Related Experiment Video
Updated: Aug 20, 2025
08:26
Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
Published on: June 23, 2022
1.8K
A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration
Juan M Banda1, Ramya Tekumalla1, Guanyu Wang2
1Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.
Epidemiologia (Basel, Switzerland)
|November 23, 2022
Summary
This study introduces a large dataset of over 1.12 billion COVID-19 related tweets. This resource aids researchers in analyzing social dynamics, sentiment, and misinformation during the pandemic.
Area of Science:
- Public Health
- Computational Social Science
- Epidemiology
Background:
- The COVID-19 pandemic has generated vast amounts of open data for research.
- Existing research often lacks integrated social dynamics data.
- There is a need for comprehensive datasets to analyze the pandemic's societal impact.
Purpose of the Study:
- To present a large-scale, curated dataset of COVID-19 related tweets.
- To provide a freely available resource for researchers worldwide.
- To enable diverse research projects analyzing social dynamics during the pandemic.
Main Methods:
- Collected over 1.12 billion tweets related to COVID-19.
- Data curated from January 1, 2020, to June 27, 2021.
- Dataset is continuously growing.
Main Results:
- A substantial dataset of public sentiment and discussion is now available.
- The dataset facilitates near real-time analysis of pandemic-related trends.
- Enables research into misinformation, mental health, and social distancing impacts.
Conclusions:
- The curated tweet dataset is a valuable resource for interdisciplinary research.
- It supports epidemiological and social science analyses of the COVID-19 pandemic.
- Facilitates a deeper understanding of the pandemic's societal dimensions.
More Related Videos
Related Concept Videos
01:25Statistical Methods for Analyzing Epidemiological Data
480
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
480
01:18Steps in Outbreak Investigation
170
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
170
01:05Single Nucleotide Polymorphisms-SNPs
15.6K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.6K
01:12Statistical Software for Data Analysis and Clinical Trials
731
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
731
01:08Data Collection by Observations
12.4K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
12.4K
02:46Chi-square Analysis
38.6K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
38.6K