Mining tweets on sexual violence in South Africa
Date
2020
Authors
Oyasor, Jude Imuede
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The “wisdom of the crowds” emerges from mining tweets incited by the behaviour of the masses.
South Africans have resorted to venting their frustrations regarding the state of Gender-based
violence (GBV) on social media. This is done in a bid to seek justice and raise awareness of the
issue at large. Sexual violence is a kind of GBV that is widespread in South Africa, and Twitter is
that popular social media and micro-blogging service where these frustrations are mostly reported
as tweets. An effective analysis of Twitter data on sexual violence can expose unknown information
and provide insights leading to better mitigating strategies. Within this context, this research
investigates the disparity in reported cases of sexual violence in terms of gender representation and
sentiment expression based on geolocation. It is envisaged that this information can be used in the
form of an interactive data visualisation tool that policy analysts can use to investigate counterpreventive
measures. Moreover, law enforcement agencies can better allocate their resources to
help mitigate this problem. To do this, we achieved the development of a web-based, AI-driven
application that automatically reveals the gender and sentiments in a tweet document. The system
is built using the Estimator framework, a TensorFlow high-level-API which simplifies machine
learning programming and model development. The process is initiated by the Indexer which is
a server-side Node.js application that runs persistently and enables streaming of Twitter data for
gender prediction, sentiment analysis and visualisation of GBV in South Africa
Description
A dissertation submitted to the Faculty of Science in fulfillment of the requirements for
the degree of Master of Science (M.Sc) in Computer Science, University of the Witwatersrand, Johannesburg, 2020