An abusive text detection system based on enhanced abusive and non-abusive word lists.
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| Title: | An abusive text detection system based on enhanced abusive and non-abusive word lists. |
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| Authors: | Lee, Ho-Suk1 hosuklee@yonsei.ac.kr, Lee, Hong-Rae1 hongraelee@yonsei.ac.kr, Park, Jun-U1 junupark@yonsei.ac.kr, Han, Yo-Sub1 emmous@yonsei.ac.kr |
| Source: | Decision Support Systems. Sep2018, Vol. 113, p22-31. 10p. |
| Subjects: | Swearing (Profanity), Prevention of cyberbullying, Web-based user interfaces, Social networks, Video games |
| Abstract: | Abusive text (indiscriminate slang, abusive language, and profanity) on the Internet is not just a message but rather a tool for very serious and brutal cyber violence. It has become an important problem to devise a method for detecting and preventing abusive text online. However, the intentional obfuscation of words and phrases makes this task very difficult and challenging. We design a decision system that successfully detects (obfuscated) abusive text using an unsupervised learning of abusive words based on word2vec's skip-gram and the cosine similarity. The system also deploys several efficient gadgets for filtering abusive text such as blacklists, n-grams, edit-distance metrics, mixed languages, abbreviations, punctuation, and words with special characters to detect the intentional obfuscation of abusive words. We integrate both an unsupervised learning method and efficient gadgets into a single system that enhances abusive and non-abusive word lists. The integrated decision system based on the enhanced word lists shows a precision of 94.08%, a recall of 80.79%, and an f-score of 86.93% in malicious word detection for news article comments, a precision of 89.97%, a recall of 80.55%, and an f-score 85.00% for online community comments, and a precision of 90.65%, a recall of 93.57%, and an f-score 92.09% for Twitter tweets. We expect that our approach can help to improve the current abusive word detection system, which is crucial for several web-based services including social networking services and online games. [ABSTRACT FROM AUTHOR] |
| Copyright of Decision Support Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 131199575 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An abusive text detection system based on enhanced abusive and non-abusive word lists. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lee%2C+Ho-Suk%22">Lee, Ho-Suk</searchLink><relatesTo>1</relatesTo><i> hosuklee@yonsei.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Hong-Rae%22">Lee, Hong-Rae</searchLink><relatesTo>1</relatesTo><i> hongraelee@yonsei.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Park%2C+Jun-U%22">Park, Jun-U</searchLink><relatesTo>1</relatesTo><i> junupark@yonsei.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Han%2C+Yo-Sub%22">Han, Yo-Sub</searchLink><relatesTo>1</relatesTo><i> emmous@yonsei.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Decision+Support+Systems%22">Decision Support Systems</searchLink>. Sep2018, Vol. 113, p22-31. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Swearing+%28Profanity%29%22">Swearing (Profanity)</searchLink><br /><searchLink fieldCode="DE" term="%22Prevention+of+cyberbullying%22">Prevention of cyberbullying</searchLink><br /><searchLink fieldCode="DE" term="%22Web-based+user+interfaces%22">Web-based user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Social+networks%22">Social networks</searchLink><br /><searchLink fieldCode="DE" term="%22Video+games%22">Video games</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abusive text (indiscriminate slang, abusive language, and profanity) on the Internet is not just a message but rather a tool for very serious and brutal cyber violence. It has become an important problem to devise a method for detecting and preventing abusive text online. However, the intentional obfuscation of words and phrases makes this task very difficult and challenging. We design a decision system that successfully detects (obfuscated) abusive text using an unsupervised learning of abusive words based on word2vec's skip-gram and the cosine similarity. The system also deploys several efficient gadgets for filtering abusive text such as blacklists, n-grams, edit-distance metrics, mixed languages, abbreviations, punctuation, and words with special characters to detect the intentional obfuscation of abusive words. We integrate both an unsupervised learning method and efficient gadgets into a single system that enhances abusive and non-abusive word lists. The integrated decision system based on the enhanced word lists shows a precision of 94.08%, a recall of 80.79%, and an f-score of 86.93% in malicious word detection for news article comments, a precision of 89.97%, a recall of 80.55%, and an f-score 85.00% for online community comments, and a precision of 90.65%, a recall of 93.57%, and an f-score 92.09% for Twitter tweets. We expect that our approach can help to improve the current abusive word detection system, which is crucial for several web-based services including social networking services and online games. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Decision Support Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.dss.2018.06.009 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 22 Subjects: – SubjectFull: Swearing (Profanity) Type: general – SubjectFull: Prevention of cyberbullying Type: general – SubjectFull: Web-based user interfaces Type: general – SubjectFull: Social networks Type: general – SubjectFull: Video games Type: general Titles: – TitleFull: An abusive text detection system based on enhanced abusive and non-abusive word lists. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee, Ho-Suk – PersonEntity: Name: NameFull: Lee, Hong-Rae – PersonEntity: Name: NameFull: Park, Jun-U – PersonEntity: Name: NameFull: Han, Yo-Sub IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 01679236 Numbering: – Type: volume Value: 113 Titles: – TitleFull: Decision Support Systems Type: main |
| ResultId | 1 |
