The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets
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arXiv
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| Format: | Preprint |
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2024
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| author | Yu, Zehui Sen, Indira Assenmacher, Dennis Samory, Mattia Fröhling, Leon Dahn, Christina Nozza, Debora Wagner, Claudia |
| author_facet | Yu, Zehui Sen, Indira Assenmacher, Dennis Samory, Mattia Fröhling, Leon Dahn, Christina Nozza, Debora Wagner, Claudia |
| contents | Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evidence that they underperform in detecting hateful communications directed towards specific identities and may discriminate against them, we know surprisingly little about the provenance of such bias. To fill this gap, we present a systematic review of the datasets for the automated detection of hateful communication introduced over the past decade, and unpack the quality of the datasets in terms of the identities that they embody: those of the targets of hateful communication that the data curators focused on, as well as those unintentionally included in the datasets. We find, overall, a skewed representation of selected target identities and mismatches between the targets that research conceptualizes and ultimately includes in datasets. Yet, by contextualizing these findings in the language and location of origin of the datasets, we highlight a positive trend towards the broadening and diversification of this research space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08562 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets Yu, Zehui Sen, Indira Assenmacher, Dennis Samory, Mattia Fröhling, Leon Dahn, Christina Nozza, Debora Wagner, Claudia Computation and Language Computers and Society Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evidence that they underperform in detecting hateful communications directed towards specific identities and may discriminate against them, we know surprisingly little about the provenance of such bias. To fill this gap, we present a systematic review of the datasets for the automated detection of hateful communication introduced over the past decade, and unpack the quality of the datasets in terms of the identities that they embody: those of the targets of hateful communication that the data curators focused on, as well as those unintentionally included in the datasets. We find, overall, a skewed representation of selected target identities and mismatches between the targets that research conceptualizes and ultimately includes in datasets. Yet, by contextualizing these findings in the language and location of origin of the datasets, we highlight a positive trend towards the broadening and diversification of this research space. |
| title | The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2405.08562 |