Predicting referendum results in the Big Data Era

In addressing the challenge of Big Data Analytics, what has been of notable significance is the analysis of online search traffic data in order to analyze and predict human behavior. Over the last decade, since the establishment of the most popular such tool, Google Trends, the use of online data has been proven valuable in various research fields, including -but not limited to- medicine, economics, politics, the environment, and behavior. In the field of politics, given the inability of poll agencies to always well approximate voting intentions and results over the past years, what is imperative is to find new methods of predicting elections and referendum outcomes. This paper aims at presenting a methodology of predicting referendum results using Google Trends; a method applied and verified in six separate occasions: the 2014 Scottish Referendum, the 2015 Greek Referendum, the 2016 UK Referendum, the 2016 Hungarian Referendum, the 2016 Italian Referendum, and the 2017 Turkish Referendum. Said referendums were of importance for the respective country and the EU as well, and received wide international attention. Google Trends has been empirically verified to be a tool that can accurately measure behavioral changes as it takes into account the users’ revealed and not the stated preferences. Thus we argue that, in the time of intelligence excess, Google Trends can well address the analysis of social changes that the internet brings

Inter-gender interaction and communication in ultimatum games

In this paper, we focus on bargaining within male–female pairs, the most pervasive partnership in humankind. We analyze data from an ultimatum game played by Greek subjects. Parallel to this, we introduce a one-way communication protocol according to which the responders can send short messages to the receivers, after making their decisions. The analysis shows that gender and message effects exist and that males are more effective bargainers

Evaluating Google Trends as a Tool for Integrating the ‘Smart Health’ Concept in the Smart Cities’ Governance in USA

AbstractThe aim of this paper is to introduce the methodology of using online search traffic data in order to integrate the public's online behavior in Smart Health; a concept that is currently rising concerning the health factor of Smart Cities. We use normalized data from Google Trends from January 2013 to December 2015 in the US, aiming at exploring the change in interest in various medical terms, and examine if Google Trends is a possible tool for evaluating health search queries by nowcasting the public's online interest. The results show that Google Trends’ data can be used for measuring the public's interest in health related terms, in order to assist with the evaluation of ‘Smart Health’

Descartes de la pesca de cerco enfocadas en peces pequeños pelágicos en el mar Mediterráneo Oriental

We analysed data collected on board commercial purse seine vessels in the Aegean and Ionian Seas (eastern Mediterranean Sea, Greece) in 13 seasonal sampling periods from 2003 to 2008 in order to describe the composition of the retained and discarded catch and shed light on discarding practices. In each area, five species constituted the majority of the marketable catch (> 97%): sardine (Sardina pilchardus), anchovy (Engraulis encrasicolus), round sardinella (Sardinella aurita), bogue (Boops boops) (in both areas), mackerel (Scomber japonicus; in the Aegean Sea) and picarel (Spicara smaris; in the Ionian Sea). Discarded quantities were on average 4.6% and 2.2% of the total catch in terms of weight in the Aegean and Ionian Seas respectively. Discards on the marketable ratio fluctuated over years and seasons without showing any particular trend. At the species level, sardine and mackerel were seldom discarded while large amounts of anchovy were discarded only during its recruitment period (autumn), when juvenile fish dominate the population. The discarding ratio for bogue, picarel and round sardinella ranged from zero to total discarding because they constitute a supplementary source of income for the fishers. Discarded fish comprised mainly small individuals for all species considered with the exception of round sardinella. However, the lengths at which 50% of the individuals were discarded were generally small, often smaller than the species minimum landing sizes. Geographical coordinates and marketable catch explained part of the variability of the discarded quantities, as revealed by generalized additive models. Discarding practices and implications for management of purse seine fisheries are also discussed.Analizamos datos recogidos a bordo de pesqueros comerciales de cerco en el mar Egeo y el mar Jónico (Mediterráneo Oriental, Grecia) durante 13 estacionales de muestreo desde 2003 hasta 2008, con el objetivo de describir la composición de las capturas conservadas y de las descartadas con el fin de arrojar luz sobre la práctica del descarte. En cada zona, la mayor parte de la captura comercial (> 97%) consistía en cinco especies, a saber: la sardina (Sardina pilchardus), el boquerón (Engraulis encrasicolus), la alacha (Sardinella aurita), la boga (Boops boops) (en ambas zonas), la caballa (Scomber japonicus; en el mar Egeo) y el caramel (Spicara smaris; en el mar Jónico). Las cantidades descartadas representaban un promedio del 4.6% y 2.2% de la captura total en términos de peso, en el mar Egeo y en el Jónico respectivamente. La proporción de la captura descartada sobre la captura comercializable fluctuó mucho en todos los años y estaciones sin mostrar ninguna tendencia particular. Con respecto a las especies, en el caso de la sardina y de la caballa, los descartes ocurrieron raramente e incluso los individuos más pequeños se conservaron, mientras que grandes cantidades de boquerón fueron descartadas solamente durante su temporada de reclutamiento (otoño), cuando la población está dominada por peces jóvenes. La proporción descartada en el caso de la boga, del caramel y de la alacha fluctuó mucho, desde un descarte de cero hasta un descarte total, ya que estas especies representan una fuente de ingresos complementaria para los pescadores. Las coordenadas geográficas y la captura comercial explicaban parte de la variabilidad de las cantidades descartadas, como se demuestra con modelos aditivos generalistas. Asimismo, presentamos una discusión sobre la práctica del descarte así como de las consecuencias sobre la gestión de la pesca de cerco

Predictability analysis of the Pound's Brexit exchange rates based on Google Trends data

During the last decade, the use of online search traffic data is becoming popular in examining, analyzing, and predicting human behavior, with Google Trends being a popular tool in monitoring and analyzing the users' online search patterns in several research areas, like health, medicine, politics, economics, and finance. Towards the direction of exploring the Sterling Pound’s predictability, we employ Google Trends data from the last 5 years (March 1st, 2015 to February 29th, 2020) and perform predictability analysis on the Pound’s exchange rates to Euro and Dollar. The period selected includes the 2016 UK referendum as well as the actual Brexit day (January 31st, 2020), with the analysis aiming at analyzing the Pound’s relationships with Google query data on Pound-related keywords and topics. A quantile dependence method is employed, i.e., cross-quantilograms, to test for directional predictability from Google Trends data to the Pound’s exchange rates for lags from zero to 30 (in weeks). The results indicate that statistically significant quantile dependencies exist between Google query data and the Pound’s exchange rates, which point to the direction of one of the main implications in this field, that is to examine whether the movements in one economic variable can cause reactions in other economic variables

Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era

Background: With the internet’s penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma—a common respiratory disease—is present. Objective: This study aimed at forecasting the online behavior toward asthma and examined the correlations between queries and reported cases in order to explore the possibility of nowcasting asthma prevalence in the United States using online search traffic data. Methods: Applying Holt-Winters exponential smoothing to Google Trends time series from 2004 to 2015 for the term “asthma,” forecasts for online queries at state and national levels are estimated from 2016 to 2020 and validated against available Google query data from January 2016 to June 2017. Correlations among yearly Google queries and between Google queries and reported asthma cases are examined. Results: Our analysis shows that search queries exhibit seasonality within each year and the relationships between each 2 years’ queries are statistically significant (P < .05). Estimated forecasting models for a 5-year period (2016 through 2020) for Google queries are robust and validated against available data from January 2016 to June 2017. Significant correlations were found between (1) online queries and National Health Interview Survey lifetime asthma (r=–.82, P=.001) and current asthma (r=–.77, P=.004) rates from 2004 to 2015 and (2) between online queries and Behavioral Risk Factor Surveillance System lifetime (r=–.78, P=.003) and current asthma (r=–.79, P=.002) rates from 2004 to 2014. The correlations are negative, but lag analysis to identify the period of response cannot be employed until short-interval data on asthma prevalence are made available. Conclusions: Online behavior toward asthma can be accurately predicted, and significant correlations between online queries and reported cases exist. This method of forecasting Google queries can be used by health care officials to nowcast asthma prevalence by city, state, or nationally, subject to future availability of daily, weekly, or monthly data on reported cases. This method could therefore be used for improved monitoring and assessment of the needs surrounding the current population of patients with asthma

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Background: In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. Objective: This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. Results: All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. Conclusions: The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise

Strong fisheries management and governance positively impact ecosystem status

Fisheries have had major negative impacts on marine ecosystems, and effective fisheries management and governance are needed to achieve sustainable fisheries, biodiversity conservation goals and thus good ecosystem status. To date, the IndiSeas programme (Indicators for the Seas) has focussed on assessing the ecological impacts of fishing at the ecosystem scale using ecological indicators. Here, we explore fisheries Management Effectiveness' and Governance Quality' and relate this to ecosystem health and status. We developed a dedicated expert survey, focused at the ecosystem level, with a series of questions addressing aspects of management and governance, from an ecosystem-based perspective, using objective and evidence-based criteria. The survey was completed by ecosystem experts (managers and scientists) and results analysed using ranking and multivariate methods. Results were further examined for selected ecosystems, using expert knowledge, to explore the overall findings in greater depth. Higher scores for Management Effectiveness' and Governance Quality' were significantly and positively related to ecosystems with better ecological status. Key factors that point to success in delivering fisheries and conservation objectives were as follows: the use of reference points for management, frequent review of stock assessments, whether Illegal, Unreported and Unregulated (IUU) catches were being accounted for and addressed, and the inclusion of stakeholders. Additionally, we found that the implementation of a long-term management plan, including economic and social dimensions of fisheries in exploited ecosystems, was a key factor in successful, sustainable fisheries management. Our results support the thesis that good ecosystem-based management and governance, sustainable fisheries and healthy ecosystems go together.IOC-UNESCO; EuroMarine; European FP7 MEECE research project; European Network of Excellence Eur-Oceans; FRB EMIBIOS project [212085]info:eu-repo/semantics/publishedVersio