dc.contributor.author | Pitsilis, Georgios | |
dc.contributor.author | Ramampiaro, Heri | |
dc.contributor.author | Langseth, Helge | |
dc.date.accessioned | 2019-04-30T07:20:10Z | |
dc.date.available | 2019-04-30T07:20:10Z | |
dc.date.created | 2018-07-10T10:55:09Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Applied intelligence (Boston). 2018, 1-13. | nb_NO |
dc.identifier.issn | 0924-669X | |
dc.identifier.uri | http://hdl.handle.net/11250/2596029 | |
dc.description.abstract | This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users’ tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. We evaluate our approach on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state-of-the-art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Springer Verlag | nb_NO |
dc.relation.uri | https://rdcu.be/3AaG | |
dc.title | Effective hate-speech detection in Twitter data using recurrent neural networks | nb_NO |
dc.title.alternative | Effective hate-speech detection in Twitter data using recurrent neural networks | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 1-13 | nb_NO |
dc.source.journal | Applied intelligence (Boston) | nb_NO |
dc.identifier.doi | 10.1007/s10489-018-1242-y | |
dc.identifier.cristin | 1596511 | |
dc.relation.project | Næringsliv: 538485 | nb_NO |
dc.description.localcode | This is a post-peer-review, pre-copyedit version of an article published in [Applied intelligence (Boston)] Locked until 26.7.2019 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/s10489-018-1242-y | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |