Social media has evolved into a platform for the dissemination of information, including fake news. There is a lot of false information about the current situation of the Coronavirus Disease 2019 (COVID-19) pandemic, such as false information regarding vaccination. In this paper, we focus on sentiment analysis for Malaysian COVID-19-related news on social media such as Twitter. Tweets in Malaysia are often a combination of Malay, English, and Chinese with plenty of short forms, symbols, emojis, and emoticons within the maximum length of a tweet. The contributions of this paper are twofold. Firstly, we built a multilingual COVID-19 Twitter dataset, comprising tweets written from 1 September 2021 to 12 December 2021. In particular, we collected 108,246 tweets, with over $67\%$ in Malay language, $27\%$ in English, $2\%$ in Chinese, and $4\%$ in other languages. We then manually annotated and assigned the sentiment of 11,568 tweets into three-class sentiments (positive, negative, and neutral) to develop a Malay-language sentiment analysis tool. For this purpose, we applied a data compression method using Byte-Pair Encoding (BPE) on the texts and used two deep learning approaches, i.e., the Multilingual Bidirectional Encoder Representation for Transformer (M-BERT) and convolutional neural network (CNN). BPE tokenization is used to encode rare and unknown words into smaller meaningful subwords. With the CNN, we converted the labeled tweets into image files. Our experiments explored different BPE vocabulary sizes with our BPE-Text-to-Image-CNN and BPE-M-BERT models. The results show that the optimal vocabulary size for BPE is 12,000; any values beyond that would not contribute much to the F1-score. Overall, our results show that BPE-M-BERT slightly outperforms the CNN model, thereby showing that the pre-trained M-BERT network has the advantage for our multilingual dataset.

While fake news is morally reprehensible, irresponsible parties intentionally use it to achieve their goals by disseminating it to vulnerable and targeted groups. Machine learning techniques have been researched extensively to detect fake news. On the other hand, evolutionary-based algorithms are now gaining popularity in the research community. In this study, a two-stage evolutionary approach is proposed to generate and optimize a mathematical equation for fake news detection. In the first stage, tree-based Genetic Programming (GP) algorithm is used to generate mathematical expressions to detect correlations between the language-independent (Lang-IND) features, extracted from Fake.my-COVID19 dataset, the newly curated fake news dataset in a mixed Malay - English language. The uniqueness of the proposed approach is that the mathematical expressions are formed by basic arithmetic operators or to include complex arithmetic operators such as addition, multiplication, subtraction, division, square, abs, log1p, sign, square root, and exponential together with Lang-IND features as the variables. Prior to second stage of the evolutionary approach, a sensitivity analysis is applied to shorten the best equation while maintaining the F1-score performance. In the second stage, an Adaptive Differential Evolution (ADE), is used to fine-tune the mathematical model. The experimental results conclude that the proposed two-stage evolutionary approach can be applied in fake news detection and the model can learn to predict using the Lang-IND features. Results from the first stage shows that the equation from GP scores F1-score of 83.23% on Fake.my-COVID19 dataset using complex arithmetic operators and at tree depth of 8. After the fine-tuning stage, the model performance increases the F1-score to 84.44%. The performance of the proposed two-stage evolutionary approach outperforms the baseline performance of six commonly-used machine learning algorithms, with Random Forest having the highest F1-score of 84.07%. The mathematical model is also tested separately on two other unseen datasets of different domain topic or language and achieves acceptable F1-scores.

Acquiring sonic waves is an essential part of oil and gas exploration as they give critical information about the well’s data and lithography at each well depth progression. However, these measurements are not always accessible, making analysis challenging. As computational power has improved, machine learning methods may now be used to predict these values from other data. Nonetheless, one shortcoming of existing models is that the most of them are not transparent (i.e., black box models). As a result, although promising great performance, they do not offer much insights to petrophysicists and geologists. This research aims to generate mathematical models for predicting compression wave (P-wave) and shear wave (S-wave) readings using a multistage evolutionary modelling approach. In particular, a multistage equation modelling approach using tree-based Genetic Programming (GP) and Adaptive Differential Evolution (ADE) is proposed. The obtained best mathematical models yield R ² of 0.745 and 0.9066 for P-wave and S-wave regression on normalized data, respectively. The average performance of models are R ² = 0.90 (P-Wave) and R ² = 0.75 (S-Wave). The performance of these mathematical models are comparable with other ”black box” models but with more compact mathematical approach in regression thereby opening opportunities for interpretability and analysis. Finally, the ”white box” models presented in this paper can be fine-tuned further as needed.

In the era where massive information can be spread easily through social media, fake news detention is increasingly used to prevent widespread misinformation. Machine learning algorithms have been used to identify patterns in news content, and databases are built to filter the false information. This paper investigates the state-of-the-art development into this research domain. A brief review is presented, from public domain dataset to various machine learning models. In addition, a mixed language dataset is presented for future researchers and investigators in this area.