Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis
Despite a body of research revolving around online abusive language, aiming at different objectives such as detection, diffusion prediction, and mitigation, existing research has seldom looked at factors motivating this behaviour. To further research in this direction, we investigate the motivations...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10160004/ |
| _version_ | 1797790952647032832 |
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| author | Raneem Alharthi Rajwa Alharthi Ravi Shekhar Arkaitz Zubiaga |
| author_facet | Raneem Alharthi Rajwa Alharthi Ravi Shekhar Arkaitz Zubiaga |
| author_sort | Raneem Alharthi |
| collection | DOAJ |
| description | Despite a body of research revolving around online abusive language, aiming at different objectives such as detection, diffusion prediction, and mitigation, existing research has seldom looked at factors motivating this behaviour. To further research in this direction, we investigate the motivations behind online abuse by looking at the characteristics of the targets of such abuse, i.e. is the abuse more prominent for specific characteristics of the targets? To enable target-oriented research into online abuse, we introduce the Online Abusive Attacks (OAA) dataset, the first benchmark dataset providing a holistic view of online abusive attacks, including social media profile data and metadata for both targets and perpetrators, in addition to context. The dataset contains 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations. Further, we conduct an in-depth statistical analysis of online abuse centred around the targets’ characteristics. We identify two types of abusive attacks: those motivated by characteristics of the targets (identity-based attacks) and others (behavioural attacks). We find that online abusive attacks are predominantly motivated by the targets’ identities (97%), behavioural attacks accounting for a much smaller proportion (3%). Abuse is also more likely to target users who are popular and have a verified status. Interestingly, an analysis of the user bios shows no clear indication that keywords used in the bios are likely to trigger abuse. Additionally, we also look at the frequency with which perpetrators perform online abusive attacks. Our analysis shows a large number of infrequent perpetrators, with only a few recurrent perpetrators. Findings from our study have important implications for the development of abusive language detection models that incorporate an awareness of the targets to improve their potential for prediction. |
| first_indexed | 2024-03-13T02:11:51Z |
| format | Article |
| id | doaj.art-3dc717974a1b4d01a5d1e795648fcc68 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T02:11:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3dc717974a1b4d01a5d1e795648fcc682023-06-30T23:01:18ZengIEEEIEEE Access2169-35362023-01-0111641146412710.1109/ACCESS.2023.328914810160004Target-Oriented Investigation of Online Abusive Attacks: A Dataset and AnalysisRaneem Alharthi0https://orcid.org/0000-0001-8749-7352Rajwa Alharthi1Ravi Shekhar2Arkaitz Zubiaga3https://orcid.org/0000-0003-4583-3623College of Computers and Information Technology, Taif University, Ta’if, Saudi ArabiaCollege of Computers and Information Technology, Taif University, Ta’if, Saudi ArabiaSchool of Computer Science and Electronic Engineering, University of Essex, Colchester, U.KSchool of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.KDespite a body of research revolving around online abusive language, aiming at different objectives such as detection, diffusion prediction, and mitigation, existing research has seldom looked at factors motivating this behaviour. To further research in this direction, we investigate the motivations behind online abuse by looking at the characteristics of the targets of such abuse, i.e. is the abuse more prominent for specific characteristics of the targets? To enable target-oriented research into online abuse, we introduce the Online Abusive Attacks (OAA) dataset, the first benchmark dataset providing a holistic view of online abusive attacks, including social media profile data and metadata for both targets and perpetrators, in addition to context. The dataset contains 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations. Further, we conduct an in-depth statistical analysis of online abuse centred around the targets’ characteristics. We identify two types of abusive attacks: those motivated by characteristics of the targets (identity-based attacks) and others (behavioural attacks). We find that online abusive attacks are predominantly motivated by the targets’ identities (97%), behavioural attacks accounting for a much smaller proportion (3%). Abuse is also more likely to target users who are popular and have a verified status. Interestingly, an analysis of the user bios shows no clear indication that keywords used in the bios are likely to trigger abuse. Additionally, we also look at the frequency with which perpetrators perform online abusive attacks. Our analysis shows a large number of infrequent perpetrators, with only a few recurrent perpetrators. Findings from our study have important implications for the development of abusive language detection models that incorporate an awareness of the targets to improve their potential for prediction.https://ieeexplore.ieee.org/document/10160004/Abusive languageonline hatetargets characteristics of online abusesocial network abuseonline abusive attacks dataset |
| spellingShingle | Raneem Alharthi Rajwa Alharthi Ravi Shekhar Arkaitz Zubiaga Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis IEEE Access Abusive language online hate targets characteristics of online abuse social network abuse online abusive attacks dataset |
| title | Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis |
| title_full | Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis |
| title_fullStr | Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis |
| title_full_unstemmed | Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis |
| title_short | Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis |
| title_sort | target oriented investigation of online abusive attacks a dataset and analysis |
| topic | Abusive language online hate targets characteristics of online abuse social network abuse online abusive attacks dataset |
| url | https://ieeexplore.ieee.org/document/10160004/ |
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