Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection

OSN platforms are under attack by intruders born and raised within their own ecosystems. These attacks have multiple scopes from mild critiques to violent offences targeting individual or community rights and opinions. Negative publicity on microblogging platforms, such as Twitter, is due to the inf...

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Autores principales:	Ilias Dimitriadis, Konstantinos Georgiou, Athena Vakali
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	anomaly detection bot detection data mining explainable AI social network analytics supervised classification Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/d4f88d8e09204d8db15c09aa074c526f
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spelling	oai:doaj.org-article:d4f88d8e09204d8db15c09aa074c526f2021-11-11T14:59:14ZSocial Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection10.3390/app112198572076-3417https://doaj.org/article/d4f88d8e09204d8db15c09aa074c526f2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9857https://doaj.org/toc/2076-3417OSN platforms are under attack by intruders born and raised within their own ecosystems. These attacks have multiple scopes from mild critiques to violent offences targeting individual or community rights and opinions. Negative publicity on microblogging platforms, such as Twitter, is due to the infamous Twitter bots which highly impact posts’ circulation and virality. A wide and ongoing research effort has been devoted to develop appropriate countermeasures against emerging “armies of bots”. However, the battle against bots is still intense and unfortunately, it seems to lean on the bot-side. Since, in an effort to win any war, it is critical to know your enemy, this work aims to demystify, reveal, and widen inherent characteristics of Twitter bots such that multiple types of bots are recognized and spotted early. More specifically in this work we: (i) extensively analyze the importance and the type of data and features used to generate ML models for bot classification, (ii) address the open problem of multi-class bot detection, identifying new types of bots, and share two new datasets towards this objective, (iii) provide new individual ML models for binary and multi-class bot classification and (iv) utilize explainable methods and provide comprehensive visualizations to clearly demonstrate interpretable results. Finally, we utilize all of the above in an effort to improve the so called Bot-Detective online service. Our experiments demonstrate high accuracy, explainability and scalability, comparable with the state of the art, despite multi-class classification challenges.Ilias DimitriadisKonstantinos GeorgiouAthena VakaliMDPI AGarticleanomaly detectionbot detectiondata miningexplainable AIsocial network analyticssupervised classificationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9857, p 9857 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	anomaly detection bot detection data mining explainable AI social network analytics supervised classification Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	anomaly detection bot detection data mining explainable AI social network analytics supervised classification Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Ilias Dimitriadis Konstantinos Georgiou Athena Vakali Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
description	OSN platforms are under attack by intruders born and raised within their own ecosystems. These attacks have multiple scopes from mild critiques to violent offences targeting individual or community rights and opinions. Negative publicity on microblogging platforms, such as Twitter, is due to the infamous Twitter bots which highly impact posts’ circulation and virality. A wide and ongoing research effort has been devoted to develop appropriate countermeasures against emerging “armies of bots”. However, the battle against bots is still intense and unfortunately, it seems to lean on the bot-side. Since, in an effort to win any war, it is critical to know your enemy, this work aims to demystify, reveal, and widen inherent characteristics of Twitter bots such that multiple types of bots are recognized and spotted early. More specifically in this work we: (i) extensively analyze the importance and the type of data and features used to generate ML models for bot classification, (ii) address the open problem of multi-class bot detection, identifying new types of bots, and share two new datasets towards this objective, (iii) provide new individual ML models for binary and multi-class bot classification and (iv) utilize explainable methods and provide comprehensive visualizations to clearly demonstrate interpretable results. Finally, we utilize all of the above in an effort to improve the so called Bot-Detective online service. Our experiments demonstrate high accuracy, explainability and scalability, comparable with the state of the art, despite multi-class classification challenges.
format	article
author	Ilias Dimitriadis Konstantinos Georgiou Athena Vakali
author_facet	Ilias Dimitriadis Konstantinos Georgiou Athena Vakali
author_sort	Ilias Dimitriadis
title	Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
title_short	Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
title_full	Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
title_fullStr	Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
title_full_unstemmed	Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
title_sort	social botomics: a systematic ensemble ml approach for explainable and multi-class bot detection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d4f88d8e09204d8db15c09aa074c526f
work_keys_str_mv	AT iliasdimitriadis socialbotomicsasystematicensemblemlapproachforexplainableandmulticlassbotdetection AT konstantinosgeorgiou socialbotomicsasystematicensemblemlapproachforexplainableandmulticlassbotdetection AT athenavakali socialbotomicsasystematicensemblemlapproachforexplainableandmulticlassbotdetection
_version_	1718437931074453504

Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection

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