Using machine learning algorithms (supervised) to generate automatically labeled dataset for detecting digital dating abuse from text messages

Authors

  • Tania Roy New College of Florida
  • Thomas Maranzatto University of Illinois at Chicago https://orcid.org/0000-0002-6105-2758
  • Zachary Loomas New College of Florida

DOI:

https://doi.org/10.32473/flairs.36.133332

Abstract

Digital dating abuse is a form of intimate partner violence that uses technology as a medium to propagate fear and cause harm for dating partners. Over several years digital dating abuse has been on the rise, and particularly during COVID-19, the issue has risen exponentially. This project aims to create a tool that raises awareness and detects digital dating from text messages. Previously, we generated a dataset with expert labelers to use supervised machine learning algorithms for abuse detection. However, the cost and time associated with generating human-annotated datasets limit the size of these verified datasets. This poster explores using machine learning algorithms trained on human-annotated datasets to label more extensive crowd-sourced datasets and generate a larger training dataset for abuse detection algorithms. We used Naive Bayes, Decision Tree, LSVM, and LSTM to test for accuracy and speed of labeling this more extensive dataset.

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Published

08-05-2023

How to Cite

Roy, T., Maranzatto, T. ., & Loomas, Z. . (2023). Using machine learning algorithms (supervised) to generate automatically labeled dataset for detecting digital dating abuse from text messages. The International FLAIRS Conference Proceedings, 36(1). https://doi.org/10.32473/flairs.36.133332