Unmasking Social Robots’ Camouflage: A GNN-Random Forest Framework for Enhanced Detection
Source abstract
Abstract
The proliferation of robot accounts on social media platforms has posed a significant negative impact, necessitating robust measures to counter network anomalies and safeguard content integrity. Social robot detection has eme... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Video Surveillance and Tracking Methods · Face recognition and analysis · Advanced Image and Video Retrieval Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Disinformation’s spread: Bots, trolls and all of us10.1038/d41586-019-02235-x ↗
2. Detecting political bots on Twitter during the 2019 Finnish parliamentary election
3. Online conspiracy groups: Micro-bloggers, bots, and coronavirus conspiracy talk on Twitter10.1177/00031224221125937 ↗
4. Fame for sale: Efficient detection of fake Twitter followers10.1016/j.dss.2015.09.003 ↗
5. The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
6. The rise of social bots10.1145/2818717 ↗
7. Scalable and generalizable social bot detection through data selection10.1609/aaai.v34i01.5460 ↗
8. RTbust: Exploiting temporal patterns for botnet detection on Twitter
9. BotRGCN: Twitter bot detection with relational graph convolutional networks
10. Heterogeneity-aware twitter bot detection with relational graph transformers10.1609/aaai.v36i4.20314 ↗
11. S. Shi et al., “MGTAB: A multi-relational graph-based twitter account detection benchmark,” 2023. doi: 10.48550/arXiv.2301.01123.
12. Twitter bot detection using bidirectional long short-term memory neural networks and word embeddings
13. Language modeling on location-based social networks
14. Are you human? Detecting bots on twitter using BERT
15. Using BERT to extract topic-independent sentiment features for social media bot detection
16. A decade of social bot detection10.1145/3409116 ↗
17. Detect me if you can: Spam bot detection using inductive representation learning
18. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” 2016. doi: 10.48550/arXiv.1609.02907.
19. Socialbots on fire: Modeling adversarial behaviors of socialbots via multi-agent hierarchical reinforcement learning
20. SATAR: A self-supervised approach to twitter account representation learning and its application in bot detection
21. Political bots and the Swedish general election
22. Detecting bots and assessing their impact in social networks10.1287/opre.2021.2118 ↗
23. RoSGAS: Adaptive social bot detection with reinforced self-supervised GNN architecture search10.1145/3572403 ↗
24. Wu · Advances in Knowledge Discovery and Data Mining · 2021 · 12713 · 156
25. H. Pei, B. Wei, K. C. -C. Chang, Y. Lei, and B. Yang, “Geom-GCN: Geometric graph convolutional networks,” 2020. doi: 10.48550/arXiv.2002.05287.
26. Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
27. Beyond homophily in graph neural networks: Current limitations and effective designs
28. Graph neural networks with heterophily
29. Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu and L. Wang, “Deep graph contrastive representation learning,” 2020. doi: 10.48550/arXiv.2006.04131.
30. P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio and R. D. Hjelm, “Deep graph infomax,” 2018. doi: 10.48550/arXiv.1809.10341.
31. Weakly supervised contrastive learning
32. Supervised contrastive learning
33. S. Luan et al., “Is heterophily a real nightmare for graph neural networks to do node classification?” 2021. doi: 10.48550/arXiv.2109.05641.
34. TwiBot-20: A comprehensive twitter bot detection benchmark
35. TwiBot-22: Towards graph-based twitter bot detection
36. Y. Liu et al., “RoBERTa: A robustly optimized bert pretraining approach,” 2019. doi: 10.48550/arXiv.1907.11692.
37. LOBO: Evaluation of generalization deficiencies in twitter bot classifiers
38. Simplifying graph convolutional networks
39. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio and Y. Bengio, “Graph attention networks,” 2017. doi: 10.48550/arXiv.1710.10903.
40. Boosting-GNN: Boosting algorithm for graph networks on imbalanced node classification10.3389/fnbot.2021.775688 ↗
41. BotBuster: Multi-platform bot detection using a mixture of experts10.1609/icwsm.v17i1.22179 ↗
42. BIC: Twitter bot detection with text-graph interaction and semantic consistency