From 3722f3be96f84664d4dc440106fabbec01cd42fe Mon Sep 17 00:00:00 2001
From: RaneemAlharthi <137334063+RaneemAlharthi@users.noreply.github.com>
Date: Thu, 11 Apr 2024 16:12:51 +0300
Subject: [PATCH] Update README.md (#99)

**Online-Abusive-Attacks-OAA-Dataset Description:**
The Online Abusive Attacks (OAA) dataset, the first benchmark dataset providing a holistic view of online
abusive attacks, including social media profile data and metadata for both targets and perpetrators, in addition
to context. The dataset contains 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations.
---
 README.md | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/README.md b/README.md
index be54c20..d25fd07 100644
--- a/README.md
+++ b/README.md
@@ -318,6 +318,27 @@ Please send contributions via github pull request. You can do this by visiting t
 * Platform: Twitter
 * Medium: Text and image
 * Reference: Cagri Toraman, Furkan Şahinuç, Eyup Yilmaz. 2022. Large-Scale Hate Speech Detection with Cross-Domain Transfer. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2215–2225, Marseille, France. European Language Resources Association.
+* 
+  #### Online-Abusive-Attacks-OAA-Dataset
+* Link to publication: [https://ieeexplore.ieee.org/abstract/document/10160004](https://ieeexplore.ieee.org/abstract/document/10160004)
+* Link to data: [https://github.com/RaneemAlharthi/Online-Abusive-Attacks-OAA-Dataset](https://github.com/RaneemAlharthi/Online-Abusive-Attacks-OAA-Dataset)
+* Task description: "Binary (abusive, Notabusive)", "Hierarchical", "six-class (toxicity, severe toxicity, identity attack,insult, profanity, and threat)"
+* Details of task: "the first benchmark dataset providing a holistic view of online
+abusive attacks, including social media profile data and metadata for both targets and perpetrators, in addition
+to context. The dataset contains 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations."
+* Size of dataset: 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations.
+* Percentage abusive:  online abusive attacks motivated
+by the targets’ identities (97%), and motivated
+by the targets’ behavioural attacks (3%).
+* Language: e.g. English
+* Level of annotation: What is an "instance", in this dataset? e.g.  Conversation 
+* Platform: e.g. twitter
+* Medium: text /metadata
+* Reference: @article{alharthi2023target,
+title={Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis},
+author={Alharthi, Raneem and Alharthi, Rajwa and Shekhar, Ravi and Zubiaga, Arkaitz},
+journal={IEEE Access}, year={2023}, publisher={IEEE}
+}
 
 #### ConvAbuse
 * Link to publication: [https://aclanthology.org/2021.emnlp-main.587/](https://aclanthology.org/2021.emnlp-main.587/)