Science / Press

Understanding economic and health factors impacting the spread of COVID-19 disease 15 February 2022

Background

The rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely populated areas.

Objective


Aiming at bringing more light on key economic and population health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly available COVID-19 datasets.

Methods

We have applied Pearson Correlation Analysis and Clustering Analysis (X-Means Clustering) techniques on the data obtained by combining multiple datasets related to country economics, medical system & health, and COVID-19 - related statistics. The resulting dataset consisted of COVID-19 Case and Mortality Rates, Economic Statistics, and Population Public Health Statistics for 165 countries reported between 22 January 2020 and 28 March 2020. The correlation analysis was conducted with the significance level α of 0.05. The clustering analysis was guided by the value of Bayesian Information Criterion (BIC) with the bin value b = 1.0 and the cutoff factor c = 0.5, and have provided a stable split into four country-level clusters.
Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling 10 February 2022

Human personality traits are the key drivers behind our decision-making, influencing our life path on a daily basis. Inference of personality traits, such as Myers-Briggs Personality Type, as well as an understanding of dependencies between personality traits and users' behavior on various social media platforms is of crucial importance to modern research and industry applications. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, the research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse, and the level of impact of different social network data on machine learning performance has yet to be comprehensively evaluated.
SoMin.ai: Personality-Driven Content Generation Platform 10 February 2022

In this technical demonstration, we showcase the World's first personality-driven marketing content generation platform, called SoMin. ai. The platform combines deep multi-view personality profiling framework and style generative adversarial networks facilitating the automatic creation of content that appeals to different human personality types. The platform can be used for enhancement of the social networking user experience as well as for content marketing routines. Guided by the MBTI personality type, automatically derived from a user social network content, SoMin. ai generates new social media content based on the preferences of other users with a similar personality type aiming at enhancing the user experience on social networking venues as well diversifying the efforts of marketers when crafting new content for digital marketing campaigns.

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