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Authors: Ashkan Dehghan 1 ; Kinga Siuta 1 ; 2 ; Agata Skorupka 1 ; 2 ; Akshat Dubey 1 ; Andrei Betlen 3 ; David Miller 3 ; Wei Xu 1 ; Bogumił Kamiński 2 and Paweł Prałat 1

Affiliations: 1 Ryerson University, Toronto, ON, Canada ; 2 SGH Warsaw School of Economics, Warsaw, Poland ; 3 Patagona Technologies, Pickering, ON, Canada

Keyword(s): Structural and Node Embeddings, Detecting Bots, Social Networks.

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 introd ucing noise in the network. Degradation of classification accuracy is comparable to models trained on carefully designed node features, hinting at the stability of embeddings. (More)

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Paper citation in several formats:
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 - DATA, ISBN 978-989-758-583-8; ISSN 2184-285X, pages 50-61. DOI: 10.5220/0011147300003269

@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 - DATA,},
year={2022},
pages={50-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011147300003269},
isbn={978-989-758-583-8},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA,
TI - Detecting Bots in Social-networks using Node and Structural Embeddings
SN - 978-989-758-583-8
IS - 2184-285X
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