The amount of data produced, captured and transmitted through the media has never been greater. But for this data to be useful, it needs to be properly understood and claims made about or with data need to be properly scrutinized. Through a series of examples of statistics in the media, this book shows you how to critically assess the presentation of data in the media, to identify what is significant and to sort verifiable conclusions from misleading claims. How accurate are polls, and how should we know? How should league tables be read? Are numbers presented as ‘large’ really as big as they may seem at first glance? By answering these questions and more, readers will learn a number of statistical concepts central to many undergraduate social science statistics courses. By tying them in to real life examples, the importance and relevance of these concepts comes to life. As such, this book does more than teaches techniques needed for a statistics course; it teaches you life skills that we need to use every single day.

The proliferation of mobile media in recent years is an international phenomenon, with billions of devices sold annually. Mobile communications are now moving beyond individualized voice to mass media content--text, voice, sound, images, and even video. This will create new types of content that allow media companies and users to interact in new ways. There is a strong interest from the media and telecom industries in what manner of applications and content can be distributed in that fashion, and at what cost. To answer these questions, the book provides 18 chapters from internationally renowned authors. They identify likely types of content such as news, entertainment, peer-to-peer, and location-specific information; evaluate the economics, business models, and payment mechanisms necessary to support these media; and cover policy dimensions such as copyright, competitiveness, and access rights for content providers. This volume takes the reader through the various elements that need to be considered in the development of third generation (3G) content, and explains pitfalls and barriers. The result is a volume of interest to business professionals, academics, and policy makers. The book is international in focus and a glossary of terms is provided. There are few publications available which give an overview of this rapidly changing field.

CUET-UG Mass Media And Communication Question Bank 2000+ Chapter wise question With Explanations As per Updated Syllabus [ cover all 7 Units] The Units are – Unit-1 : Communication Unit-2: Journalism Unit-3: TV Unit-4: Radio Unit-5: Cinema Unit -6: Social Media Unit-7: New Media

Does media history really start with a bang? More than just newspapers, television, and social networks, media are the means by which any information is communicated, from cosmic radiation traces to medieval church bells to modern identity documents. Cultures are held together as much by bookkeeping and records as they are by stories and myths. From Big Bang to Big Data is a long history of the media – how it has been established, used, and transformed from the beginning of recorded time until the present. It is not primarily a story of revolutions and innovations, but of continuities and overlaps that reveal surprising patterns across history. Many media were invented as ways to store and share information, and many have served as powerful tools for administration and control. The concerns raised about media today, whether about privacy, piracy, or anxieties over declining cultural standards, preoccupied earlier generations too. In a playful style, accompanied by more than one hundred illustrations, the authors show us how every society has been a media society in its own way. From antique graffiti to last year’s viral YouTube clip, the past is only approachable through media. From Big Bang to Big Data provides a new way of thinking about media in history – and about human societies past and present.

Social media platforms are one of the main generators of textual data where people around the world share their daily life experiences and information with online society. The social, personal, and professional lives of people on these social networking sites generate not only a huge amount of data but also open doors for researchers and academicians with numerous research opportunities. This ample amount of data needs advanced machine learning, deep learning, and intelligent tools and techniques to receive, process, and interpret the information to resolve real-life challenges and improve the online social lives of people. Advanced Applications of NLP and Deep Learning in Social Media Data bridges the gap between natural language processing (NLP), advanced machine learning, deep learning, and online social media. It hopes to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. Covering topics such as machine learning-based prediction, emotion recognition, and high-dimensional text clustering, this premier reference source is an essential resource for OSN service providers, psychiatrists, psychologists, clinicians, sociologists, students and educators of higher education, librarians, researchers, and academicians.

A methodological toolkit for social scientists to create their own infrastructure for collecting and analyzing social media big data.

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.