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The 1730 Great Metropolitan Chile Earthquake and Tsunami Commemoration: Joint Efforts to Increase the Country’s Awareness
(MDPI) Zamora, Natalia; Gubler, Alejandra; Orellana, Víctor; León, Jorge; Urrutia, Alejandro; Carvajal, Matías; Cisternas, Marco; Catalán, Patricio; Winckler Grez, Patricio; Cienfuegos, Rodrigo; Karich, Cristóbal; Vogel, Stefan; Galaz, José; Pereira, Sebastián; Bertin, Celeste
On 8 July 1730, a great earthquake struck metropolitan Chile, causing extensive damage 1000 km along the country and focused in Valparaíso. Due to the date of occurrence of this event, large uncertainties about the earthquake’s magnitude have been discussed among the scientific community, and the earthquake and tsunami have remained unknown for most of the population. The purpose of this paper is to describe joint efforts undertaken by organizations, academia, and authorities to rescue the forgotten memory of an event that occurred almost three centuries ago and that may be repeated in the near future. In line with the Sendai Framework, we focus on one of the four priorities for action, which is to understand disaster risk, with the premise that the memory activation and raising awareness can save lives in the future. We designed outreach strategies to communicate this knowledge to the community in a participatory way. The latter involves scientific talks, earthquake simulators, tsunami projection mapping on relief scaled models (mock-up), and a public debate including the participation of academia, politicians, authorities, and the local community. The emulation of such activities and the constant work of regional and national authorities, academia, and non-governmental organizations dealing with risk mitigation encourage involving the community to build safer cities against the tsunami hazard.
A comprehensive comparison between shortest-path HARP refinement, SinMod, and DENSEanalysis processing tools applied to CSPAMM and DENSE images
(Elsevier, 2021) Mella, Hernán; Mura, Joaquín; Sotelo, Julio; Uribe, Sergio
We addressed comprehensively the performance of Shortest-Path HARP Refinement (SP-HR), SinMod, and DENSEanalysis using 2D slices of synthetic CSPAMM and DENSE images with realistic contrasts obtained from 3D phantoms. The three motion estimation techniques were interrogated under ideal and no-ideal conditions (with MR induced artifacts, noise, and through-plane motion), considering several resolutions and noise levels. Under noisy conditions, and for isotropic pixel sizes of 1.5 mm and 3.0 mm in CSPAMM and DENSE images respectively, the nRMSE obtained for the circumferential and radial strain components were 10.7 ± 10.8% and 25.5 ± 14.8% using SP-HR, 11.9 ± 2.5% and 29.3 ± 6.5% using SinMod, and 6.4 ± 2.0% and 18.2 ± 4.6% using DENSEanalysis. Overall, the results showed that SP-HR tends to fail for large tissue motions, whereas SinMod and DENSEanalysis gave accurate displacement and strain field estimations, being the last which performed the best.
A Descriptive Analysis for a Collaborative Work Process: A Complex Real Medical Case in the Radiological Field
(American Scientific Publishers, 2021) Aguilera, Ana; Quintero, José
Collaborative acts occur daily in every human activity. In the case of medicine, and particularly in the diagnostic decision process, these acts are very frequent and occur naturally. It is very important to properly understand how these collaborative acts are developed in order to provide tools that facilitate and support them. In this article, we describe this collaborative work process in the framework of a complex real medical case in the radiological field. Usually, complex cases require several specialists. In this work, we have analyzed the intervention of several specialists and the exchange and interaction of different reasoning strategies among specialists, while considering their temporal dimension. Two types of collaboration are presented in the case analysis (1) exchange between specialists from different specialties and (2) exchange between specialists from the same specialty. The method of analysis follows five steps: (1) Case synopsis, (2) Temporal representation of the case, (3) Analysis of the general decision in the case, (4) Analysis of the reasoning in the medical case using the different strategies, and (5) Analysis of radiological collaboration. We have presented different reasoning strategies, data, hypotheses and complementary tests from different sources in the diagnostic resolution process and we have shown that collaboration is present during the entire process. The temporality and the intervention of different specialists is shown using a graphical representation. We have focused special attention on radiological collaboration, and have shown how a radiological diagnosis is achieved. We have discussed different elements present in the collaboration process. Our study has produced meta-knowledge derived from these exchanges that is of value in the context of artificial intelligence progress, in particular for the comprehension of collaborative medical work.
A Feature-Based Analysis for Time-Series Classification of COVID-19 Incidence in Chile: A Case Study
(MDPI, 2021) Flores, Christopher; Taramasco, Carla; Lagos, Maria Elena; Rimassa, Carla; Figueroa, Rosa
The 2019 Coronavirus disease (COVID-19) pandemic is a current challenge for the world’s health systems aiming to control this disease. From an epidemiological point of view, the control of the incidence of this disease requires an understanding of the influence of the variables describing a population. This research aims to predict the COVID-19 incidence in three risk categories using two types of machine learning models, together with an analysis of the relative importance of the available features in predicting the COVID-19 incidence in the Chilean urban commune of Concepción. The classification results indicate that the ConvLSTM (Convolutional Long Short-Term Memory) classifier performed better than the SVM (Support Vector Machine), with results between 93% and 96% in terms of accuracy (ACC) and F-measure (F1) metrics. In addition, when considering each one of the regional and national features as well as the communal features (DEATHS and MOBILITY), it was observed that at the regional level the CRITICAL BED OCCUPANCY and PATIENTS IN ICU features positively contributed to the performance of the classifiers, while at the national level the features that most impacted the performance of the SVM and ConvLSTM were those related to the type of hospitalization of patients and the use of mechanical ventilators.
A Fuzzy Inference System for Management Control Tools
(MDPI, 2021) Nicolas, Carolina; Müller, Javiera; Arroyo-Cañada, Francisco-Javier
Despite the importance of the role of small and medium enterprises (SMEs) in developing and growing economies, little is known regarding the use of management control tools in them. In management control in SMEs, a holistic system needs to be modeled to enable a careful study of how each lever (belief systems, boundary systems, interactive control systems, and diagnostic control systems) affects the organizational performance of SMEs. In this article, a fuzzy logic approach is proposed for the decision-making system in management control in small and medium enterprises. C. Mamdani fuzzy inference system (MFIS) was applied as a decision-making technique to explore the influence of the use of management control tools on the organizational performance of SMEs. Perceptions data analysis is obtained through empirical research.
A Generalization of the Importance of Vertices for an Undirected Weighted Graph
(MDPI, 2021) Manríquez, Ronald; Guerrero-Nancuante, Camilo; Martínez, Felipe; Taramasco, Carla
Establishing a node importance ranking is a problem that has attracted the attention of many researchers in recent decades. For unweighted networks where the edges do not have any attached weight, many proposals have been presented, considering local or global information of the networks. On the contrary, it occurs in undirected edge-weighted networks, where the proposals to address this problem have been more scarce. In this paper, a ranking method of node importance for undirected and edge-weighted is provided, generalizing the measure of line importance (DIL) based on the centrality degree proposed by Opsahl. The experimentation was done on five real networks and the results illustrate the benefits of our proposal.
A Generation 1.5 Palestinian Diaspora Child Refugee in Chile
(Taylor & Francis, 2021) Arancibia, Héctor; Leihy, Pete; Samari, Davood
This study follows a former child refugee’s experience of family resettlement in Chile. Born into the Palestinian Iraqi community further imperiled by the 2003 invasion of Iraq, his family fled first to the Al-Tanf refugee camp before placement in Chile. While most of the world’s refugees dwell in marginal conditions in areas neighboring conflicts, another strain of permanent settlement has been highly developed amongst some of the wealthiest countries. For countries such as Chile—by strict definition now high-income, but only newly considering a role as a haven for refugees—tentative steps toward resettlement protocols mean that case data are limited. By carefully studying a family’s resettlement and subsequent experience from a child refugee’s reflections, it is possible to sketch out and understand a range of challenges at the human scale of supporting refugees.
A hybrid model of viscous and Chaplygin gas to tackle the Universe acceleration
(Springer, 2021) Hernández-Almada, A.; García-Aspeitia, Miguel A.; Rodríguez-Meza, M. A.; Motta, V.
Motivated by two seminal models proposed to explain the Universe acceleration, this paper is devoted to study a hybrid model which is constructed through a generalized Chaplygin gas with the addition of a bulk viscosity. We call the model a viscous generalized Chaplygin gas (VGCG) and its free parameters are constrained through several cosmological data like the Observational Hubble Parameter, Type Ia Supernovae, Baryon Acoustic Oscillations, Strong Lensing Systems, HII Galaxies and using Joint Bayesian analysis. In addition, we implement a Om-diagnostic to analyze the VGCC dynamics and its difference with the standard cosmological model. The hybrid model shows important differences when compared with the standard cosmological model. Finally, based on our Joint analysis we find that the VGCG could be an interesting candidate to alleviate the well-known Hubble constant tension.
A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models
(MDPI, 2021) Castillo, Mauricio; Soto, Ricardo; Crawford, Broderick; Castro, Carlos; Olivares, Rodrigo
Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.
A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations
(MDPI, 2021) Vieira, Felipe; Cechinel, Cristian; Ramos, Vinicius; Riquelme, Fabián; Noel, Rene; Villarroel, Rodolfo; Cornide-Reyes, Hector; Munoz, Roberto
Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.
A massive open cluster hiding in full sight
(Royal Astronomical Society, 2021) Negueruela, I.; Chene, A.-N.; Tabernero, H. M.; Dorda, R.; Borissova, J.; Marco, A.; Kurtev, R.
Obscuration and confusion conspire to limit our knowledge of the inner MilkyWay. Even atmoderate distances, the identification of stellar systems becomes compounded by the extremely high density of background sources. Here, we provide a very revealing example of these complications by unveiling a large, massive, young cluster in the Sagittarius arm that has escaped detection until now despite containing more than 30 stars brighter than G = 13. By combining Gaia DR2 astrometry, Gaia and 2MASS photometry, and optical spectroscopy, we find that the new cluster, which we name Valparaiso 1, located at ∼ 2.3 kpc, is about 75 Ma old and includes a large complement of evolved stars, among which we highlight the 4 d classical Cepheid CM Sct and an M-type giant that probably represents the first detection of an asymptotic giant branch star in a Galactic young open cluster. Although strong differential reddening renders accurate parameter determination unfeasible with the current data set, direct comparison to clusters of similar age suggests that Valparaiso 1 was born as one of the most massive clusters in the solar neighbourhood, with an initial mass close to 104M .
A modeling and simulation platform for space-based compartmental modeling of pandemic spread
(IEEE, 2021) Cárdenas, Román; Inostrosa-Psijas, Alonso; Wainer, Gabriel
The COVID-19 outbreak has shown that Modeling and Simulation (M&S) methodologies are an important aspect to study the spread of the disease and assess the effect of different measures to diminish its negative effect. Although traditional models have been widely used, there is a need to build new, highly configurable disease models to explore multiple scenarios quickly. We present an M&S framework to perform rapid prototyping of pandemic spread using the Cell-DEVS space-based discrete-event modeling approach. This method supports age segmentation of the population, hospital-capacity-dependent deaths, and enforcing mobility restriction policies. This method is useful for studying the spread of the disease, as well as combining the simulation results with different visualization tools.
A multi-objective linear threshold influence spread model solved by swarm intelligence-based methods
(Elsevier, 2021) Olivares, Rodrigo; Muñoz, Francisco; Riquelme, Fabián
The influence maximization problem (IMP) is one of the most important topics in social network analysis. It consists of finding the smallest seed of users that maximizes the influence spread in a social network. The main influence spread models are the linear threshold model (LT-model) and the independent cascade model (IC-model). These models have mainly been treated by using the single-objective paradigm which covers just one perspective: maximize the influence spread starting by given seed size, or minimize the seed set to reach a given number of influenced nodes. Sometimes, this minimization problem has been called the least cost influence problem (LCI). In this work, we propose a new optimization model for both perspectives under conflict, through the LT-model, by applying a binary multi-objective approach. Swarm intelligence methods are implemented to solve our proposal on real networks. Results are promising and suggest that the new multi-objective solution proposed can be properly solved in harder instances.
A Multisubcellular Compartment Model of AMPA Receptor Trafficking for Neuromodulation of Hebbian Synaptic Plasticity
(Frontiers, 2021) Mihalas, Stefan; Ardiles, Alvaro; He, Kaiwen; Palacios, Adrian; Kirkwood, Alfredo
Neuromodulation can profoundly impact the gain and polarity of postsynaptic changes in Hebbian synaptic plasticity. An emerging pattern observed in multiple central synapses is a pull–push type of control in which activation of receptors coupled to the G-protein Gs promote long-term potentiation (LTP) at the expense of long-term depression (LTD), whereas receptors coupled to Gq promote LTD at the expense of LTP. Notably, coactivation of both Gs- and Gq-coupled receptors enhances the gain of both LTP and LTD. To account for these observations, we propose a simple kinetic model in which AMPA receptors (AMPARs) are trafficked between multiple subcompartments in and around the postsynaptic spine. In the model AMPARs in the postsynaptic density compartment (PSD) are the primary contributors to synaptic conductance. During LTP induction, AMPARs are trafficked to the PSD primarily from a relatively small perisynaptic (peri-PSD) compartment. Gs-coupled receptors promote LTP by replenishing peri-PSD through increased AMPAR exocytosis from a pool of endocytic AMPAR. During LTD induction AMPARs are trafficked in the reverse direction, from the PSD to the peri-PSD compartment, and Gq-coupled receptors promote LTD by clearing the peri-PSD compartment through increased AMPAR endocytosis. We claim that the model not only captures essential features of the pull–push neuromodulation of synaptic plasticity, but it is also consistent with other actions of neuromodulators observed in slice experiments and is compatible with the current understanding of AMPAR trafficking.
A near-infrared interferometric survey of debris-disk stars VII. The hot-to-warm dust connection
(European Southern Observatory (ESO), 2021) Absil, O.; Marion, L.; Ertel, S.; Defrère, D.; Kennedy, G. M.; Romagnolo, A.; Le Bouquin, J.-B.; Christiaens, V.; Milli, J.; Bonsor, A.; Olofsson, Johan; Su, K. Y. L.; Augereau, J.-C.
Context. Hot exozodiacal dust has been shown to be present in the innermost regions of an increasing number of main sequence stars over the past 15 yr. However, the origin of hot exozodiacal dust and its connection with outer dust reservoirs remains unclear. Aims. We aim to explore the possible connection between hot exozodiacal dust and warm dust reservoirs (≥100 K) in asteroid belts. Methods. We use precision near-infrared interferometry with VLTI/PIONIER to search for resolved emission at H-band around a selected sample of 62 nearby stars that show possible signs of warm dust populations. Results. Our observations reveal the presence of resolved near-infrared emission around 17 out of 52 stars with sufficient data quality. For four of these, the emission is shown to be due to a previously unknown stellar companion. The 13 other H-band excesses are thought to originate from the thermal emission of hot dust grains, close to their sublimation temperature. Taking into account earlier PIONIER observations, where some stars with warm dust were also observed, and after re-evaluating the warm dust content of all our PIONIER targets through spectral energy distribution modeling, we find a detection rate of 17.1−4.6+8.1% for H-band excess around main sequence stars hosting warm dust belts, which is statistically compatible with the occurrence rate of 14.6−2.8+4.3% found around stars showing no signs of warm dust. After correcting for the sensitivity loss due to partly unresolved hot disks, under the assumption that they are arranged in a thin ring around their sublimation radius, we find tentative evidence at the 3σ level that H-band excesses around stars with outer dust reservoirs (warm or cold) could be statistically larger than H-band excesses around stars with no detectable outer dust. Conclusions. Our observations do not suggest a direct connection between warm and hot dust populations at the sensitivity level of the considered instruments, although they bring to light a possible correlation between the level of H-band excess and the presence of outer dust reservoirs in general.
A novel artificial autonomous system for supporting investment decisions using a Big Five model approach
(Elsevier, 2021) Cabrera Paniagua, Daniel; Rubilar Torrealba, Rolando
This paper presents the design of an artificial autonomous system (called AAS) for the stock market domain that considers an approximation from the Big Five model, which proposes that the personality of an individual belongs to one of five different personality profiles: openness, conscientiousness, extraversion, agreeableness, and neuroticism. Several studies have explored investment and financial issues while considering the Big Five model, usually by analyzing data obtained from surveys applied to real people. However, to the best of our knowledge, there are no proposals that suggest the design of an AAS for supporting investment decisions that use the Big Five model as the central approach. The main objective of this proposal is to design an AAS for making investment decisions, where the decisions are adjusted to market conditions through the use of a policy function that adapts over time. This policy function adjusts the consumption level and investment portfolio composition required by the investment profile, considering both the market conditions and the Big Five model profile associated with the AAS. The effectiveness of the investment process is measured by observing the variations in the accumulated wealth and utility. The utility is measured through an abstract representation of the well-being or satisfaction of the investor (i.e., the AAS). AAS—Extraversion obtained the highest accumulated wealth, while AAS—Agreeableness obtained the highest level of utility, showing that the accumulated wealth is only one factor influencing the investor’s well-being.
A physiologic rise in cytoplasmic calcium ion signal increases pannexin1 channel activity via a C-terminus phosphorylation by CaMKII
(National Academy Of Science, 2021) Lópeza, Ximena; Palacios-Pradoa, Nicolás; Güizac, Juan; Escamilla, Rosalba; Fernández, Paola; Vega, José L.; Rojas, Maximiliano; Marquez-Miranda, Valeria; Chamorro, Eduardo; Cárdenas, Ana M.; Constanza Maldifassi, María; Martínez, Agustín D.; Duarte, Yorley; González-Nilo, Fernando D.; Sáez, Juan C.
Pannexin1 (Panx1) channels are ubiquitously expressed in vertebrate cells and are widely accepted as adenosine triphosphate (ATP)-releasing membrane channels. Activation of Panx1 has been associated with phosphorylation in a specific tyrosine residue or cleavage of its C-terminal domains. In the present work, we identified a residue (S394) as a putative phosphorylation site by Ca2+/calmodulin-dependent kinase II (CaMKII). In HeLa cells transfected with rat Panx1 (rPanx1), membrane stretch (MS)-induced activation—measured by changes in DAPI uptake rate—was drastically reduced by either knockdown of Piezo1 or pharmacological inhibition of calmodulin or CaMKII. By site-directed mutagenesis we generated rPanx1S394A-EGFP (enhanced green fluorescent protein), which lost its sensitivity to MS, and rPanx1S394D-EGFP, mimicking phosphorylation, which shows high DAPI uptake rate without MS stimulation or cleavage of the C terminus. Using whole-cell patch-clamp and outside-out excised patch configurations, we found that rPanx1-EGFP and rPanx1S394D-EGFP channels showed current at all voltages between ±100 mV, similar single channel currents with outward rectification, and unitary conductance (∼30 to 70 pS). However, using cell-attached configuration we found that rPanx1S394D-EGFP channels show increased spontaneous unitary events independent of MS stimulation. In silico studies revealed that phosphorylation of S394 caused conformational changes in the selectivity filter and increased the average volume of lateral tunnels, allowing ATP to be released via these conduits and DAPI uptake directly from the channel mouth to the cytoplasmic space. These results could explain one possible mechanism for activation of rPanx1 upon increase in cytoplasmic Ca2+ signal elicited by diverse physiological conditions in which the C-terminal domain is not cleaved.
A producao do conhecimento da Educacao Física sobre Educacao Infantil como tema de pesquisa
(Scielo, 2021) Rocha, María Celeste; Almeida, Felipe Quintao De; Moreno Doña, Alberto
Este artigo analisa e discute as producóes academicas da Educacáo Física sobre Educacáo Infantil a partir de urna breve apresentacáo e análise dos estudos que tomararn tal producáo como tema de investigacáo. Em termos metodológicos se constitui numa revisáo imegrativa dos artigos, teses e dissertacóes que se propuseram a estudar a referida producáo e estáo disponíveis no Portal de Periódicos da CAPES, no Catálogo de Teses e Dissertacóes da CAPES e na Biblioteca Digital Brasileira de Teses e Dissertacóes (BDTD). Apresenta urna síntese e análise dos principais aspectos da producáo que os estudos nos permitem conhecer, aponta algumas !acunas e indica algumas relacóes entre os referenciais teóricos que embasam as producóes e as proposicóes apresentadas para a prática pedagógica da Educacáo com a Educacáo Infantil que vem senda sistematizadas. Conclui que, embora importante, a enfase dada pelos estudos de revisáo e estado da arte aos aspectos quantitativos e descritivos da producáo tem dificultado urna compreensáo mais abrangeme dos discursos que sao produzidos sobre a Educacáo Física na Educacáo Infantil.
A proof of consistency of the MLE for nonlinear Markov-switching AR processes
(Elsevier, 2022) Fermín, Lisandro; Marcano, José; Rodríguez, Luis-Angel
We propose a new approach to demonstrate the consistency of the maximum likelihood estimator for nonlinear Markov-switching AR processes (abbreviated MS-NAR). We obtain a uniform exponential memory loss property for the prediction filter by approximating it by a filter with finite memory. From the -mixing property for the MS-NAR process we obtain an ergodic theorem. Finally, we show that in the linear and Gaussian case our assumptions are fully satisfied.
A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis
(Emerald, 2021) Dongo, Irvin; Cardinale, Yudith; Aguilera, Ana; Martinez, Fabiola; Quintero, Yuni; Robayo, German; Cabeza, David
Purpose – This paper aims to perform an exhaustive revision of relevant and recent related studies, which reveals that both extraction methods are currently used to analyze credibility on Twitter. Thus, there is clear evidence of the need of having different options to extract different data for this purpose. Nevertheless, none of these studies perform a comparative evaluation of both extraction techniques. Moreover, the authors extend a previous comparison, which uses a recent developed framework that offers both alternates of data extraction and implements a previously proposed credibility model, by adding a qualitative evaluation and a Twitter-Application Programming Interface (API) performance analysis from different locations. Design/methodology/approach – As one of the most popular social platforms, Twitter has been the focus of recent research aimed at analyzing the credibility of the shared information. To do so, several proposals use either Twitter API or Web scraping to extract the data to perform the analysis. Qualitative and quantitative evaluations are performed to discover the advantages and disadvantages of both extraction methods. Findings – The study demonstrates the differences in terms of accuracy and efficiency of both extraction methods and gives relevance to much more problems related to this area to pursue true transparency and legitimacy of information on the Web. Originality/value – Results report that some Twitter attributes cannot be retrieved by Web scraping. Both methods produce identical credibility values when a robust normalization process is applied to the text i.e. tweet). Moreover, concerning the time performance, Web scraping is faster than Twitter API and it is more flexible in terms of obtaining data; however, Web scraping is very sensitive to website changes. Additionally, the response time of the Twitter API is proportional to the distance from the central server at San Francisco.

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