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Machine learning to detect invalid text responses: Validation and comparison to existing detection methods.

Ryan C Yeung1, Myra A Fernandes2

  • 1Department of Psychology, University of Waterloo, Psychology, Anthropology, and Sociology (PAS) Building, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada. rcyeung@uwaterloo.ca.

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This summary is machine-generated.

Researchers developed a machine learning method to automatically detect and remove invalid text data, improving data quality for studies like autobiographical memory research. This approach matches human accuracy without manual coding.

Keywords:
Autobiographical memoryCareless respondingMachine learningText as dataText classification

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Psychology

Background:

  • Analyzing text data is vital for research areas like autobiographical memory.
  • Existing methods for identifying invalid text lack empirical validation and practicality.
  • There is a need for effective, automated solutions to ensure text data quality.

Purpose of the Study:

  • To propose and implement a supervised machine learning approach for detecting invalid text.
  • To validate the performance of machine learning against human coding and existing indicators.
  • To provide an accessible tool for improving text data quality in research.

Main Methods:

  • A supervised machine learning model was trained and validated on manually labeled text data (valid/invalid).
  • Performance metrics were calculated to select the optimal model.
  • The model was used to predict the validity of unlabeled text data based on content alone.

Main Results:

  • Machine learning models accurately detected invalid autobiographical memory texts.
  • Performance approached human coding accuracy.
  • The proposed method significantly outperformed traditional data quality indicators.

Conclusions:

  • Supervised machine learning offers an effective and practical solution for identifying invalid text data.
  • This approach enhances data quality in text-based research without requiring extensive manual annotation.
  • Openly available code facilitates broader adoption and improved research integrity.