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Detecting bots in social-networks using node and structural embeddings

Users on social networks such as Twitter interact with each other without much knowledge of the real-identity behind the accounts they interact with. This anonymity has created a perfect environment for bot accounts to influence the network by mimicking real-user behaviour. Although not all bot acco...

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Autores principales: Dehghan, Ashkan, Siuta, Kinga, Skorupka, Agata, Dubey, Akshat, Betlen, Andrei, Miller, David, Xu, Wei, Kamiński, Bogumił, Prałat, Paweł
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356665/
https://www.ncbi.nlm.nih.gov/pubmed/37483882
http://dx.doi.org/10.1186/s40537-023-00796-3
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author Dehghan, Ashkan
Siuta, Kinga
Skorupka, Agata
Dubey, Akshat
Betlen, Andrei
Miller, David
Xu, Wei
Kamiński, Bogumił
Prałat, Paweł
author_facet Dehghan, Ashkan
Siuta, Kinga
Skorupka, Agata
Dubey, Akshat
Betlen, Andrei
Miller, David
Xu, Wei
Kamiński, Bogumił
Prałat, Paweł
author_sort Dehghan, Ashkan
collection PubMed
description Users on social networks such as Twitter interact with each other without much knowledge of the real-identity behind the accounts they interact with. This anonymity has created a perfect environment for bot accounts to influence the network by mimicking real-user behaviour. Although not all bot accounts have malicious intent, identifying bot accounts in general is an important and difficult task. In the literature there are three distinct types of feature sets one could use for building machine learning models for classifying bot accounts. These feature-sets are: user profile metadata, natural language features (NLP) extracted from user tweets and finally features extracted from the the underlying social network. Profile metadata and NLP features are typically explored in detail in the bot-detection literature. At the same time less attention has been given to the predictive power of features that can be extracted from the underlying network structure. To fill this gap we explore and compare two classes of embedding algorithms that can be used to take advantage of information that network structure provides. The first class are classical embedding techniques, which focus on learning proximity information. The second class are structural embedding algorithms, which capture the local structure of node neighbourhood. We show that features created using structural embeddings have higher predictive power when it comes to bot detection. This supports the hypothesis that the local social network formed around bot accounts on Twitter contains valuable information that can be used to identify bot accounts.
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spelling pubmed-103566652023-07-21 Detecting bots in social-networks using node and structural embeddings Dehghan, Ashkan Siuta, Kinga Skorupka, Agata Dubey, Akshat Betlen, Andrei Miller, David Xu, Wei Kamiński, Bogumił Prałat, Paweł J Big Data Research Users on social networks such as Twitter interact with each other without much knowledge of the real-identity behind the accounts they interact with. This anonymity has created a perfect environment for bot accounts to influence the network by mimicking real-user behaviour. Although not all bot accounts have malicious intent, identifying bot accounts in general is an important and difficult task. In the literature there are three distinct types of feature sets one could use for building machine learning models for classifying bot accounts. These feature-sets are: user profile metadata, natural language features (NLP) extracted from user tweets and finally features extracted from the the underlying social network. Profile metadata and NLP features are typically explored in detail in the bot-detection literature. At the same time less attention has been given to the predictive power of features that can be extracted from the underlying network structure. To fill this gap we explore and compare two classes of embedding algorithms that can be used to take advantage of information that network structure provides. The first class are classical embedding techniques, which focus on learning proximity information. The second class are structural embedding algorithms, which capture the local structure of node neighbourhood. We show that features created using structural embeddings have higher predictive power when it comes to bot detection. This supports the hypothesis that the local social network formed around bot accounts on Twitter contains valuable information that can be used to identify bot accounts. Springer International Publishing 2023-07-19 2023 /pmc/articles/PMC10356665/ /pubmed/37483882 http://dx.doi.org/10.1186/s40537-023-00796-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Dehghan, Ashkan
Siuta, Kinga
Skorupka, Agata
Dubey, Akshat
Betlen, Andrei
Miller, David
Xu, Wei
Kamiński, Bogumił
Prałat, Paweł
Detecting bots in social-networks using node and structural embeddings
title Detecting bots in social-networks using node and structural embeddings
title_full Detecting bots in social-networks using node and structural embeddings
title_fullStr Detecting bots in social-networks using node and structural embeddings
title_full_unstemmed Detecting bots in social-networks using node and structural embeddings
title_short Detecting bots in social-networks using node and structural embeddings
title_sort detecting bots in social-networks using node and structural embeddings
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356665/
https://www.ncbi.nlm.nih.gov/pubmed/37483882
http://dx.doi.org/10.1186/s40537-023-00796-3
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