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dc.contributor.authorHammer, Hugo Lewi
dc.date.accessioned2017-10-06T09:49:36Z
dc.date.accessioned2017-10-16T12:24:49Z
dc.date.available2017-10-06T09:49:36Z
dc.date.available2017-10-16T12:24:49Z
dc.date.issued2017
dc.identifier.citationHammer HL. Automatic detection of hateful comments in online discussion. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2017;188:164-173language
dc.identifier.issn1867-8211
dc.identifier.issn1867-822X
dc.identifier.urihttps://hdl.handle.net/10642/5300
dc.description.abstractMaking violent threats towards minorities like immigrants or homosexuals is increasingly common on the Internet. We present a method to automatically detect threats of violence using machine learning. A material of 24,840 sentences from YouTube was manually annotated as violent threats or not, and was used to train and test the machine learning model. Detecting threats of violence works quit well with an error of classifying a violent sentence as not violent of about 10% when the error of classifying a non-violent sentence as violent is adjusted to 5%. The best classification performance is achieved by including features that combine specially chosen important words and the distance between those in the sentence.language
dc.language.isoenlanguage
dc.publisherSpringerlanguage
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-52569-3_15language
dc.subjectHateful commentslanguage
dc.subjectMachine learninglanguage
dc.subjectThreat detectionlanguage
dc.titleAutomatic detection of hateful comments in online discussionlanguage
dc.typeJournal articlelanguage
dc.typePeer reviewedlanguage
dc.date.updated2017-10-06T09:49:36Z
dc.description.versionacceptedVersionlanguage
dc.identifier.doihttp://doi.org/10.1007/978-3-319-52569-3_15
dc.identifier.cristin1481785
dc.source.journalLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering


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