Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/22982
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Herodotou, Herodotos | - |
dc.contributor.author | Chatzakou, Despoina | - |
dc.contributor.author | Kourtellis, Nicolas | - |
dc.date.accessioned | 2021-09-07T07:29:42Z | - |
dc.date.available | 2021-09-07T07:29:42Z | - |
dc.date.issued | 2021-04 | - |
dc.identifier.isbn | 9781728191843 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/22982 | - |
dc.description.abstract | Aggression on social media has evolved into a major point of concern. However, recently proposed machine learning (ML) approaches to detect various types of aggressive behavior fall short, due to the fast and increasing pace of content generation as well as evolution of such behavior over time. This work introduces the first, practical, real-time framework for detecting aggression on Twitter via embracing the streaming ML paradigm. This method adapts its ML binary classifiers in an incremental fashion, while receiving new annotated examples, and achieves similar performance as batch-based ML models, with 82-93% accuracy, precision, and recall. Experimental analysis on real Twitter data reveals how this framework, implemented in Spark Streaming, easily scales to process millions of tweets in minutes. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.rights | © IEEE | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Online aggression detection | en_US |
dc.subject | Streaming machine learning | en_US |
dc.subject | Social media | en_US |
dc.title | Catching them red-handed: Real-time aggression detection on social media | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Centre for Research and Technology Hellas (CERTH) | en_US |
dc.collaboration | Telefonica Research | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.country | Cyprus | en_US |
dc.country | Greece | en_US |
dc.country | Spain | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.relation.conference | IEEE International Conference on Data Engineering | en_US |
dc.identifier.doi | 10.1109/ICDE51399.2021.00211 | en_US |
dc.identifier.scopus | 2-s2.0-85110895335 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85110895335 | - |
dc.relation.volume | 37th IEEE International Conference on Data Engineering, 2021, 19-22 April, Virtual, Chania | en_US |
cut.common.academicyear | 2020-2021 | en_US |
item.openairetype | conferenceObject | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-8717-1691 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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