Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Archival Research01:40

Archival Research

16.0K
Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
16.0K
Data Reporting and Recording01:24

Data Reporting and Recording

4.8K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
4.8K
Data Collection by Experiments01:13

Data Collection by Experiments

24.5K
Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
24.5K
Introduction to R01:11

Introduction to R

466
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
466
Bootstrapping01:24

Bootstrapping

650
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
650
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

697
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...
697

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

CitNAC71-CitNAC76 coordinate cellulose and hemicellulose biosynthesis to regulate high-temperature-mediated granulation in citrus.

The Plant cell·2026
Same author

Body fat distributions differentiate the risk of type 2 diabetes in women with a history of gestational diabetes.

Diabetes research and clinical practice·2026
Same author

Esaxerenone Attenuates Aldosterone-Induced Renal Fibrosis by Suppressing Fibroblast-to-Lymphatic Endothelial-like Cell Transdifferentiation.

International journal of molecular sciences·2026
Same author

A Fine-Tuned Multimodal AI Chatbot for Dietary Health and Nutrition, Purrfessor: Development and Mixed Methods Evaluation.

JMIR AI·2026
Same author

Can Algorithms Efficiently Identify Interpretable and Persuasive Message Features? An Agnostic Causal Machine Learning Approach.

Health communication·2026
Same author

Identifying Persuasive Visual Features within Tobacco Pictorial Warnings: Effects on Anticipated Loss of Face, Gifting, and Refrain Intentions Among Chinese Men Who Smoke.

Journal of health communication·2026

Related Experiment Video

Updated: Aug 8, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.2K

Twitter as research data Tools, costs, skill sets, and lessons learned.

Kaiping Chen1, Zening Duan2, Sijia Yang2

  • 1University of Wisconsin-Madison, kchen67@wisc.edu.

Politics and the Life Sciences : the Journal of the Association for Politics and the Life Sciences
|March 6, 2023
PubMed
Summary

Scholars need to carefully evaluate Twitter data collection tools. This study assesses tool costs, training, and data quality, finding that samples may not represent the full Twitter archive.

Keywords:
Twittercomputational social sciencecostdata collection toolsdata quality evaluationskill sets

More Related Videos

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.6K
Measuring the Switch Cost of Smartphone Use While Walking
07:00

Measuring the Switch Cost of Smartphone Use While Walking

Published on: April 30, 2020

1.9K

Related Experiment Videos

Last Updated: Aug 8, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.2K
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.6K
Measuring the Switch Cost of Smartphone Use While Walking
07:00

Measuring the Switch Cost of Smartphone Use While Walking

Published on: April 30, 2020

1.9K

Area of Science:

  • Social Sciences
  • Computational Social Science
  • Digital Humanities

Background:

  • Scholars increasingly utilize Twitter data for life sciences and political research.
  • Challenges exist in using Twitter data collection tools, particularly for novice researchers.
  • Concerns remain regarding the representativeness of Twitter data samples offered by various tools.

Purpose of the Study:

  • To evaluate Twitter data collection tools based on cost, training requirements, and data quality.
  • To introduce Twitter data as a viable research tool for scholarly inquiry.
  • To assess the representativeness of Twitter data samples compared to the full Twitter archive.

Main Methods:

  • Comparative analysis of Twitter data collection tools (standard APIs vs. third-party access).
  • Evaluation of tool costs, training needs, and data quality.
  • Analysis of COVID-19 and moral foundations theory discussions using Twitter data samples and the full archive.

Main Results:

  • Twitter data collection tools vary significantly in cost and training demands.
  • Data samples from commonly used tools may not accurately represent the full Twitter archive.
  • Discrepancies were observed in the distribution of moral discussions between sampled data and the full archive.

Conclusions:

  • Researchers must critically assess the comparability of Twitter data sources to ensure findings are robust.
  • Understanding the limitations of Twitter data samples is crucial for reliable social science research.
  • The study reviews new features in Twitter's API version 2, offering insights for future data collection.