No abstract

The beginning of this decade brought utter international chaos with the COVID-19 pandemic and the Russia-Ukraine war (RUW). The ongoing war has been building pressure across the globe. People have been showcasing their opinions through different communication media, of which social media is the prime source. Consequently, it is important to analyze people’s emotions toward the RUW. This paper therefore aims to provide the framework for automatically classifying the distinct societal emotions on Twitter, utilizing the amalgamation of Emotion Robustly Optimized Bidirectional Encoder Representations from the Transformers Pre-training Approach (Emoroberta) and machine-learning (ML) techniques. This combination shows the originality of our proposed framework, i.e., Russia-Ukraine War emotions (RUemo), in the context of the RUW. We have utilized the Twitter dataset related to the RUW available on Kaggle.com. The RUemo framework can extract the 27 distinct emotions of Twitter users that are further classified by ML techniques. We have achieved 95% of testing accuracy for multilayer perceptron and logistic regression ML techniques for the multiclass emotion classification task. Our key finding indicates that:First, 81% of Twitter users in the survey show a neutral position toward RUW; second, there is evidence of social bots posting RUW-related tweets; third, other than Russia and Ukraine, users mentioned countries such as Slovakia and the USA; and fourth, the Twitter accounts of the Ukraine President and the US President are also mentioned by Twitter users. Overall, the majority of tweets describe the RUW in key terms related more to Ukraine than to Russia.

The world is on the verge of rapid technological advancements, and acceptance. Healthcare is also influenced by recent innovations such as medical imaging and electronic patient health records. Thus, to maintain the patient's record in this digital era giant healthcare providers are adopting internet-based services such as patient portals that are portraying great results for patient involvement in this mechanism. However, it has been observed that patients, physicians, and healthcare providers are facing certain challenges to synch the health information cycle among them. Therefore, the focus of this study is to examine the interleaved issues, challenges, and benefits of healthcare patient portals. We have studied existing literature to retrieve our findings that accomplished the identified objective. We have found that patient portals are beneficial for maintaining and updating patients’ health histories at any time and can be shared across the healthcare network. However, physician acceptance and patient resistance are key challenges to implementing patient portals in healthcare settings.

Advancement in technology provides numerous solutions to not only legitimate businesses but to illegal trades as well. Selling substances, drugs, and prohibited merchandise and goods on the internet comes under illegal trading. The internet we surf is merely a thin layer of this deeply rooted miraculous mechanism of connecting the world. The dark web is the part of the deep web that utilizes the internet to flourish the illicit intentions of trading illegal items, thereby fostering the ongoing societal devastation. This chapter is exploring anonymous trading on the dark online marketplace using the Silkroad 2.0 dataset. This work aims to analyze the various aspects of dark e-commerce trading and highlight different themes used for trading illicit drugs on Twitter by performing the thematic analysis using Latent Dirichlet Allocation unsupervised machine learning with a 0.44 coherence score. The findings have shown that developed countries are participating in illegal trading, and teenage schoolgoers can be victims of social media drug trading.