Information diffusion, information spread, and influencers are important concepts in many studies on social media, especially Twitter analytics. However, literature overviews on the information diffusion of Twitter analytics are sparse, especially on the use of continuous time Markov chain (CTMC). This paper examines the following topics: (1) the purposes of studies about information diffusion on Twitter, (2) the methods adopted to model information diffusion on Twitter, (3) the metrics applied, and (4) measures used to determine influencer rankings. We employed a systematic literature review (SLR) to explore the studies related to information diffusion on Twitter extracted from four digital libraries. In this paper, a two-stage analysis was conducted. First, we implemented a bibliometric analysis using VOSviewer and R-bibliometrix software. This approach was applied to select 204 papers after conducting a duplication check and assessing the inclusion–exclusion criteria. At this stage, we mapped the authors’ collaborative networks/collaborators and the evolution of research themes. Second, we analyzed the gap in research themes on the application of CTMC information diffusion on Twitter. Further filtering criteria were applied, and 34 papers were analyzed to identify the research objectives, methods, metrics, and measures used by each researcher. Nonhomogeneous CTMC has never been used in Twitter information diffusion modeling. This finding motivates us to further study nonhomogeneous CTMC as a modeling approach for Twitter information diffusion.

(1) Background: sentiment analysis is a computational technique employed to discern individuals opinions, attitudes, emotions, and intentions concerning a subject by analyzing reviews. Machine learning-based sentiment analysis methods, such as Support Vector Machine (SVM) classification, have proven effective in opinion classification. Feature selection methods have been employed to enhance model performance and efficiency, with the Chi-Square method being a commonly used technique; (2) Methods: this study analyzes user reviews of Indonesia’s National Health Insurance (Mobile JKN) application, evaluating model performance and identifying optimal hyperparameters using the F1-Score metric. Sentiment analysis is conducted using a combined approach of SVM classification and Chi-Square feature selection; (3) Results: the sentiment analysis of user reviews for the Mobile JKN application reveals a predominant tendency towards positive reviews. The best model performance is achieved with an F1-Score of 96.82%, employing hyperparameters where $C$ is set to 10 and a “linear” kernel; (4) Conclusions: this study highlights the effectiveness of SVM classification and the significance of Chi-Square feature selection in sentiment analysis. The findings offer valuable insights into users’ sentiments regarding the Mobile JKN application, contributing to the improvement of user experience and advancing the field of sentiment analysis.

This article presents a study on forecasting silver prices using the extreme gradient boosting (XGBoost) machine learning method with hyperparameter tuning. Silver, a valuable precious metal used in various industries and medicine, experiences significant price fluctuations. XGBoost, known for its computational efficiency and parallel processing capabilities, proves suitable for predicting silver prices. The research focuses on identifying optimal hyperparameter combinations to improve model performance. The study forecasts silver prices for the next six days, evaluating models based on mean absolute percentage error (MAPE) and root mean square error (RMSE). Model A (the best model based on MAPE value) suggests silver prices decline on the first and second days, rise on the third, decline again on the fourth, and stabilize with an increase on the fifth and sixth days. Model A achieves a MAPE of 5.98% and an RMSE of 1.6998, utilizing specific hyperparameters. Conversely, model B (the best model based on RMSE value) indicates a price decrease until the third day, followed by an upward trend until the sixth day. Model B achieves a MAPE of 6.06% and an RMSE of 1.6967, employing distinct hyperparameters. The study also compared the proposed models with several other ensemble models (CatBoost and random forest). The model comparison was carried out by incorporating 2 additional metrics (MAE and SI), and it was found that the proposed models exhibited the best performance. These findings provide valuable insights for forecasting silver prices using XGBoost.

On social media, information spreads quickly and can affect other users. In order for information to spread to many users, it is important to know how the information diffusion model or the flow of information is and who is the influencer on social media. In this paper, the Information Diffusion Model in social media was developed using the Continuous Time Markov Chain (CTMC). The tweet-retweet activity of social media users such as on Twitter can be seen as the CTMC model, because it fulfills the nature of Markov, i.e. retweets from subsequent users depend only on the current user’s retweet and do not depend on the retweet history of previous users. Users engaged in retweet tweets are state of CTMC. By using the tweet-retweet network simulation data including 10 users and 4 topics, the influencer rankings were obtained. An influencer rating is determined not only by the number of retweets, but also by the time it takes to get those retweets.

Estimasi Parameter Model Volatilitas Stokastik dengan Metode Bayesian Rantai Markov Monte Carlo untuk Memprediksi Return Saham

Analisis sentimen merupakan pemrosesan bahasa alami yang bertujuan untuk menemukan opini, mengidentifikasi sentimen apa yang diungkapkan, dan mengklasifikasikannya berdasarkan nilai yang terkandung. Teknologi pembelajaran mesin, seperti Klasifikasi Naïve Bayes banyak digunakan dalam klasifikasi sentimen karena memiliki tingkat akurasi dan kecepatan yang tinggi. Pembelajaran mesin dapat dipadukan dengan metode seleksi fitur untuk meningkatkan efisiensi model. Salah satu metode seleksi fitur adalah metode Information Gain yang dapat digunakan untuk menentukan atribut paling efektif. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap aplikasi PeduliLindungi dan menghitung evaluasi performa model berdasarkan nilai metrik F1-Score. Pada penelitian ini analisis sentimen dilakukan menggunakan metode Klasifikasi Naïve Bayes dengan penggabungan metode seleksi fitur Information Gain untuk meningkatkan performa model dan mempercepat komputasi. Hasil analisis sentimen menggunakan metode klasifikasi Naïve Bayes dan seleksi fitur Information Gain menunjukkan bahwa pengguna cenderung memberikan ulasan positif terhadap aplikasi PeduliLindungi. Performa model untuk analisis sentimen pengguna terhadap aplikasi PeduliLindungi ditentukan berdasarkan rata-rata F1-Score dengan 10-fold cross validation diperoleh sebesar 95.1%; artinya model yang diperoleh mempunyai presisi dan recall yang baik.