Project: Social Media Data Analysis - Twitter Case Study
Description
In this project, we will analyze social media dataset to answer interesting questions about human behavior. We aim to study biases using social media data and propose fair solutions. The project also aims to model human behavior on social media (depends on the topic).
This project has a very vast object and several sub-projects. Therefore, if you want to work on this please contact me at (a.saxena@tue.nl) and we can discuss that further.
Some of the sub-projects are:
How to model influence propagation on Twitter.
Identify the impact of network structure on information propagation.
Identify influential leaders on Twitter
Identify bias and inequalities using social media data analysis.
Quantify Gender Gap using Twitter dataset
Measuring polarization on Twitter Communication
Each subproject will require you to read different kinds of papers. Still the following papers will give you a better understanding of the topic.
- Saxena, Akrati, George Fletcher, and Mykola Pechenizkiy. "FairSNA: Algorithmic Fairness in Social Network Analysis." arXiv preprint arXiv:2209.01678 (2022).
- Riquelme, Fabián, and Pablo González-Cantergiani. "Measuring user influence on Twitter: A survey." Information processing & management 52, no. 5 (2016): 949-975.
Conover, Michael, Jacob Ratkiewicz, Matthew Francisco, Bruno Gonçalves, Filippo Menczer, and Alessandro Flammini. "Political polarization on twitter." In Proceedings of the international aaai conference on web and social media, vol. 5, no. 1, pp. 89-96. 2011.
Details
- Supervisor
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George Fletcher
- Secondary supervisor
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Akrati Saxena
- Interested?
- Get in contact