Uncovering Annotator Dynamics in Hate Speech Detection: A Multi-Model Investigation
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Poesio, Massimo | |
| dc.contributor.author | Mikulin, Marco | |
| dc.date.accessioned | 2024-10-10T00:01:55Z | |
| dc.date.available | 2024-10-10T00:01:55Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/47938 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | investigation of multi-model classification algorithm for hate speech detection. Addressing annotator disagreement by training a different model for each annotator in the dataset. Due to the huge amount of resource requirements for the task clusterization methods have been applied to cluster the annotators. Clusters of annotators are then used to train the model considering one cluster as one annotator. comparing the results for the different models trained | |
| dc.title | Uncovering Annotator Dynamics in Hate Speech Detection: A Multi-Model Investigation | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Artificial Intelligence | |
| dc.thesis.id | 31594 |
