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

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.

Identifying the evidence of speech emotional dialects using artificial intelligence: A cross-cultural study.

The advancement in technology especially in the field of artificial intelligence has opened up novel and robust ways to reanalyze the many aspects of human emotional behavior. One of such behavioral studies is the cultural impact on the expression and perception of human emotions. In-group advantage makes it easy for the people of the same cultural group to perceive each other's emotions accurately. The goal of this research is to re-investigate human behavior regarding expression and perception of emotions in speech. The theoretical basis of this research is grounded on the dialect theory of emotions. For the purpose of this study, six datasets of audio speeches have been considered. The participants of these datasets belong to six different cultural areas. A fully automated, machine learning-based framework i.e. Support Vector Machine (SVM) is used to carry out this study. The overall emotion perception for all six cultural groups supports in-group advantage, whereas emotion wise analysis partially supports the In-group advantage.