Honors Theses
Date of Award
2018
Document Type
Undergraduate Thesis
Department
Computer and Information Science
First Advisor
Naeemul Hassan
Relational Format
Dissertation/Thesis
Abstract
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.
Recommended Citation
Hubacek, Claire, "Classifying #MeToo Hash-tagged Tweets by Semantics to Understand the Extent of Sexual Harassment" (2018). Honors Theses. 206.
https://egrove.olemiss.edu/hon_thesis/206
Accessibility Status
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