Using Causality to Infer Coordinated Attacks in Social Media
<|reference_start|>Using Causality to Infer Coordinated Attacks in Social Media: The rise of social media has been accompanied by a dark side with the ease of creating fake accounts and disseminating misinformation through coordinated attacks. Existing methods to identify such attacks often rely on thematic similarities or network-based approaches, overlooking the intricate causal relationships that underlie coordinated actions. This work introduces a novel approach for detecting coordinated attacks using Convergent Cross Mapping (CCM), a technique that infers causality from temporal relationships between user activity. We build on the theoretical framework of CCM by incorporating topic modelling as a basis for further optimizing its performance. We apply CCM to real-world data from the infamous IRA attack on US elections, achieving F1 scores up to 75.3% in identifying coordinated accounts. Furthermore, we analyse the output of our model to identify the most influential users in a community. We apply our model to a case study involving COVID-19 anti-vax related discussions on Twitter. Our results demonstrate the effectiveness of our model in uncovering causal structures of coordinated behaviour, offering a promising avenue for mitigating the threat of malicious campaigns on social media platforms.<|reference_end|>
@article{manchanayaka2024using,
    title={Using Causality to Infer Coordinated Attacks in Social Media},
    author={Isura Manchanayaka, Zainab Razia Zaidi, Shanika Karunasekera,
  Christopher Leckie},
    journal={arXiv preprint arXiv:2407.11690},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11690},
    primaryClass={cs.SI}
}