#WhyIDidntReport: Using Social Media as a Tool to Understand Why Sexual Assault Victims Do Not Report

Garrett, Abby (2019) #WhyIDidntReport: Using Social Media as a Tool to Understand Why Sexual Assault Victims Do Not Report. Undergraduate thesis, under the direction of Naeemul Hassan from Computer and Information Science, University of Mississippi.


Download (1MB) | Preview


Sexual assault has gone largely under-reported, and social media movements, like #WhyIDidntReport, have brought great awareness to this issue. In order to take advantage of the large amounts of data the #WhyIDidntReport movement has generated, the study uses tweets to explore reasons why victims do not report their assault. The thesis cites current research on the topic of assault to generate a list of explanations victims use to describe their lack of reporting and compares the distributions with existing studies. We use a supervised learning technique to automatically categorize tweets into one of eight categories. This approach uses social sensing to determine why people do not report rather than surveys and interviews like current research. The machine learning algorithms used to categorize the tweets as having a reason or not are Naive Bayes, Random Forest, and Recurrent Neural Networks. Only Naive Bayes and Random Forest were used for categorizing the reasons because there was not enough data to train large numbers of parameters of RNN. Each algorithm produces relatively precise results for the binary classification and categorizing whether a tweet references shame, denial/minimization, fear of consequences, hopelessness/helplessness, drugs or drinking or disassociation, lack of information, protecting the assailant, or age as the reason they did not report. These algorithms and tweets can be used to label data in future studies. Using the current research, natural language processing, and machine learning, we were able to determine a list of reasons mentioned on Twitter under the #WhyIDidntReport movement. The distribution of the reasons differed from current research, most likely as a result of the form of data collection. However, the categories themselves were consistent with findings from other studies. The use of social sensing to determine reasons presents a new perspective on the topic and allows for comparison with other research.

Item Type: Thesis (Undergraduate)
Creators: Garrett, Abby
Student's Degree Program(s): BA Computer Science and BS Mathematics
Thesis Advisor: Naeemul Hassan
Thesis Advisor's Department: Computer and Information Science
Institution: University of Mississippi
Subjects: T Technology > T Technology (General)
Depositing User: Abby Garrett
Date Deposited: 10 May 2019 04:44
Last Modified: 10 May 2019 04:44
URI: http://thesis.honors.olemiss.edu/id/eprint/1410

Actions (login required)

View Item View Item