Dataset Information

Comparative analysis of machine learning methods to detect fake news in an Urdu language corpus.

ABSTRACT: Wide availability and large use of social media enable easy and rapid dissemination of news. The extensive spread of engineered news with intentionally false information has been observed over the past few years. Consequently, fake news detection has emerged as an important research area. Fake news detection in the Urdu language spoken by more than 230 million people has not been investigated very well. This study analyzes the use and efficacy of various machine learning classifiers along with a deep learning model to detect fake news in the Urdu language. Logistic regression, support vector machine, random forest (RF), naive Bayes, gradient boosting, and passive aggression have been utilized to this end. The influence of term frequency-inverse document frequency and BoW features has also been investigated. For experiments, a manually collected dataset that contains 900 news articles was used. Results suggest that RF performs better and achieves the highest accuracy of 0.92 for Urdu fake news with BoW features. In comparison with machine learning models, neural networks models long short term memory, and multi-layer perceptron are used. Machine learning models tend to show better performance than deep learning models.

SUBMITTER: Rafique A

PROVIDER: S-EPMC9299232 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundProliferation of misinformation in digital news environments can harm society in a number of ways, but its dangers are most acute when citizens believe that false news is factually accurate. A recent wave of empirical research focuses on factors that explain why people fall for the so-called fake news. In this scoping review, we summarize the results of experimental studies that test different predictors of individuals' belief in misinformation.MethodsThe review is based on a synthetic analysis of 26 scholarly articles. The authors developed and applied a search protocol to two academic databases, Scopus and Web of Science. The sample included experimental studies that test factors influencing users' ability to recognize fake news, their likelihood to trust it or intention to engage with such content. Relying on scoping review methodology, the authors then collated and summarized the available evidence.ResultsThe study identifies three broad groups of factors contributing to individuals' belief in fake news. Firstly, message characteristics-such as belief consistency and presentation cues-can drive people's belief in misinformation. Secondly, susceptibility to fake news can be determined by individual factors including people's cognitive styles, predispositions, and differences in news and information literacy. Finally, accuracy-promoting interventions such as warnings or nudges priming individuals to think about information veracity can impact judgements about fake news credibility. Evidence suggests that inoculation-type interventions can be both scalable and effective. We note that study results could be partly driven by design choices such as selection of stimuli and outcome measurement.ConclusionsWe call for expanding the scope and diversifying designs of empirical investigations of people's susceptibility to false information online. We recommend examining digital platforms beyond Facebook, using more diverse formats of stimulus material and adding a comparative angle to fake news research.

Dataset Information

Comparative analysis of machine learning methods to detect fake news in an Urdu language corpus.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets