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Related Concept Videos

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
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Related Experiment Video

Updated: Sep 12, 2025

The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory
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Quantifying Controversy: A Novel Approach to Detecting Misinformation.

Oliver Li1, Sahiti Myneni2, Trevor Cohen1

  • 1University of Washington.

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Social media misinformation harms public health. This study introduces a novel controversy measurement to understand user responses, aiding in developing interventions against health misinformation.

Keywords:
LLMsQuantifying ControversyStance Detection

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Area of Science:

  • Public Health
  • Social Media Analysis
  • Computational Linguistics

Background:

  • Misinformation on social media poses significant challenges to public health initiatives.
  • Understanding user reception and response to misinformation is crucial for effective intervention design.

Purpose of the Study:

  • To develop a novel metric for measuring the controversy of social media posts.
  • To investigate the relationship between misinformation and post controversy.
  • To inform the development of public health interventions targeting misinformation.

Main Methods:

  • Utilized in-context learning with Llama 3.1 for stance detection on social media posts, achieving high accuracy on COVID-19 datasets.
  • Developed a controversy metric based on the entropy of predicted user stances in response to posts.
  • Evaluated the hypothesis that posts containing misinformation exhibit higher controversy.

Main Results:

  • Achieved 85.3% and 71.8% accuracy in COVID-19 stance detection using Llama 3.1.
  • Demonstrated a method to quantify social media post controversy based on stance variability.
  • Provided empirical evidence supporting the link between misinformation and increased post controversy.

Conclusions:

  • The developed controversy metric offers a new lens for analyzing information reception on social media.
  • Findings suggest that controversial posts may be a key indicator of misinformation presence.
  • This research has implications for designing targeted public health strategies to combat online misinformation.