Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/22054
DC FieldValueLanguage
dc.contributor.authorHerodotou, Herodotos-
dc.contributor.authorChatzakou, Despoina-
dc.contributor.authorKourtellis, Nicolas-
dc.date.accessioned2021-03-01T13:08:52Z-
dc.date.available2021-03-01T13:08:52Z-
dc.date.issued2020-06-17-
dc.identifier.citationarXiv.org, 2020en_US
dc.identifier.urihttps://ktisis.cut.ac.cy/handle/10488/22054-
dc.description.abstractThe rise of online aggression on social media is evolving into a major point of concern. Several machine and deep learning approaches have been proposed recently for detecting various types of aggressive behavior. However, social media are fast paced, generating an increasing amount of content, while aggressive behavior evolves over time. In this work, we introduce the first, practical, real-time framework for detecting aggression on Twitter via embracing the streaming machine learning paradigm. Our method adapts its ML classifiers in an incremental fashion as it receives new annotated examples and is able to achieve the same (or even higher) performance as batch-based ML models, with over 90% accuracy, precision, and recall. At the same time, our experimental analysis on real Twitter data reveals how our framework can easily scale to accommodate the entire Twitter Firehose (of 778 million tweets per day) with only 3 commodity machines. Finally, we show that our framework is general enough to detect other related behaviors such as sarcasm, racism, and sexism in real time.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofarXiv.orgen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectOnline aggression detectionen_US
dc.subjectStreaming machine learningen_US
dc.subjectSocial mediaen_US
dc.titleA Streaming Machine Learning Framework for Online Aggression Detection on Twitteren_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationCenter for Research and Technology-Hellasen_US
dc.collaborationTelefonica Researchen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.countryGreeceen_US
dc.countrySpainen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doiarXiv:2006.10104v2en_US
dc.identifier.urlhttp://arxiv.org/abs/2006.10104v2-
cut.common.academicyear2019-2020en_US
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-8717-1691-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Άρθρα/Articles
Files in This Item:
File Description SizeFormat
2006.10104v2.pdfFulltext5.78 MBAdobe PDFView/Open
CORE Recommender
Show simple item record

Page view(s)

26
Last Week
0
Last month
1
checked on May 6, 2021

Download(s)

34
checked on May 6, 2021

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons