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dc.contributor.authorHammer, Hugo Lewi
dc.date.accessioned2017-01-25T10:03:04Z
dc.date.accessioned2017-03-10T10:14:25Z
dc.date.available2017-01-25T10:03:04Z
dc.date.available2017-03-10T10:14:25Z
dc.date.issued2016
dc.identifier.citationHammer HL: Automatic Detection of Hateful Comments in Online Discussion. In: Maglaras. Industrial Networks and Intelligent Systems, 2016. Springer p. 164-173language
dc.identifier.issn1867-8211
dc.identifier.issn1867-822X
dc.identifier.urihttps://hdl.handle.net/10642/4202
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 learn- ing. A material of 24,840 sentences from YouTube was manually anno- tatedasviolentthreatsornot,andwasusedtotrainandtestthemachine 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.rightsOriginal available at www.springerlink.comlanguage
dc.subjectHateful commentslanguage
dc.subjectMachine learninglanguage
dc.subjectThreat detectionlanguage
dc.titleAutomatic Detection of Hateful Comments in Online Discussionlanguage
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2017-01-25T10:03:04Z
dc.description.versionacceptedVersionlanguage
dc.identifier.cristin1437259
dc.source.isbn978-3-319-52568-6


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