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Related Concept Videos

Random Sampling Method01:09

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Systematic Sampling Method01:17

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Updated: May 25, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Comparing methods for creating a national random sample of twitter users.

Meysam Alizadeh1, Darya Zare2, Zeynab Samei3

  • 1Department of Political Science, University of Zurich, Zurich, Switzerland.

Social Network Analysis and Mining
|February 28, 2025
PubMed
Summary
This summary is machine-generated.

The 1% Stream method best samples US Twitter users for population accuracy but is slow and real-time only. The Bounding Box method is a suitable alternative when historical data or user engagement is crucial.

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Area of Science:

  • Social Sciences
  • Computer Science
  • Data Science
  • Computational Social Science

Background:

  • Twitter data is extensively utilized in social and computer science research.
  • A frequent research objective involves creating representative samples of users from specific countries.
  • Existing methods for sampling Twitter users lack comprehensive comparative analysis.

Purpose of the Study:

  • To implement and compare four common methods for generating random Twitter user samples in the US: 1% Stream, Bounding Box, Location Query, and Language Query.
  • To evaluate these methods based on tweet- and user-level metrics and their accuracy in estimating the US population.

Main Methods:

  • Implementation of four distinct sampling strategies: 1% Stream, Bounding Box, Location Query, and Language Query.
  • Comparative analysis of collected data using tweet-level metrics (e.g., tweet frequency) and user-level metrics (e.g., followers, account age).
  • Assessment of population estimation accuracy for each sampling method.

Main Results:

  • The 1% Stream method yielded users with higher tweet volume, engagement metrics, and younger accounts, while also showing the lowest population estimation error.
  • Users sampled via 1% Stream were disproportionately male compared to other methods.
  • The 1% Stream method is time-intensive, limited to real-time data, and unsuitable for studies focusing on user engagement.

Conclusions:

  • The 1% Stream method is superior for obtaining a statistically accurate US Twitter user sample for population estimation.
  • The Bounding Box method emerges as a viable alternative when the limitations of the 1% Stream (time, historical data, engagement focus) are critical research constraints.