Competition

Fake News Detection in Dravidian Languages- DravidianLangTech@EACL 2024

Organized by DravidianLangTech - Current server time: Nov. 6, 2025, 3:34 p.m. UTC

First phase

First phase
Sept. 1, 2023, midnight UTC

End

Competition Ends
May 31, 2024, 11 p.m. UTC

Fake News Detection in Dravidian Languages-DravidianLangTech@EACL 2024

The proliferation of online social media over the past few years has significantly streamlined the ways in which individuals are able to communicate with one another. Users of social media platforms are able to exchange information, communicate and maintain awareness of current events. On the other hand, much of the recent information that has been emerging on social media is false and, in some instances, is intended to mislead users. This type of content is commonly referred to as fake news. Any false or misleading information that appears as original news is considered as fake news. 

 

There are two subtasks:

Task 1: The goal of this task is to classify a given social media text into original or fake. The sources of data are various social-media platforms such as Twitter, Facebook etc. Given a social media post, the objective of the shared task is to classify it into either fake or original news. For example, the following two posts belong to fake and original categories, respectively. 

Task: This is a comment / post level classification task. Given a Youtube comment, the systems submitted by the participants should classify it into original or fake news. To download the data and participate, go to the Participate tab

Task 2: The Fake News Detection from Malayalam News (FakeDetect-Malayalam) shared task provides a platform for researchers to address the pressing challenge of identifying and flagging fake news within the realm of Malayalam-language news articles. In an age of information overload, accurate detection of misinformation is crucial for fostering trustworthy communication. The core objective of the FakeDetect-Malayalam shared task is to encourage participants to develop effective models capable of accurately detecting and classifying fake news articles in the Malayalam language into different categories. Here, we considered five fake categories - False, Half True, Mostly False, Partly False and Mostly True

Task: This is a comment / post level classification task. Given a comment/news, the systems submitted by the participants should classify it into the five classes mentioned above. To download the data and participate, go to the Participate tab 

The participants will be provided training and test dataset in Malayalam. To download the data and participate, go to codalab and click “Participate" tab. 

 

Paper  name format should be: TEAM_NAME@DravidianLangTech 2024: Title of the paper.

Example: Uog_NLP@DravidianLangTech 2024: Fake News Detection in Dravidian Languages

For electronic submission of papers to DravidianLangTech workshop please use this link: Will be update sson...

Following are some general guidelines to keep in mind while submitting the working notes.

  • Basic sanity check for grammatical errors and reported results
  • Papers should have sufficient information for reproducing the mentioned results- Papers should follow the appropriate style (We will use EACL 2024 style: details below)
  • Check the papers for text reuse / Plagiarism. This includes self-plagiarism as well. We would like to stress this point as EACL is quite strict about it. Any paper found to have plagiarized content should be rejected without further consideration.
  • Please ensure the author names do not have any salutations like Dr., Prof., etc in the final version

 

All submissions should be in Double column EACL 2024 format. Authors should use one of the EACL 2024 Templates below:

- Overleaf:  https://www.overleaf.com/latex/templates/acl-2023-proceedings-template/qjdgcrdwcnwp

Submission: https://openreview.net/group?id=eacl.org/EACL/2024/Workshop/DravidianLangTech

Email: mallinishanth72@gmail.com, bharathiraja.akr@gmail.com, & b_premjith@cb.amrita.edu

 

 

Evaluation Criteria:

Submission should be a zip file with your team name containing CSV for Task 1 & TSV for Task 2 files for each run (Maximum 3 submissions) -

teamname.zip
- teamname_taskname_run.tsv'
- teamname_taskname_run.csv'
e.g. CEN.zip
-- CEN_task1_run1.csv
-- CEN_task2_run1.tsv

  • The submission will be evaluated with a macro average F1-score.

We accept the test results only through the Google form. The results should be submitted on the Google form: Closed

 

The classification system’s performance will be measured in terms of macro-averaged Precision, macro-averaged Recall, and macro-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

 

Thank you for participating in our shared task, we have attached the RANK LIST below: 

 

Click TASK 1 to see the Task 1 rank list

Click TASK 2 to see the Task 2 rank list

 

If you are using our dataset, please cite the below papers

@inproceedings{fakenews-2023-overview,

    title = "Overview of the Shared Task on Fake News Detection from Social Media Text",

    author = "Subramanian, Malliga and 

      Chakravarthi, Bharathi Raja and

      Shanmugavadivel, Kogilavani and

      Pandiyan, Santhiya and

      Kumaresan, Prasanna Kumar and

      Palani, Balasubramanian and

      Singh, Muskaan and

      Raja, Sandhiya and

      Vanaja and

      S, Mithunajha",      

    booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",

    month = September,

    year = "2023",

    address = "Varna, Bulgaria ",

    publisher = "Recent Advances in Natural Language Processing",

}

 

Terms and Conditions

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.

@inproceedings{fakenews-2023-overview,

    title = "Overview of the Shared Task on Fake News Detection from Social Media Text",

    author = "Subramanian, Malliga and 

      Chakravarthi, Bharathi Raja and

      Shanmugavadivel, Kogilavani and

      Pandiyan, Santhiya and

      Kumaresan, Prasanna Kumar and

      Palani, Balasubramanian and

      Singh, Muskaan and

      Raja, Sandhiya and

      Vanaja and

      S, Mithunajha",      

    booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",

    month = September,

    year = "2023",

    address = "Varna, Bulgaria ",

    publisher = "Recent Advances in Natural Language Processing",

}

Important Dates for shared tasks:

  • Task announcement: October 15, 2023
  • Release of Training data: October 20, 2023
  • Release of Test data: November 15, 2023
  • Run submission deadline: November 25, 2023
  • Results declared: December 1, 2023
  • Paper submission: December 18, 2023
  • Peer review notification: January 20, 2024
  • Camera-ready paper due: January 30, 2024
  • Workshop Dates: March 21-22, 2024

 

Malliga Subramanian, Kongu Engineering College, Tamil Nadu, India

Bharathi Raja Chakravarthi, School of Computer Science, University of Galway, Ireland

Kogilavani Shanmugavadivel, Kongu Engineering College, Tamil Nadu, India

Santhia Pandiyan, Kongu Engineering College, Tamil Nadu, India

Prasanna Kumar Kumaresan, School of Computer Science, University of Galway, Ireland

Balasubramanian Palani, National Institute of Technology, Tamil Nadu, India

Premjith B, Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Amrita Vishwa Vidyapeetham, India

 

Student Volunteer:

Sandhiya Raja, Kongu Engineering College, Tamil Nadu, India

Vanaja, Kongu Engineering College, Tamil Nadu, India

Mithunajha S, Kongu Engineering College, Tamil Nadu, India

Devika K, Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Amrita Vishwa Vidyapeetham, India

Hariprasath S.B, Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Amrita Vishwa Vidyapeetham, India

Haripriya B, Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Amrita Vishwa Vidyapeetham, India

Vigneshwar E, Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Amrita Vishwa Vidyapeetham, India

 

Email: mallinishanth72@gmail.com, bharathiraja.akr@gmail.com, & b_premjith@cb.amrita.edu

First phase

Start: Sept. 1, 2023, midnight

Competition Ends

May 31, 2024, 11 p.m.

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