Imbalance Node Classification with Graph Neural Networks (GNN): A Study on a Twitter Dataset

Social networks produce a large volume of infor-mation, a part of which is fake. Social media platforms do a good job in moderating content and banning fake news spreaders, but a proactive solution is more desirable especially during global threats like COVID-19 pandemic and war. A proactive solution would be to ban users who spread fake news before they become important spreaders. In this paper we propose to model user’s interactions in a social media platform as a graph and then evaluate state of the art (SOTA) graph neural networks (GNN) that can classify users’ (nodes) profiles as being suspended or not. As with other real world data, we are faced with the imbalanced data problem and we evaluate different algorithms that try to fix this issue. Data used for this study were collected from X (Twitter) by using Twitter API 1.1 from November 2021 to July 2022 with the focus to collect information spread through tweets about vaccines. The aim of this paper is to evaluate if current models can deal with real world imbalanced data.

@article{Kika2023,
  title     = {Imbalance Node Classification with Graph Neural Networks (GNN): A Study on a Twitter Dataset},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Alda Kika and Arber Ceni and Denada Collaku and Emiranda Loka and Ledia Bozo and Klesti Hoxha},
  doi       = {10.14569/IJACSA.2023.01411140},
  url       = {https://doi.org/10.14569/IJACSA.2023.01411140}
}