Introduction: In an age of rapid technical innovation and a growing digital world, protecting sensitive data from cyberattacks is crucial. The dynamic and complicated nature of these attacks requires novel cybersecurity solutions. Methods: This study analyses how Artificial Intelligence (AI) and Natural Language Processing (NLP) strengthen cybersecurity. The qualitative research approach is followed to gather data through a literature review of relevant scholarly articles and conduct interviews with cybersecurity specialists. Results: Recent AI advances have greatly enhanced the detection of anomalous patterns and behaviors in huge datasets, a key threat identification tool. NLP has also excelled at detecting malevolent intent in textual data, such as phishing efforts. AI and NLP enable adaptive security policies, enabling agile responses to evolving security issues. Expert interviews confirm that AI and NLP reduce false positives, improve threat intelligence, streamline network security setups, and improve compliance checks. These technologies enable responsive security policies, which give a strategic edge against developing security threats. AI and NLP's predictive skills could revolutionize cybersecurity by preventing threats. Conclusion: This study shows that AI and NLP have improved cybersecurity threat detection, automated incident response, and adaptive security policies. Overcoming threat detection, aggressive attacks and data privacy issues is essential to properly leveraging these advances and strengthening cyber resilience in a changing digital landscape.

In the modern era of digital communication, the analysis of sentiment has emerged as a crucial tool for understanding and inferring public sentiment as communicated through written text. This is particularly relevant in the context of social media platforms such as Twitter, Facebook and Instagram. The present study focuses on the urgent matter of public opinion regarding the practice of animal testing, employing advanced deep-learning methodologies for sentiment analysis. A dataset of 15,360 tweets about animal testing was collected using the Twitter API. The data was prepared for analysis by undergoing careful preprocessing and word embedding it through the utilization of Word2vec. To classify tweets into positive and negative sentiment categories, a Long Short-Term Memory (LSTM) model was employed, given its suitability for processing sequential data. Remarkably, an accuracy rate of 88.7 percent was achieved by the model. It was determined that around 80% of tweets expressed criticism towards animal testing, indicating the presence of a substantial negative sentiment majority. These results show the topic's continuing significance by emphasizing its highly emotional and controversial nature. It is concluded that deep learning, and in particular LSTM models, can be used to effectively analyze large amounts of social media data and yield insightful understandings of public opinion. This study underlines the significance of sentiment analysis for gaining insight into public opinion and for its applications in policymaking and discourse analysis.

Social media has become a vital communication tool for higher education institutions (HEIs) to reach larger targets, attract followers, and engage with diverse audiences. This study conducted a quantitative and qualitative analysis of 4148 Facebook posts from 16 public and private HEIs in the United Arab Emirates (UAE). The aim of the study is to evaluate users’ engagement through their reactions to various post characteristics, including format, language, and content type. The posts generated 177,022 emotes, 17,269 shares, and 8374 comments. The results showed that images are an efficient format for boosting interaction, whereas plain text posts did not generate high engagement. The English language was more conducive for generating shares, while Arabic-language posts generated more emotes and likes. The comparative analysis results showed that private HEIs are more active on their Facebook pages than public HEIs. The content analysis suggested that student-related posts generate the highest level of engagement, while announcements and faculty- and research-related posts drive the lowest levels of engagement. These results offer valuable insights into how HEIs can optimize their social media strategies to enhance user engagement.