Trust, Social Protection, and Compliance: Moral Hazard in Latin America During the COVID-19 Pandemic

Bird, Matthew; Arispe, Samuel; Muñoz, Paula; Freier, Luisa Feline

doi:10.2139/ssrn.4058125

Cited by 1 publication

(1 citation statement)

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“…The study carried out by Nieves-Cuervo et al [1] details the behavior of the propagation of false information in the context of mortality from COVID-19 in Peru, where it was found that said country presented the highest percentage regarding the inability to recognize false news (79.0%) and that it was the second most trusted in the content of social networks (46.0%) leading to a higher mortality rate from COVID-19 above countries such as Chile, Colombia, Brazil, Argentina and Mexico. Similarly, the survey carried out by researchers from the Pacific University [2] details that Peruvians are more likely to avoid COVID-19 vaccines because they do not trust them, since they believe that these vaccines are harmful to your health, modify your DNA or contain some kind of chip.…”

Section: Introductionmentioning

confidence: 99%

Model Proposal for the Detection of False Information About COVID-19 Using Machine Learning and Natural Language Processing Techniques

Ticona,

Salinas-Bolaños

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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One of the main problems that arose as a result of this health emergency was the circulation of false information on COVID-19. Therefore, the study carried out aimed to find the best classifier of false information on COVID-19 in the Peruvian context. For this, 2022 information records related to COVID-19 were collected through web scraping of websites, Facebook and Twitter, which were manually labeled as True or False and then validated. Natural Language Processing techniques such as Bag of Words, TF-IDF, Word2Vec and FastText were used for feature extraction. Finally, different Machine Learning model were developed using KNN, Decision Tree, Naive Bayes, SVM, Logistic Regression and MLP. The results were evaluated according to the Accuracy, Precision, Recall and F1-score metrics. The best model resulted from the combination of the SVM algorithm (C (0.5), gamma (1) and kernel (rbf)) with TF -IDF of dimension 300 and n-grams from 1 to 2, whose metricswere superior to the others with 87.41% Accuracy, 88.63% Precision, 87.39% Recall and 88% F1-score.

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Section: Introductionmentioning

confidence: 99%