Detecting Bots in Social-networks using Node and Structural Embeddings

Ashkan Dehghan, Kinga Siuta, Kinga Siuta, Agata Skorupka, Agata Skorupka, Akshat Dubey, Andrei Betlen, David Miller, Wei Xu, Bogumił Kamiński, Paweł Prałat

2022

Abstract

Users on social networks such as Twitter interact with and are influenced by each other without much knowledge of the identity behind each user. This anonymity has created a perfect environment for bot and hostile accounts to influence the network by mimicking real-user behaviour. To combat this, research into designing algorithms and datasets for identifying bot users has gained significant attention. In this work, we highlight various techniques for classifying bots, focusing on the use of node and structural embedding algorithms. We show that embeddings can be used as unsupervised techniques for building features with predictive power for identifying bots. By comparing features extracted from embeddings to other techniques such as NLP, user profile and node-features, we demonstrate that embeddings can be used as unique source of predictive information. Finally, we study the stability of features extracted using embeddings for tasks such as bot classification by artificially introducing noise in the network. Degradation of classification accuracy is comparable to models trained on carefully designed node features, hinting at the stability of embeddings.

Paper Citation

in Harvard Style

Dehghan A., Siuta K., Skorupka A., Dubey A., Betlen A., Miller D., Xu W., Kamiński B. and Prałat P. (2022). Detecting Bots in Social-networks using Node and Structural Embeddings. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 50-61. DOI: 10.5220/0011147300003269

in Bibtex Style

@conference{data22,
author={Ashkan Dehghan and Kinga Siuta and Agata Skorupka and Akshat Dubey and Andrei Betlen and David Miller and Wei Xu and Bogumił Kamiński and Paweł Prałat},
title={Detecting Bots in Social-networks using Node and Structural Embeddings},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={50-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011147300003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Detecting Bots in Social-networks using Node and Structural Embeddings
SN - 978-989-758-583-8
AU - Dehghan A.
AU - Siuta K.
AU - Skorupka A.
AU - Dubey A.
AU - Betlen A.
AU - Miller D.
AU - Xu W.
AU - Kamiński B.
AU - Prałat P.
PY - 2022
SP - 50
EP - 61
DO - 10.5220/0011147300003269