UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu

Amjad, Maaz; Butt, Sabur; Amjad, Hamza Imam; Zhila, Alisa; Sidorov, Grigori; Gelbukh, Alexander

doi:10.1145/3503162.3505240

Cited by 6 publications

(11 citation statements)

References 5 publications

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“…Further, the analysis concludes that the machinetranslated corpus' quality is sufficient for fake news detection task for Urdu text if the corpus is preprocessed correctly, and the stopwords of the language are removed from the text. Our findings negate the findings of (Amjad et al 2020a) about the low-quality machine-translated corpus and the Google API. Further, we conclude that preprocessing and stopword removal enhances the classification performance significantly for Urdu fake news detection.…”

Section: Resultscontrasting

confidence: 90%

“…When resources are limited, manually annotating a corpus for a language is a laborious, timeconsuming, and expert-level undertaking, according to the findings of research that was carried out by (Amjad et al 2020b). It is mentioned by (Amjad et al 2020a) that researchers have been making use of enhanced corpora, which mostly consist of machine translations from English into other languages. The limited number of corpora that are now available is the reason behind this.…”

Section: Related Workmentioning

confidence: 99%

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Monitoring Social Networking Platforms to Detect and Filter Fake News using Ensemble Learning

Zaheer,

Talib,

Hanif

et al. 2024

Preprint

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Social networking platforms and news blogs are providing information to the public. Different business, political, and educational communities rely on these news sources for strategic decision-making. It is straightforward to quickly manipulate and spread real digital news to spread misinformation among communities to get a few benefits or relief. Therefore, an automated system is vital that can detect fake news early during monitoring before it is published online. Several studies have been conducted to detect fake news, focusing on resource-rich languages (mostly English). Because of a lack of annotated corpora, resource-poor languages such as Urdu have not been studied. The objective of this study is to provide an effective method for fake news detection from social media platforms in Urdu. Therefore, in this study, we propose a four-level methodology and perform extensive experiments to find out the best model for fake news detection from social media contents in Urdu. This study proposes a public corpus of Urdu news articles and a methodology for detecting early Urdu fake news. We apply eight machine learning and ensemble learning techniques to three Urdu news corpora. Our experiments show that Bagging with Decision Tree as base learner outperforms the others and obtained F-measure scores of 80.9% on UFN, 84.2% on BET, and 86.02% on FNAC.

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Section: Resultscontrasting

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Monitoring Social Networking Platforms to Detect and Filter Fake News using Ensemble Learning

Zaheer,

Talib,

Hanif

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It is expensive, time-consuming, and requires specialized knowledge to manually create an annotated corpus for a language with limited resources [7]. Because there are not many datasets available, researchers have been employing augmented datasets that have been primarily machine translated from English into other languages [1], [34].…”

Section: Related Workmentioning

confidence: 99%

“…Information purposely prepared and distributed to convey a misleading impression about a person, place, or thing is considered deceptive. A piece of news that is blatantly and demonstrably false is regarded as fake news [1]. One of the most damaging types of deception is fake news, in which the source of the story is unclear, and someone has changed the content of the official press [2].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Kernel Optimized Convolutional Neural Network With Urdu Word Embedding to Detect Fake News

Zaheer,

Talib,

Hanif

et al. 2023

IEEE Access

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One of the biggest threats to international trade, journalism, and democracy is fake news on social media, which also causes substantial collateral damage. The manipulation of digital news to spread misinformation has become a common practice for personal benefits or relief. Therefore, developing an automated system that can detect fake news before publication is crucial. This study proposes a three-level methodology with a new model called Multi-Kernel Optimized Convolutional Neural Network (MOCNN) to investigate its effectiveness for fake news detection. The parameters of the proposed model have been optimized using the grid search technique. We evaluated ten different deep learning models on two benchmark datasets of fake news articles and compared their performance with the proposed model. Finding the best model with good accuracy performance is the primary goal of this paper. F-measure and accuracy are used to evaluate and compare the classification performance of these deep learning models. Our proposed model achieves 85.8% and 68.2% accuracy and 85.8% and 67.7% F1-measure on UFN and BET, respectively. Experimental results confirm that the proposed model performs better than other models on UFN and BET datasets.

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“…Nevertheless, the meaning of this phrase (i.e., "fake news"), in addition to its interpretation and conceptualization, has recently been the subject of discussion [10]. As a result, articulating the definition that runs throughout the study is of the utmost importance.…”

Section: Fake News Definitionmentioning

confidence: 99%

Fake News Detection in Urdu using Deep Learning

Rauf,

Irfan,

Mushtaq

et al. 2022

VFAST trans. softw. eng.

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An increase in the volume of false information circulating as a direct consequence of the rise in the growth of social media has an effect of misguiding the general population. Therefore, a mechanism for identifying fake news is required to prevent such repercussions. Almost all of these already existing algorithms for detecting fake news work with resource-rich languages such as Spanish and English; however, few techniques can work for resource-constrained languages such as Urdu. The study aims to identify instances of fake news written in Urdu by applying deep learning and machine learning methodologies. We use the MuRIL and T5 models for the implementation process because these models were developed specifically for Urdu and Hindi language recognition. The newspaper articles included in the valid subset derived from credible news sources, and the accuracy of these items have been checked by hand. Inside the misinformation subsection, the problem of how challenging it was to discover fake news was overcome by employing experienced reporters who were native Urdu speakers and instructing them to compose deceptive news items purposefully. It has allowed the researchers to overcome the problem. The dataset covers various subjects, including business, sports, health, showbiz, and technologies. We have carried out baseline classification to use our Urdu database as a standard for other datasets. The experiments with the various systems have indicated that the MuRIL model significantly improves over the other models, such as the T5 Model, and attains an average F1 score of 0.96 and a validation accuracy of 0.83. These results have been determined based on the findings of the experiments.

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UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu

Cited by 6 publications

References 5 publications

Monitoring Social Networking Platforms to Detect and Filter Fake News using Ensemble Learning

Monitoring Social Networking Platforms to Detect and Filter Fake News using Ensemble Learning

A Multi-Kernel Optimized Convolutional Neural Network With Urdu Word Embedding to Detect Fake News

Fake News Detection in Urdu using Deep Learning

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