Text Mining Approach to Analyze Coronavirus Impact: Mexico City as Case of Study
Chire Saire, J. E.; Pineda-Briseno, A.
Show abstract
The epidemiological outbreak of a novel coronavirus (2019-nCoV or Covid-19) in China, and its rapid spread, gave rise to the first pandemic in the digital age. Derived from this fact that has surprised humanity, many countries started with different strategies in order to stop the infection. In this context, one of the greatest challenges for the scientific community is monitoring (real time) the global population to get immediate feedback of what is happening with the people during this public health contingency. An alternative interesting and affordable for the materialization of the aforementioned are the social networks. In a social network, the persons can act as sensors/information not only of personal data but also data derived from their behavior. This paper aims to analyze the publications of people in Mexico using a Text Mining approach. Specifically, Mexico City is presented as a case study to help understand the impact on society of the spread of Covid-19.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Prediction of confirmed and death cases of Covid-19 in Chile through time series techniques: A comparative study 93%
- Prediction and control of COVID-19 infection based on a hybrid intelligent model 93%
- Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space 93%
Similar papers in this journal
- Predicting COVID-19 Pandemic in Saudi Arabia Using Modified Singular Spectrum Analysis 90%
- Google Trends as a predictive tool for COVID-19 vaccinations in Italy: a retrospective infodemiological analysis 90%
- Prediction of COVID-19 Mortality to Support Patient Prognosis and Triage and Limits of Current Open-Source Data 90%
Similar papers in this journal
- Users’ Reactions on Announced Vaccines against COVID-19 Before Marketing in France: Analysis of Twitter posts 93%
- Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: A Digital Topic Modeling Approach 92%
- Fear of Infection and Sufficient Vaccine Reservation Information Might Drive Rapid Coronavirus Disease 2019 Vaccination in Japan: Evidence from Twitter Analysis 91%
Similar papers in this journal
- A Transfer Entropy-based methodology to analyze information flow under eyes-open and eyes-closed conditions with a clinical perspective 90%
- Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine 88%
- Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification 88%
Similar papers in this journal
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 94%
- A new, simple method of describing COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting 92%
- A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.