Datasets:
Update README.md
Browse files
README.md
CHANGED
@@ -65,7 +65,7 @@ size_categories:
|
|
65 |
---
|
66 |
|
67 |
# The Canceling Attitudes Detection (CADE) Dataset
|
68 |
-
CADE is a dataset created in the context of the research
|
69 |
|
70 |
|
71 |
Canceling is a morally-driven phenomenon that hinders the development of safe social media platforms and contributes to ideological polarization. To address this issue we present the Canceling Attitudes Detection (CADE) dataset, an annotated corpus of canceling incidents aimed at exploring the factors of disagreements in evaluating people canceling attitudes on social media. Specifically, we study the impact of annotators' morality in their perception of canceling, showing that morality is an independent axis for the explanation of disagreement on this phenomenon. Annotator's judgments heavily depend on the type of controversial events and involved celebrities. This shows the need to develop more event-centric datasets to better understand how harms are perpetrated in social media and to develop more aware technologies for their detection.
|
|
|
65 |
---
|
66 |
|
67 |
# The Canceling Attitudes Detection (CADE) Dataset
|
68 |
+
CADE is a dataset created in the context of the research [**That is Unacceptable: the Moral Foundations of Canceling**](https://arxiv.org/pdf/2503.05720). Here you can find the abstract.
|
69 |
|
70 |
|
71 |
Canceling is a morally-driven phenomenon that hinders the development of safe social media platforms and contributes to ideological polarization. To address this issue we present the Canceling Attitudes Detection (CADE) dataset, an annotated corpus of canceling incidents aimed at exploring the factors of disagreements in evaluating people canceling attitudes on social media. Specifically, we study the impact of annotators' morality in their perception of canceling, showing that morality is an independent axis for the explanation of disagreement on this phenomenon. Annotator's judgments heavily depend on the type of controversial events and involved celebrities. This shows the need to develop more event-centric datasets to better understand how harms are perpetrated in social media and to develop more aware technologies for their detection.
|