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2021 | ISBN: 1984647148 | English | 320 pages | True PDF | 13 MB [/center]
Although the future of using AI and Machine Learning in social media remains in doubt due to the global data privacy legislation, AI and Machine Learning are still being applied by social media businesses to help them understand, segment and organize the vast amounts of data that are being collected daily. A social networking service in its most basic form includes the ability to connect with other people and, through connecting with those people, extend connections to even more people. Social networks have greatly influenced the usage pattern of Internet by common man. It has paved way for the great proliferation of Internet into the life of even the ordinary people. Such an exponential rise in the use of the World Wide Web due to social networks started to fetch huge volume of data across diverse domains in short period of time. These characteristics by which the huge amounts of social network data are generated make them to categorize as Big Data. Since the use of big data analytical techniques in many domains have obtained remarkable improvements in the way various businesses operate, we consider that social network domain is not an exception to this perception.
The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. It also integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining. The emergence of social media and advances in mobile technology and internet has resulted in constant connectivity across users enabling them to post, share, and engage with content published on the web. Studying and learning from such data about users, and their engagement with content can give insights into the current and emerging trends in society. However, studying social media data comes with its own set of unique challenges. The Internet has rapidly evolved from being merely an information sharing platform to being a social networking platform used by individuals to share content, opinions, and information. Social networking is a global phenomenon that has revolutionized how people interact with each other. It affects nearly every aspect of our life: education, communication, employment, politics, healthcare, social relationships, and personal productivity. In this digital age, the smartest brands are the ones that make use of social media monitoring. The process of social media monitoring allows brands to keep track of their online reputation and image. However, with the size and complexity of data increasing day by day, brands need to understand not only how to make use of this data but also about how to cope with this growing data. Hence, this book will serve to act as a survey of big data analytical techniques as applied to social networks and efforts are taken appropriately to present the pros and cons of each algorithm. We also present the suggestions for use of various algorithms to various classes of social network data. The book focuses on various technical concepts and aspects of social network analysis. The book features the latest developments and findings in this emerging field.
2021 | ISBN: 1984647148 | English | 320 pages | True PDF | 13 MB [/center]
Although the future of using AI and Machine Learning in social media remains in doubt due to the global data privacy legislation, AI and Machine Learning are still being applied by social media businesses to help them understand, segment and organize the vast amounts of data that are being collected daily. A social networking service in its most basic form includes the ability to connect with other people and, through connecting with those people, extend connections to even more people. Social networks have greatly influenced the usage pattern of Internet by common man. It has paved way for the great proliferation of Internet into the life of even the ordinary people. Such an exponential rise in the use of the World Wide Web due to social networks started to fetch huge volume of data across diverse domains in short period of time. These characteristics by which the huge amounts of social network data are generated make them to categorize as Big Data. Since the use of big data analytical techniques in many domains have obtained remarkable improvements in the way various businesses operate, we consider that social network domain is not an exception to this perception.
The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. It also integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining. The emergence of social media and advances in mobile technology and internet has resulted in constant connectivity across users enabling them to post, share, and engage with content published on the web. Studying and learning from such data about users, and their engagement with content can give insights into the current and emerging trends in society. However, studying social media data comes with its own set of unique challenges. The Internet has rapidly evolved from being merely an information sharing platform to being a social networking platform used by individuals to share content, opinions, and information. Social networking is a global phenomenon that has revolutionized how people interact with each other. It affects nearly every aspect of our life: education, communication, employment, politics, healthcare, social relationships, and personal productivity. In this digital age, the smartest brands are the ones that make use of social media monitoring. The process of social media monitoring allows brands to keep track of their online reputation and image. However, with the size and complexity of data increasing day by day, brands need to understand not only how to make use of this data but also about how to cope with this growing data. Hence, this book will serve to act as a survey of big data analytical techniques as applied to social networks and efforts are taken appropriately to present the pros and cons of each algorithm. We also present the suggestions for use of various algorithms to various classes of social network data. The book focuses on various technical concepts and aspects of social network analysis. The book features the latest developments and findings in this emerging field.
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