do this, the authors have selected the whole Spanish academic system. The results show that method A is unusable yet due to the low web connectivity between LinkedIn profiles, while method B provides reliable though too volatile data that complements method C, which reveal in turn relations of different nature.
Keywords: LinkedIn, webometrics, web, connections, analysis, mentions.
The scientists’ search behavior in their quest for scientific information has shift in recent years, increasing the use of Google Scholar as a main source of inquiry. Therefore, it is imperative to better understand how is possible to improve the findability of scientific production within this search engine. In the article, a selection of factors and communication actions are presented, so as to implement them in order to improve the online presence of pediatricians, supporting the online impact of their digital scientific production.
Keywords: Google Scholar, altmetrics, scientific impact, scientific visibility, scientific search engines optimization, ASEO.
This paper presents Airvlc, an application for producing real-time urban air pollution forecasts for the city of Valencia in Spain. Although many cities provide air quality data, in many cases, this information is presented with significant delays (three hours for the city of Valencia) and it is limited to the area where the measurement stations are located. The application employs regression models able to predict the levels of four different pollutants (CO, NO, PM2.5, NO2) in three different locations of the city. These models are trained using features that represent traffic intensity, persistence of pollutants and meteorological parameters such as wind speed and temperature. We compare different learning techniques to get the better performance in the prediction of pollutants. According to our experiments, ensembles of decision trees (Random Forest) outperforms the rest of methods in almost all of our tests. Airvlc incorporates the best regression models and, by a distance-weighted combination of the predictions, is able to generate a real-time pollution map of the city of Valencia. The application also includes a warning system for sending notifications to users when a nearby risk pollution concentration is detected.
Keywords: pollution, forecast, machine learning, big data, open data, valencia, air, quality, traffic
The aim of this project is to provide reliable information about public investment in science, in order to allow citizens to exercise their rights: to be informed in a transparent way, to control their government’s actions and to bring their ideas to guide the country’s policies on public investment in science. The web application Transparency Science has been developed to cover these needs. It presents the topics and the amount of investment in science by collecting and processing the data from several open sources of the Spanish government. Different visualizations facilitate the understanding of the return of investment in science by citizens. Their participation are promoted through three ways: the first, a voting system; the second, a commenting system for collecting citizens’ opinion in natural language; finally, a crowdfunding system for proposed actions/petitions/etc, driven by the citizen himself, based on the detected topics with the feedback system. The application will use the most popular social networks to spread the proposals and promote the participation of the community.
Keywords: social network, vote system, crowdfunding, public investment, open data, open government, citizen participation, transparency, data visualization, data mining.