Term of Award
Summer 2019
Degree Name
Master of Science, Information Technology
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Information Technology
Committee Chair
Jeffrey Kaleta
Committee Member 1
Cheryl Aasheim
Committee Member 2
Christopher Kadlec
Abstract
Last few years have seen tremendous development in neural language modeling for transfer learning and downstream applications. In this research, I used Howard and Ruder’s Universal Language Model Fine Tuning (ULMFiT) pipeline to develop a classifier that can determine whether a tweet is politically left leaning or right leaning by likening the content to tweets posted by @TheDemocrats or @GOP accounts on Twitter. We achieved 87.7% accuracy in predicting political ideological inclination.
Recommended Citation
Iqbal, Mehtab, "Determining Political Inclination in Tweets Using Transfer Learning" (2019). Electronic Theses and Dissertations. 1988.
https://digitalcommons.georgiasouthern.edu/etd/1988
Research Data and Supplementary Material
No
Included in
Artificial Intelligence and Robotics Commons, Social Influence and Political Communication Commons, Social Media Commons
