A second look at SecondLook: Design iterations and usability of digital dating abuse detection and awareness app

T, Roy; Young, Eleanor; Hodges, Larry F.

doi:10.1109/ichi48887.2020.9374325

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Our research on digital dating abuse is closely related to the work mentioned above (Roy et al 2016;Roy, Young, and Hodges 2020). As digital dating abuse falls under interpersonal violence, we predicted that we could use similar machine-learning methods to conduct our studies.…”

Section: Introductionmentioning

confidence: 91%

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

Maranzatto

Loomas

2023

FLAIRS

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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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Section: Introductionmentioning

confidence: 91%