Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection

Core Concepts

The importance of detecting fake news in Urdu is addressed through the creation of a benchmark dataset, "Ax-to-Grind Urdu," to bridge existing gaps and limitations.

Abstract

Abstract:

Misinformation's impact on society.
Lack of regional language fact-checking portals.
Introduction of "Ax-to-Grind Urdu" dataset.
Introduction:

Significance of Fake News Detection (FND).
Examples of FN impact globally.
Importance of FND in the digital era.
Data Extraction:

"The dataset contains news items in Urdu from the year 2017 to the year 2023."
"F1-score of 0.924, accuracy of 0.956, precision of 0.942, recall of 0.940 and an MCC value of 0.902."
Related Work:

Overview of previous datasets and techniques used for Urdu FND.
Performance metrics comparison with existing models.
Ax-to-Grind Dataset:
Dataset Collection and Annotation:

Collection sources for true and fake news.
Removal of meaningless words and symbols from raw data.
Corpus Statistics:

Unique words: 29,911.
Average words per news item: True - 34.82, Fake - 116.98, Combined - 75.90.
Dataset Pre-processing:

Techniques used for cleaning data before model input.
Methodology for Baseline Transformer:
Lexical Feature Extraction:

Explanation of TF-IDF technique for feature extraction.
NLP Pre-trained Transformer-based Models:

Description and selection criteria for mBERT, XLNet, XLM-RoBERTa models.
Ensembling the Pre-trained Models:

Stacking method used to enhance model performance.
Experimental Evaluation:
Performance Evaluation:

Results comparison with ML and DL models.
McNemar’s Test:

Statistical significance evaluation using McNemar's test.
Conclusion:
Summary highlighting dataset creation, model performance, and statistical significance validation.

Stats

"The dataset contains news items in Urdu from the year 2017 to the year 2023."
"F1-score of 0.924, accuracy of 0.956, precision of 0.942, recall of 0.940 and an MCC value of 0.902."

Quotes

"No manual validation was performed with a limited scope."
"The proposed ensemble model shows an F1-score of 0.924."

Key Insights Distilled From

Ax-to-Grind Urdu

by Sheetal Harr... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14037.pdf

Deeper Inquiries

How can the dataset be expanded to include more diverse domains?

To expand the dataset to include more diverse domains, researchers can consider incorporating news items from a wider range of sources beyond newspapers and news channels. They could explore including content from social media platforms, blogs, forums, and other online sources where fake news is prevalent. Additionally, they could collaborate with experts in various fields to identify specific domains that are susceptible to fake news dissemination. This collaboration would ensure that the dataset covers a comprehensive array of topics such as health, technology, entertainment, politics, environment, and more. By diversifying the sources and types of content included in the dataset, researchers can create a robust resource for training models that can effectively detect fake news across multiple domains.

What ethical considerations should be taken into account when detecting fake news?

When detecting fake news ethically, it is crucial to prioritize accuracy and transparency in the process. Researchers must ensure that their methods for identifying misinformation are unbiased and based on factual evidence rather than personal beliefs or agendas. It is essential to protect individuals' privacy rights by anonymizing data used in training models and ensuring that sensitive information is not compromised during analysis.
Moreover, researchers should consider potential consequences of labeling content as fake news; misclassification could harm individuals or organizations unjustly. Therefore, validation processes should involve human oversight to minimize errors in classification.
Additionally,
transparency about how algorithms work
and what criteria are used for determining authenticity.
Researchers must also consider potential biases present within datasets or algorithms used for detection.
Ensuring fairness by addressing any inherent biases within the model.
Lastly,
maintaining open communication with stakeholders regarding limitations and uncertainties associated with automated detection systems.

How can this research impact other languages facing similar challenges in fake news detection?

This research serves as a blueprint for developing effective strategies for combating misinformation in languages beyond Urdu. The methodology employed—curating a large-scale annotated dataset from authentic sources—can be replicated across different languages facing similar challenges with limited resources.
By sharing insights gained from this study on best practices for creating benchmark datasets tailored to regional languages' needs,
researchers working on other language-specific FND projects may benefit greatly.
The ensemble approach using pre-trained transformer-based models offers an efficient solution applicable across various linguistic contexts.
Furthermore,
the performance metrics established through this research provide benchmarks against which future studies tackling FND issues in different languages can measure their effectiveness.
Overall,
this research sets a precedent for collaborative efforts among linguists,
data scientists,
and domain experts globally
to address the pervasive issue of fake news propagation regardless of language barriers or resource constraints.