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.
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