Detecting Bots on Social Media Using Graph Neural Networks
Author Information
Author(s): Tzoumanekas Georgios, Chatzianastasis Michail, Ilias Loukas, Kiokes George, Psarras John, Askounis Dimitris
Primary Institution: National Technical University of Athens
Hypothesis
Can a Neural Architecture Search approach improve bot detection in social media by leveraging graph structures?
Conclusion
The proposed model achieved an accuracy of 85.68%, surpassing state-of-the-art models in bot detection.
Supporting Evidence
- The model constructed a graph with 229,580 nodes and 227,979 edges.
- DFG-NAS was adapted to automatically search for optimal configurations in the bot detection task.
- The model achieved an accuracy of 85.7%, surpassing existing models.
- User metadata was integrated into the graph to enhance detection capabilities.
- An ablation study confirmed the importance of using all features for optimal performance.
Takeaway
This study created a smart computer program that can find fake accounts on social media by looking at how users are connected to each other.
Methodology
The study used a Deep and Flexible Graph Neural Architecture Search (DFG-NAS) to optimize bot detection through Relational Graph Convolutional Networks (RGCNs) on the TwiBot-20 dataset.
Potential Biases
The model's performance may be affected by the quality and representativeness of the labeled data used for training.
Limitations
The study was conducted on a single dataset, which may limit the generalizability of the findings.
Participant Demographics
The dataset includes user profiles from the TwiBot-20 dataset, which consists of both bot and human accounts.
Statistical Information
P-Value
0.004
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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