ProSOUL: A Framework to Identify Propaganda From Online Urdu Content

Abstract

Today, the rapid dissemination of information on digital platforms has seen the emergence of information pollution such as misinformation, disinformation, fake news, and different types of propaganda. Information pollution has become a serious threat to the online digital world and has posed several challenges to social media platforms and governments around the world. In this paper, we propose Propaganda Spotting in Online Urdu Language (ProSOUL)-a framework to identify content and sources of propaganda spread in the Urdu language. First, we develop a labelled dataset of 11,574 Urdu news to train the machine learning classifiers. Next, we develop the Linguistic Inquiry and Word Count (LIWC) dictionary to extract psycho-linguistic features of Urdu text. We evaluate the performance of different classifiers by varying n-gram, News Landscape (NELA), Word2Vec, and Bidirectional Encoder Representations from Transformers (BERT) features. Our results show that the combination of NELA, word n-gram, and character n-gram features outperform with 0.91 accuracy for Urdu text classification. In addition, Word2Vec embedding outperforms BERT features in classification of the Urdu text with 0.87 accuracy. Moreover, we develop and classify large scale Urdu content repositories to identify web sources spreading propaganda. Our results show that ProSOUL framework performs best for propaganda detection in the online Urdu news content compared to the general web content. To the best of our knowledge, this is the first study on the detection of propaganda content in the Urdu language.

Detecting and Mitigating the Dissemination of Fake News: Challenges and Future Research Opportunities

“…We can look at the features from following points of view: Being imitated: Some features are difficult to be mimicked by malicious users (topographical features), while most of them can be imitated easily. Sensitive to time: For instance, [37] shows that Word n-gram features may provide information which is less relevant at that point of time. Required level of computational resources: Some features are readily available, while some others like layer ratio in [38] need processing…”

Corpulyzer: A Novel Framework for Building Low Resource Language Corpora

“…UrduWeb20 is effectively used to develop and test NLP and IR applications for the Urdu language. For instance, UrduWeb20 is employed by Kausar et al [61] for the propaganda detection from the Urdu content. Authors train machine learning models on the gold standard dataset of Urdu content.…”

Deriving Disinformation Insights from Geolocalized Twitter Callouts

“…These dense vector representations have been leveraged extensively, for example, as input representations in neural network architectures for NLP tasks [10], e.g., detecting 'fake news' and phenomena related to the setting of this work [29]. In a recent study identifying online propaganda [18], Word2vec embeddings were found to outperform a multilingual version of BERT in Urdu [7], which the authors ascribe to the limited vocabulary of Urdu in the model. In another study, Word2vec has been leveraged as a feature in the detection of fake news where researchers found that it performs well in comparison to other textual features across multiple datasets and languages [9].…”