tmu-nlp/thai_toxicity_tweet
Thai Toxicity Tweet Corpus contains 3,300 tweets annotated by humans with guidelines including a 44-word dictionary. The author obtained 2,027 and 1,273 toxic and non-toxic tweets, respectively; these were labeled by three annotators. The result of corpus analysis indicates that tweets that include toxic words are not always toxic. Further, it is more likely that a tweet is toxic, if it contains toxic words indicating their original meaning. Moreover, disagreements in annotation are primarily because of sarcasm, unclear existing target, and word sense ambiguity. Notes from data cleaner: The da
| Author / organization | tmu-nlp |
|---|---|
| License (as declared) | cc-by-nc-3.0 |
| Downloads | 179 (as reported by Hugging Face) |
| Likes | 10 |
| Created | 2 March 2022 |
| Last updated at source | 18 January 2024 |
Popularity, last 4 days
179 now (-11 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
More from tmu-nlp
- tmu-nlp/tmu_gfm_datasetDataset
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 19 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.