Name
Abstract | ||
|---|---|---|
Digital dating abuse is carried out using text messages, emails and social media sites and has become a major mental health crisis among the college-going population. Existing technology and non-technology based intervention programs do not provide assistance at the onset of abuse. The ultimate goal of this project is to create a mobile phone application that can detect digital dating abuse. As a step toward this goal we are investigating the use of machine learning algorithms and natural language processing techniques to flag text messages as abusive or non-abusive in the context of dating abuse. Due to the lack of a publicly available dataset that could be used to create training and testing sets for the classifiers, we first had to create and validate a dataset of abusive text messages. This paper describes the dataset creation process and the results of an evaluation of different classification and feature extraction techniques used to detect abusive texts. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICHI.2018.00039 | 2018 IEEE International Conference on Healthcare Informatics (ICHI) |
Keywords | Field | DocType |
digital dating abuse,dataset creation and validation,classifiers,machine learning | Population,Social media,Information retrieval,Computer science,Support vector machine,Feature extraction,Mental health,Mobile phone,Statistical classification | Conference |
ISBN | Citations | PageRank |
978-1-5386-5378-4 | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tania Roy | 1 | 29 | 2.60 |
| Jerome Mcclendon | 2 | 18 | 5.21 |
| Larry F Hodges | 3 | 14 | 2.72 |