Analyzing digital societal interactions and sentiment classification in Twitter (X) during critical events in Chile.

This study explores the influence of social media content on societal attitudes and actions during critical events, with a special focus on occurrences in Chile, such as the COVID-19 pandemic, the 2019 protests, and the wildfires in 2017 and 2023. By leveraging a novel tweet dataset, this study introduces new metrics for assessing sentiment, inclusivity, engagement, and impact, thereby providing a comprehensive framework for analyzing social media dynamics. The methodology employed enhances sentiment classification through the use of a Deep Random Vector Functional Link (D-RVFL) neural network, which demonstrates superior performance over traditional models such as Support Vector Machines (SVM), naive Bayes, and back propagation (BP) neural networks, achieving an overall average accuracy of 78.

Assessing the impact of small firm dynamics on public mental health amid the pandemic in Latin America.

Objective: The aim of our study is to examine the relationship between the economic activity of small firms and the mental well-being of the population in five Latin American countries in the early stages of the pandemic.

Methods: We utilize the search volume of certain keywords on Google Trends (GT), such as "boredom," "frustration," "loneliness," "sleep", "anxiety", and "depression", as an indicator of the well-being of the population. By examining the data from Facebook Business Activity Trends, we investigate how social attention reacts to the activity levels of different economic sectors.

Facial biotype classification for orthodontic treatment planning using an alternative learning algorithm for tree augmented Naive Bayes.

Background: When designing a treatment in orthodontics, especially for children and teenagers, it is crucial to be aware of the changes that occur throughout facial growth because the rate and direction of growth can greatly affect the necessity of using different treatment mechanics. This paper presents a Bayesian network approach for facial biotype classification to classify patients' biotypes into Dolichofacial (long and narrow face), Brachyfacial (short and wide face), and an intermediate kind called Mesofacial, we develop a novel learning technique for tree augmented Naive Bayes (TAN) for this purpose.

Results: The proposed method, on average, outperformed all the other models based on accuracy, precision, recall, [Formula: see text], and kappa, for the particular dataset analyzed.

Individual competence predominates over host nutritional status in Arabidopsis root exudate-mediated bacterial enrichment in a combination of four Burkholderiaceae species.

Background: Rhizosphere microorganisms play a crucial role in plant health and development. Plant root exudates (PRE) are a complex mixture of organic molecules and provide nutritional and signaling information to rhizosphere microorganisms. Burkholderiaceae species are non-abundant in the rhizosphere but exhibit a wide range of plant-growth-promoting and plant-health-protection effects.

Heterogeneous responses in Google Trends measures of well-being to the COVID-19 dynamic quarantines in Chile.

We study how the Chilean population's well-being responded to the strategy implemented by their health authorities, known as Dynamic Quarantine, to contain the spread of coronavirus in which municipalities periodically entered and exited lockdowns. This unique scheme, together with the population's socioeconomic heterogeneity, facilitates the estimation of changes in this well-being as differentiated by socioeconomic status. Using Google Trends to compute measures of well-being, we find strong evidence that socioeconomic status induces heterogeneity in these changes; thus, neglecting this heterogeneity may lead to misleading prescriptions for the public policy that addresses the psychological effects of lockdowns.

Stock market volatility and the COVID-19 reproductive number.

The media has prominently featured the totemic reproductive number in its COVID-19 coverage despite being an imperfect measure of the degree of infectivity of the virus. As such, it conveys information to the public regarding the state of the pandemic that affects market sentiment. We analyze how news about affects the volatility in stock markets worldwide and find that when is greater than one, which means the spread of the disease should soar, it has a positive and significant effect on volatility.

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy.

The Chilean health authorities have implemented a sanitary strategy known as dynamic quarantine or strategic quarantine to cope with the COVID-19 pandemic. Under this system, lockdowns were established, lifted, or prolonged according to the weekly health authorities' assessment of municipalities' epidemiological situation. The public announcements about the confinement situation of municipalities country-wide are made typically on Tuesdays or Wednesdays before noon, have received extensive media coverage, and generated sharp stock market fluctuations.