Datasets:
Tasks:
Text Classification
Formats:
parquet
Sub-tasks:
multi-label-classification
Languages:
English
Size:
1K - 10K
License:
first draft of model card
Browse files
README.md
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- noncooperation
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- pejorative
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- sarcasm
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task_categories:
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- text-classification
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task_ids:
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- multi-label-classification
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---
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- noncooperation
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- pejorative
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- sarcasm
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- vulgarity
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task_categories:
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- text-classification
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task_ids:
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- multi-label-classification
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---
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# Dataset Card for incivility-arizona-daily-star-comments
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This is a collection of more than 6000 comments on Arizona Daily Star news articles from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.
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## Dataset Structure
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Each instance in the dataset corresponds to a single comment from a single commenter.
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An instance's `text` field contains the text of the comment with any quotes of other commenters removed.
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The remaining fields in each instance provide binary labels for each type of incivility annotated:
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`aspersion`, `hyperbole`, `lying`, `namecalling`, `noncooperation`, `offtopic`, `pejorative`, `sarcasm`, `vulgarity`, and `other_incivility`.
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The dataset provides three standard splits: `train`, `validation`, and `test`.
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## Dataset Creation
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The original annotation effort is described in:
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- Kevin Coe, Kate Kenski, Stephen A. Rains.
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[Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments](https://doi.org/10.1111/jcom.12104).
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Journal of Communication, Volume 64, Issue 4, August 2014, Pages 658–679.
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That dataset was converted to a computer-friendly form as described in section 4.2.1 of:
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- Farig Sadeque.
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[User behavior in social media: engagement, incivility, and depression](https://repository.arizona.edu/handle/10150/633192).
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PhD thesis. The University of Arizona. 2019.
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The current upload is a 2023 conversion of that form to a huggingface Dataset.
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## Considerations for Using the Data
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The data is intended for the study of incivility.
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It should not be used to train models to generate incivility.
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The human coders and their trainers were mostly [Western, educated, industrialized, rich and democratic (WEIRD)](https://www.nature.com/articles/466029a), which may have shaped how they evaluated incivility.
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## Citation
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```bibtex
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@article{10.1111/jcom.12104,
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author = {Coe, Kevin and Kenski, Kate and Rains, Stephen A.},
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title = {Online and Uncivil? Patterns and Determinants of Incivility in Newspaper Website Comments},
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journal = {Journal of Communication},
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volume = {64},
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number = {4},
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pages = {658-679},
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year = {2014},
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month = {06},
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issn = {0021-9916},
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doi = {10.1111/jcom.12104},
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url = {https://doi.org/10.1111/jcom.12104},
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}
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```
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