Text Mining Approach to Analyze Coronavirus Impact: Mexico City as Case of Study

Saire, Josimar Edinson Chire; Pineda-Briseño, Anabel

doi:10.1101/2020.05.07.20094466

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…So, text mining techniques are beneficial for finding misinformation. Another study was conducted by [19] to understand the impact of Covid-19 in Mexican society by using the text mining approach. The study extracted Twitter tweets about Covid-19 from the 13th to the 20th of March 2020, and the geolocalization of the retrieved tweets was Mexico City.…”

Section: B Text Mining and Text Classificationmentioning

confidence: 99%

Using a Rule-based Model to Detect Arabic Fake News Propagation during Covid-19

Alotaibi¹,

Alhammad²

2022

IJACSA

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Since the emergence of the Covid-19, both factual and false information about the new virus has been disseminated. Fake news harms societies and must be combated. This research aims to identify Arabic fake news tweets and classify them into six categories: entertainment, health, politics, religious, social, and sports. The study also aims to uncover patterns in the spread of Arabic fake news associated with the Covid-19 pandemic. The researchers created an Arabic dictionary and used text classification based on a rule-based system to detect and categorize fake news. A dataset consisting of 5 million tweets was analyzed. The developed model achieves an overall accuracy of 78.1% with 70% precision and 98%recall. The model detected more than 26006 fake news tweets. Interestingly we found an association between the number of fake news tweets and dates. The result demonstrates that as more information and knowledge about Covid-19 become available over time, people's awareness increase, while the number of fake news tweets decreases. The categorization of false news indicates that the social category was highest in all Arab countries except Palestine, Qatar, Yemen, and Algeria. Conversely, fake news related to the entertainment category was the weakest dissemination in most Arab countries.

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Section: B Text Mining and Text Classificationmentioning

confidence: 99%

Using a Rule-based Model to Detect Arabic Fake News Propagation during Covid-19

Alotaibi¹,

Alhammad²

2022

IJACSA

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“…These usually include a social media analysis where the textual information provided by individuals in Mexico is processed. Twitter and other social networks are common sources of this information [ 32 ]. These approaches report important conclusions about the behaviour of individuals, where the first perceivable sufficiently active mentions of the COVID-19 topic on social networks mainly occurred between 13 March 2020 and 20 March 2020.…”

Section: Introductionmentioning

confidence: 99%

Detecting Emotional Evolution on Twitter during the COVID-19 Pandemic Using Text Analysis

Cabezas

Moctezuma²,

Fernández-Isabel

et al. 2021

IJERPH

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Early in 2020, an unexpected and hazardous situation occurred threatening and challenging all of humankind. A new coronavirus called SARS-CoV-2 was first identified in Wuhan, China, and its related disease, called COVID-19, has induced one of the most dangerous crises at a global level since World War II. The ultra-fast transmission rate of the virus and the high mortality rate led the World Health Organization (WHO) to officially declare the situation a pandemic. Governments, for their part, were forced to implement unprecedented mobility restrictions and cease a large part of their economic activities. These facts triggered multiple reactions from people who expressed their feelings mainly through social networks (like Twitter), using them as vectors of information and opinion. In this paper, a study carried out in different Spanish speaking countries (Chile, Mexico, Peru, and Spain) is presented, which addresses the manner in which the evolution of the pandemic outbreak has affected the emotions expressed by individuals on Twitter over the last 13 months (from March 2020 to March 2021). We used a total of 3 million tweets to achieve this task. We made use of a well-known framework called EmoWeb to capture the dynamic variation in the sentimental value of pandemic-related words. The results reflect to what degree the pandemic and its derived problems have influenced and affected the population of the selected countries in different ways. The outcomes also illustrate the evolution over time of opinions published on Twitter regarding several topics related to COVID-19.

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Text Mining for Covid-19 Analysis in Latin America

Saire

Pineda-Briseño

2021

Artificial Intelligence for COVID-19

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Text Mining Approach to Analyze Coronavirus Impact: Mexico City as Case of Study

Cited by 4 publications

References 15 publications

Using a Rule-based Model to Detect Arabic Fake News Propagation during Covid-19

Using a Rule-based Model to Detect Arabic Fake News Propagation during Covid-19

Detecting Emotional Evolution on Twitter during the COVID-19 Pandemic Using Text Analysis

Text Mining for Covid-19 Analysis in Latin America

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