Publications
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.
21.
González-Llinares, Javier; Font-Julian, Cristina I.; Orduña-Malea, Enrique
Universidades en Google: hacia un modelo de análisis multinivel del posicionamiento web académico Journal Article
In: Revista Española De Documentación Científica, vol. 43, no. 2, pp. e260, 2020, ISBN: 1988-4621.
Abstract | Links | BibTeX | Tags: Academic SEO, Cybermetrics, Evaluation Model, Google, Search Engines, Search Engines Optimization, SEO, Universities, Web positioning, Web Visibility, Webometrics
@article{nokey,
title = {Universidades en Google: hacia un modelo de análisis multinivel del posicionamiento web académico},
author = {Javier González-Llinares and Cristina I. Font-Julian and Enrique Orduña-Malea},
doi = {10.3989/redc.2020.2.1691},
isbn = {1988-4621},
year = {2020},
date = {2020-04-17},
urldate = {2020-04-17},
journal = {Revista Española De Documentación Científica},
volume = {43},
number = {2},
pages = {e260},
abstract = {Se propone un modelo de análisis del posicionamiento web de universidades basado en un vocabulario de palabras clave categorizadas según las distintas misiones universitarias, que se aplica a una universidad (Universitat Politècnica de València) para comprobar su idoneidad. A partir de un vocabulario de 164 palabras clave se construyeron 290 consultas web que fueron ejecutadas en Google, recopilando los 20 primeros resultados obtenidos para cada consulta. Los resultados confirman que las universidades obtienen un posicionamiento web variable en función de la dimensión vinculada a la consulta web y que las páginas web vinculadas a la docencia (especialmente Grados) son las que mejor posicionan, incluso para consultas web orientadas a investigación. Con todo, se observa un posicionamiento bajo no sólo para la UPV sino para las universidades públicas presenciales españolas (sólo el 27% del total de resultados en el Top 20 corresponde a alguna de estas universidades). Se concluye que el análisis multinivel es necesario para estudiar el posicionamiento web de las universidades y que el modelo propuesto es viable y escalable. No obstante, se han identificado ciertas limitaciones (dependencia del vocabulario utilizado y alta variabilidad de datos) que deben tenerse en cuenta en el diseño de este tipo de modelos de análisis.},
keywords = {Academic SEO, Cybermetrics, Evaluation Model, Google, Search Engines, Search Engines Optimization, SEO, Universities, Web positioning, Web Visibility, Webometrics},
pubstate = {published},
tppubtype = {article}
}
Se propone un modelo de análisis del posicionamiento web de universidades basado en un vocabulario de palabras clave categorizadas según las distintas misiones universitarias, que se aplica a una universidad (Universitat Politècnica de València) para comprobar su idoneidad. A partir de un vocabulario de 164 palabras clave se construyeron 290 consultas web que fueron ejecutadas en Google, recopilando los 20 primeros resultados obtenidos para cada consulta. Los resultados confirman que las universidades obtienen un posicionamiento web variable en función de la dimensión vinculada a la consulta web y que las páginas web vinculadas a la docencia (especialmente Grados) son las que mejor posicionan, incluso para consultas web orientadas a investigación. Con todo, se observa un posicionamiento bajo no sólo para la UPV sino para las universidades públicas presenciales españolas (sólo el 27% del total de resultados en el Top 20 corresponde a alguna de estas universidades). Se concluye que el análisis multinivel es necesario para estudiar el posicionamiento web de las universidades y que el modelo propuesto es viable y escalable. No obstante, se han identificado ciertas limitaciones (dependencia del vocabulario utilizado y alta variabilidad de datos) que deben tenerse en cuenta en el diseño de este tipo de modelos de análisis.
22.
Compés-López, Raúl; Font-Julian, Cristina I.; Orduña-Malea, Enrique
Has Robert Parker lost his hegemony as a prescriptor in the wine World? A preliminar inquiry through Twitter Book Chapter
In: de València, Universitat Politècnica (Ed.): 2018, ISBN: 9788490486894.
Abstract | Links | BibTeX | Tags: Robert Parker, Twitter, Web Data, Web Data Analysis, Webometrics, Wine Experts, Wine Industry, Wine Prescriptor
@inbook{nokey,
title = {Has Robert Parker lost his hegemony as a prescriptor in the wine World? A preliminar inquiry through Twitter},
author = {Raúl Compés-López and Cristina I. Font-Julian and Enrique Orduña-Malea},
editor = {Universitat Politècnica de València},
doi = {10.4995/CARMA2018.2018.8320},
isbn = {9788490486894},
year = {2018},
date = {2018-09-07},
series = {2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018).},
abstract = {he 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, Twitter, Web Data, Web Data Analysis, Webometrics, Wine Experts, Wine Industry, Wine Prescriptor},
pubstate = {published},
tppubtype = {inbook}
}
he 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).
23.
Contreras-Ochando, Lidia; Font-Julian, Cristina I.; Nieves, David; Martínez-Plumed, Fernando
How Data Science helps to build Smart Cities: València as a use case Book Chapter
In: Colomina, Begoña Cantó; Ferreira, Vanesa G. Lo Iacono (Ed.): Universitat Politècnica de València, 2018, ISBN: 978-84-09-02970-9.
Abstract | Links | BibTeX | Tags: Data Science, Machine Learning, Open Data, Smart Cities
@inbook{nokey,
title = {How Data Science helps to build Smart Cities: València as a use case},
author = {Lidia Contreras-Ochando and Cristina I. Font-Julian and David Nieves and Fernando Martínez-Plumed},
editor = {Begoña Cantó Colomina and Vanesa G. Lo Iacono Ferreira},
doi = {http://hdl.handle.net/10251/104672},
isbn = {978-84-09-02970-9},
year = {2018},
date = {2018-06-07},
urldate = {2018-06-07},
publisher = {Universitat Politècnica de València},
abstract = {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},
pubstate = {published},
tppubtype = {inbook}
}
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.
24.
Font-Julian, Cristina I.; Orduña-Malea, Enrique; Ontalba-Ruipérez, José-Antonio
Hit count estimate variability for website-specific queries in search engines: The case for rare disease association websites Journal Article
In: Aslib Journal of Information Management, vol. 70, no. 2, pp. 192-213, 2018, ISSN: 2050-3806.
Abstract | Links | BibTeX | Tags: Bing, Google, Hit Count Estimates, Rare Diseases, Search Engines, Website Page Count
@article{nokey,
title = {Hit count estimate variability for website-specific queries in search engines: The case for rare disease association websites},
author = {Cristina I. Font-Julian and Enrique Orduña-Malea and José-Antonio Ontalba-Ruipérez},
doi = {10.1108/AJIM-10-2017-0226},
issn = {2050-3806},
year = {2018},
date = {2018-02-07},
urldate = {2018-02-07},
journal = {Aslib Journal of Information Management},
volume = {70},
number = {2},
pages = {192-213},
abstract = {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 = {Bing, Google, Hit Count Estimates, Rare Diseases, Search Engines, Website Page Count},
pubstate = {published},
tppubtype = {article}
}
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.
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.
25.
Orduña-Malea, Enrique; Font-Julian, Cristina I.; Ontalba-Ruipérez, José-Antonio
From Universities to Private Companies: A Measurable Route of Linkedin Users Book Chapter
In: Lloret, Nuria; Cabrera, Marga (Ed.): pp. 127-150, IGI Global, 2017, ISBN: 9781522509189.
Abstract | Links | BibTeX | Tags:
@inbook{nokey,
title = {From Universities to Private Companies: A Measurable Route of Linkedin Users},
author = {Enrique Orduña-Malea and Cristina I. Font-Julian and José-Antonio Ontalba-Ruipérez},
editor = {Nuria Lloret and Marga Cabrera},
doi = {10.4018/978-1-5225-0917-2.ch009},
isbn = {9781522509189},
year = {2017},
date = {2017-02-07},
urldate = {2017-02-07},
pages = {127-150},
publisher = {IGI Global},
abstract = {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 = {},
pubstate = {published},
tppubtype = {inbook}
}
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.
26.
Serrano-Cobos, Jorge; Font-Julian, Cristina I.; de Dios, J. González; Aleixandre-Benavent, R.
Comunicación Científica (XXXVIII). Cómo hacer una estrategia "social media" para pediatras (V). Academic SEO a través de Google Scholar. Journal Article
In: Acta Pediátrica, vol. 74, no. 10, pp. 266-272, 2016, ISBN: 978-84-9905-261-8.
Abstract | BibTeX | Tags: Academic SEO, Google Scholar, Metrics, Scientific Impact, Scientific Visibility, Search Engines Optimization
@article{nokey,
title = {Comunicación Científica (XXXVIII). Cómo hacer una estrategia "social media" para pediatras (V). Academic SEO a través de Google Scholar.},
author = {Jorge Serrano-Cobos and Cristina I. Font-Julian and J. González de Dios and R. Aleixandre-Benavent},
isbn = {978-84-9905-261-8},
year = {2016},
date = {2016-07-07},
urldate = {2016-07-07},
journal = {Acta Pediátrica},
volume = {74},
number = {10},
pages = {266-272},
abstract = { La interacción de los científicos en sus conductas de búsqueda de información científica ha cambiado en los últimos años, con la utilización cada vez más de Google Scholar como fuente principal de indagación. Por tanto, para mejorar el impacto científico es necesario entender cómo mejorar la encontrabilidad de la producción científica en este buscador, por lo que en este artículo se desglosa una selección de factores y acciones de comunicación que llevar a cabo con el fin de mejorar la presencia online de los pediatras y apoyar el impacto de su producción digital.},
keywords = {Academic SEO, Google Scholar, Metrics, Scientific Impact, Scientific Visibility, Search Engines Optimization},
pubstate = {published},
tppubtype = {article}
}
La interacción de los científicos en sus conductas de búsqueda de información científica ha cambiado en los últimos años, con la utilización cada vez más de Google Scholar como fuente principal de indagación. Por tanto, para mejorar el impacto científico es necesario entender cómo mejorar la encontrabilidad de la producción científica en este buscador, por lo que en este artículo se desglosa una selección de factores y acciones de comunicación que llevar a cabo con el fin de mejorar la presencia online de los pediatras y apoyar el impacto de su producción digital.
27.
Contreras-Ochando, Lidia; Font-Julian, Cristina I.; Contreras-Ochando, Francisco; Ferri, Cèsar
AirVLC: An application for real-time forecasting urban air pollution Proceedings
Proceedings of the 2nd International Workshop of Mining Urban Data, 2015.
Abstract | BibTeX | Tags: Data Science, Machine Learning, Open Data, Smart Cities
@proceedings{nokey,
title = {AirVLC: An application for real-time forecasting urban air pollution},
author = {Lidia Contreras-Ochando and Cristina I. Font-Julian and Francisco Contreras-Ochando and Cèsar Ferri},
year = {2015},
date = {2015-07-11},
abstract = {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 lim- ited to the area where the measurement stations are located. The application employs regression models able to predict the levels of four differ- ent pollutants (CO, NO, PM2.5, NO2) in three different locations of the city. These models are trained using features that represent traffic inten- sity, 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 pollu- tants. 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 pollu- tion map of the city of Valencia. The application also includes a warning system for sending no- tifications to users when a nearby risk pollution concentration is detected. },
howpublished = {Proceedings of the 2nd International Workshop of Mining Urban Data},
keywords = {Data Science, Machine Learning, Open Data, Smart Cities},
pubstate = {published},
tppubtype = {proceedings}
}
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 lim- ited to the area where the measurement stations are located. The application employs regression models able to predict the levels of four differ- ent pollutants (CO, NO, PM2.5, NO2) in three different locations of the city. These models are trained using features that represent traffic inten- sity, 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 pollu- tants. 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 pollu- tion map of the city of Valencia. The application also includes a warning system for sending no- tifications to users when a nearby risk pollution concentration is detected.
28.
Contreras-Ochando, Lidia; Font-Julian, Cristina I.; Morillo, Paulina; Vallejo, Diego
TransparencyScience. Return on research investment, where do the funds go? Proceedings
iConference 2015 Proceedings, 2015.
Abstract | BibTeX | Tags: Citizen Participation, Crowdfunding, Data mining, Data Visualization, Open Data, Open Government, Public Investment, Social Network, Transparency
@proceedings{nokey,
title = {TransparencyScience. Return on research investment, where do the funds go?},
author = {Lidia Contreras-Ochando and Cristina I. Font-Julian and Paulina Morillo and Diego Vallejo},
year = {2015},
date = {2015-06-17},
urldate = {2015-06-17},
abstract = {The web application www.transparencyscience.es has been created in order 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. To achieve these goals, www.transparencyscience.es collects and process data from several open sources of the Spanish government. It uses different kinds of content and visualizations to facilitate the understanding of the Spanish public investment in science. It encourages citizen participation in three ways: a voting system; commenting system for collecting citizens’ opinion in natural language; and finally, acrowdfunding system for proposed actions/petitions/etc.
The purposes of this paper are both to explain why we have designed and created the web application www.transparencyscience.es, and to describe how it works. It also reveals some added value in comparison with other projects in Spain.},
howpublished = {iConference 2015 Proceedings},
keywords = {Citizen Participation, Crowdfunding, Data mining, Data Visualization, Open Data, Open Government, Public Investment, Social Network, Transparency},
pubstate = {published},
tppubtype = {proceedings}
}
The web application www.transparencyscience.es has been created in order 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. To achieve these goals, www.transparencyscience.es collects and process data from several open sources of the Spanish government. It uses different kinds of content and visualizations to facilitate the understanding of the Spanish public investment in science. It encourages citizen participation in three ways: a voting system; commenting system for collecting citizens’ opinion in natural language; and finally, acrowdfunding system for proposed actions/petitions/etc.
The purposes of this paper are both to explain why we have designed and created the web application www.transparencyscience.es, and to describe how it works. It also reveals some added value in comparison with other projects in Spain.
The purposes of this paper are both to explain why we have designed and created the web application www.transparencyscience.es, and to describe how it works. It also reveals some added value in comparison with other projects in Spain.