Using machine learning algorithms (supervised) to generate automatically labeled dataset for detecting digital dating abuse from text messages
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 c...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133332 |
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| Summary: | 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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| ISSN: | 2334-0754 2334-0762 |