Classifying #MeToo Hash-tagged Tweets by Semantics to Understand the Extent of Sexual Harassment

Hubacek, Claire (2018) Classifying #MeToo Hash-tagged Tweets by Semantics to Understand the Extent of Sexual Harassment. Undergraduate thesis, under the direction of Naeemul Hassan from Computer and Information Science, The University of Mississippi.

Honors_Thesis (2).pdf

Download (3MB) | Preview


This thesis contains a program that will process tweets from Twitter that use the hashtag "#MeToo" and categorize them by their relevance to the movement, their stance on the movement, and the type of sexual harassment expressed (if applicable). Being able to work with a narrowed set of tweets belonging to a specific category creates the capacity to do more in-depth research and analysis, exploring Twitter as a special platform for discussing these sensitive topics and showing that this online space for expressing personal experiences has delivered unprecedented potential avenues of study. This thesis also contains research into additional solutions towards addressing sexual harassment online, exploring the needs of society through the results to a questionnaire that was administered to university students asking for opinions on how sexual harassment is addressed on social media as well as through a literature review of current obstacles for victims.

Item Type: Thesis (Undergraduate)
Creators: Hubacek, Claire
Student's Degree Program(s): B.S. in Computer and Information Science and B.A. in Art and Art History
Thesis Advisor: Naeemul Hassan
Thesis Advisor's Department: Computer and Information Science
Institution: The University of Mississippi
Subjects: K Law > K Law (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Depositing User: Ms Claire Hubacek
Date Deposited: 11 May 2018 19:17
Last Modified: 11 May 2018 19:17

Actions (login required)

View Item View Item