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Natural language model for automatic identification of Intimate Partner Violence reports from Twitter.

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Array (New York, N.Y.)
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Researchers developed an AI model to automatically detect intimate partner violence (IPV) reports on social media. This tool can improve public health surveillance and aid intervention efforts for victims.

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Area of Science:

  • Public Health
  • Computer Science
  • Social Science

Background:

  • Intimate partner violence (IPV) is a widespread public health issue affecting millions globally.
  • Social media platforms are increasingly used by victims to disclose IPV experiences.
  • Existing methods for identifying IPV reports online are limited, hindering timely support and surveillance.

Purpose of the Study:

  • To address the research gap by developing an automated system for detecting IPV reports on social media.
  • To enable improved public health surveillance and facilitate targeted interventions for IPV victims.
  • To create a foundation for proactive social media-based support frameworks.

Main Methods:

  • Collected Twitter posts using IPV-related keywords.
  • Developed annotation guidelines and manually categorized tweets into IPV-report or non-IPV-report categories.
  • Trained and evaluated a natural language processing (NLP) model for automatic IPV-report classification, achieving high F1-scores.

Main Results:

  • Annotated a dataset of 6,348 tweets with high inter-annotator agreement (Cohen's kappa = 0.86).
  • The NLP model achieved an F1-score of 0.76 for the IPV-report class and 0.97 for the non-IPV-report class.
  • Post-classification analysis confirmed the model's effectiveness and assessed for potential biases related to race and gender.

Conclusions:

  • The developed AI model demonstrates significant potential for automatically identifying intimate partner violence reports on social media.
  • This technology can serve as a crucial component for real-time public health surveillance and large-scale cohort studies.
  • The system facilitates proactive, data-driven interventions and support for individuals experiencing IPV.