Participants will be provided with sentences in comment, extracted from social. Given a comments, a system must predict whether or not it contains any form of homophobia/transphobia. The seed data for this task is the Homophobia/Transphobia Detection dataset [1], a collection of comments from social media. The comments are manually annotated to show whether the text contains homophobia/transphobia.
The participants will be provided development, training and test dataset in English, Spanish, Hindi, Tamil, and Malayalam. To download the data and participate, go to codalab and click “Participate" tab. As far as we know, this is the first shared task on Homophobia/Transphobia Detection.
Task:
This is a comment / post level classification task. Given a Youtube comment, the systems submitted by the participants should classify it'. To download the data and participate, go to the Participate tab.
As far as we know, this is the first shared task on Homophobia/Transphobia detection.
Paper name format should be: TEAM_NAME@LT-EDI@RANLP-2023: Title of the paper.
Example: NUIG_ULD@LT-EDI@RANLP-2023: Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion
For electronic submission of papers to LT-EDI@RANLP workshop please use this link:
@article{chakravarthi2022can, title={How can we detect Homophobia and Transphobia? Experiments in a multilingual code-mixed setting for social media governance}, author={Chakravarthi, Bharathi Raja and Hande, Adeep and Ponnusamy, Rahul and Kumaresan, Prasanna Kumar and Priyadharshini, Ruba}, journal={International Journal of Information Management Data Insights}, volume={2}, number={2}, pages={100119}, year={2022}, publisher={Elsevier} }
Classification system’s performance will be measured in terms of weighted averaged Precision, weighted averaged Recall, and weighted averaged F-Score across all the classes. Participants are encouraged to check their system with Sklearn classification report https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html
By downloading the data or by accessing it any manner, you agree not to redistribute the data except for non-commercial and academic-research purposes. The data must not be used for providing surveillance, analyses or research that isolates a group of individuals or any single individual for any unlawful or discriminatory purpose.
You should cite this papers if you are using our data.
@article{chakravarthi2022can, title={How can we detect Homophobia and Transphobia? Experiments in a multilingual code-mixed setting for social media governance}, author={Chakravarthi, Bharathi Raja and Hande, Adeep and Ponnusamy, Rahul and Kumaresan, Prasanna Kumar and Priyadharshini, Ruba}, journal={International Journal of Information Management Data Insights}, volume={2}, number={2}, pages={100119}, year={2022}, publisher={Elsevier} }
Important Dates for shared task:
Task announcement: Feb 20, 2023
Release of Training data: Feb 28, 2023
Release of Test data: May 10, 2023
Run submission deadline: June 1, 2023 Extended to June 7,2023
Results declared: June 10, 2023
Paper submission:1 July 2023 10 July 2023
Peer review notification: 5 August 2023
Camera-ready paper due: 20 August 2023
Workshop Dates: 7-8 September 2023
Bharathi Raja Chakravarthi, Insight SFI Research Centre for Data Analytics, School of Computer Science, University of Galway, Ireland
Malliga Subramanian, Kongu Engineering College, Tamil Nadu, India
Rahul Ponnusamy, Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway, Ireland
Paul Buitelaar, Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway, Ireland
Miguel Ángel García-Cumbreras, Universidad de Jaén, Spain
Salud María Jiménez-Zafra, Universidad de Jaén, Spain
José Antonio García-Díaz, Universidad de Murcia, Spain
Rafael Valencia-García, Universidad de Murcia, Spain
Student Volunteer:
Email: bharathiraja.akr@gmail.com
Start: Feb. 28, 2023, midnight
Aug. 31, 2023, 8:07 a.m.
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