This book provides a comprehensive overview of the theory and praxis of Big Data Analytics and how these are used to extract cognition-related information from social media and literary texts. It presents analytics that transcends the borders of discipline-specific academic research and focuses on knowledge extraction, prediction, and decision-making in the context of individual, social, and national development. The content is divided into three main sections: the first of which discusses various approaches associated with Big Data Analytics, while the second addresses the security and privacy of big data in social media, and the last focuses on the literary text as the literary data in Big Data Analytics. Sharing valuable insights into the etiology behind human cognition and its reflection in social media and literary texts, the book benefits all those interested in analytics that can be applied to literature, history, philosophy, linguistics, literary theory, media & communication studies and computational/digital humanities.

Identifying and stopping the dissemination of fabricated news, hate speech, or deceptive information camouflaged as legitimate news poses a significant technological hurdle. This book presents emergent methodologies and technological approaches of natural language processing through machine learning for counteracting the spread of fake news and hate speech on social media platforms. • Covers various approaches, algorithms, and methodologies for fake news and hate speech detection. • Explains the automatic detection and prevention of fake news and hate speech through paralinguistic clues on social media using artificial intelligence. • Discusses the application of machine learning models to learn linguistic characteristics of hate speech over social media platforms. • Emphasizes the role of multilingual and multimodal processing to detect fake news. • Includes research on different optimization techniques, case studies on the identification, prevention, and social impact of fake news, and GitHub repository links to aid understanding. The text is for professionals and scholars of various disciplines interested in fake news and hate speech detection.

In the field of modern marketing, a pivotal challenge emerges as traditional strategies grapple with the complexities of an increasingly data-centric world. Marketers, researchers, and business consultants find themselves at a crossroads, navigating the intricate intersection of data science and strategic marketing practices. This challenge serves as the catalyst for Data-Driven Marketing for Strategic Success, a guide designed to address the pressing issues faced by academic scholars and professionals alike. This comprehensive exploration unveils the transformative power of data in reshaping marketing strategies, offering a beacon of strategic success in a sea of uncertainty. This book transcends the realm of traditional marketing literature. It stands as a useful resource, not merely adding elements to ongoing research but shaping the very future of how researchers, practitioners, and students engage with the dynamic world of data-driven marketing. It is strategically tailored to reach a diverse audience, offering valuable insights to academics and researchers exploring advanced topics, practitioners in the marketing industry seeking practical applications, and graduate students studying data science, marketing, and business analytics. Policymakers, ethicists, and industry regulators will find the dedicated section on ethical considerations particularly relevant, emphasizing the importance of responsible practices in the data-driven marketing landscape.

This book covers computationally innovative methods and technologies including data collection and elicitation, data processing, data analysis, data visualizations, and data presentation. It explores how digital humanists have harnessed the hypersociality and social technologies, benefited from the open-source sharing not only of data but of code, and made technological capabilities a critical part of humanities work. Chapters are written by researchers from around the world, bringing perspectives from diverse fields and subject areas. The respective authors describe their work, their research, and their learning. Topics include semantic web for cultural heritage valorization, machine learning for parody detection by classification, psychological text analysis, crowdsourcing imagery coding in natural disasters, and creating inheritable digital codebooks.Designed for researchers and academics, this book is suitable for those interested in methodologies and analytics that can be applied in literature, history, philosophy, linguistics, and related disciplines. Professionals such as librarians, archivists, and historians will also find the content informative and instructive.

Opinion mining and text analytics are used widely across numerous disciplines and fields in today’s society to provide insight into people’s thoughts, feelings, and stances. This data is incredibly valuable and can be utilized for a range of purposes. As such, an in-depth look into how opinion mining and text analytics correlate with social media and literature is necessary to better understand audiences. The Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media introduces the use of artificial intelligence and big data analytics applied to opinion mining and text analytics on literary works and social media. It also focuses on theories, methods, and approaches in which data analysis techniques can be used to analyze data to provide a meaningful pattern. Covering a wide range of topics such as sentiment analysis and stance detection, this publication is ideal for lecturers, researchers, academicians, practitioners, and students.

Although there are various studies on theories and analytical techniques to address consumer behavior change in the current world, tracking consumer behavior change in the metaverse and the adoption of the metaverse remains a challenge that requires discussion. The advent of the metaverse will have a profound influence on consumer behavior, from how people make decisions and create brand connections to how they feel about their avatar embodiment and their purchases in the metaverse. The Handbook of Research on Consumer Behavioral Analytics in Metaverse and the Adoption of a Virtual World investigates the social, behavioral, and psychological factors that influence metaverse adoption. The focus then shifts to concepts, theories, and analytical approaches for detecting changes in consumer behavior in the metaverse. Covering topics such as e-commerce markets, user experience, and immersive technologies, this major reference work is an excellent resource for business executives, entrepreneurs, data analysts, marketers, advertisers, government officials, social media professionals, librarians, students and educators of higher education, researchers, and academicians.