Individual Retweeting Behavior on Social Networking Sites (eBook)

This book explores and analyzes influential predictors and the underlying mechanisms of individual content sharing/retweeting behavior on social networking sites (SNS) from an empirical perspective. Since Individual content sharing/ retweeting behavior expedites information dissemination, it is a critical mechanism of information diffusion on Twitter.  

Individual sharing/retweeting behavior does not appear to happen randomly. So, what factors lead to individual information dissemination behavior? What are the dominating predictors? How does the recipient make retweeting decisions? How do these influential predictors combine and by what mechanism do they influence an individual's retweeting decisions? Furthermore, are there any differences in the process of individual retweeting decisions? If so, what causes such differences? 

In order to answer these previously unexplored questions and gain a holistic view of individual retweeting behavior, the authors examined people's retweeting history on Twitter and obtained a real dataset containing more than 60 million Twitter posts. They then employed text mining and natural language processing techniques to extract useful information from social media content, and used various feature selection methods to identify a subset of salient features that have substantial effects on individual retweeting behavior. Lastly, they applied the Elaboration Likelihood Model to build an overarching theoretical framework to reveal the underlying mechanisms of individual retweeting behavior. Given its scope, this book will appeal to researchers interested in investigating information dissemination on social media, as well as to marketers and administrators who plan to use social networking sites as an important avenue for information dissemination.

Juan Shi received her Ph.D. from Xi'an Jiaotong University and City University of Hong Kong in 2018, and is currently a Lecturer at the International Business School at Shaanxi Normal University, China.  Her research focuses on social networks, consumer behavior and smart tourism, etc. She has published articles in Applied Soft Computing, Internet Research, and Information Technology and Management. She is the leader of the Xi'an Social Science Fund project (Grant No.WL108) ,the Fundamental Research Funds project for the Central Universities (Grant No. 19SZYB28) and Natural Science Basic Research Program of Shaanxi (Program No. 2020JQ-427).

Professor Lai received his Ph.D. from Michigan State University, USA, and is a former the Chair Professor of Management Science at the City University of Hong Kong. Professor Lai's main research interests include operations and supply chain management, financial and business risk analysis and modeling using computational intelligence. In 2009, Professor Lai was the recipient of the Michigan State University's Joon S. Moon Distinguished International Alumni Award and was also appointed Chang Jiang Scholar Chair Professor by the Ministry of Education, China.

Gang Chen received his ph.D. from department of automation of Xi'an Jiaotong University in 2013. In the same year, he joined the 705th research institute of CSSC (China State Shipbuilding Cooperation Limited) and devoted to building the control and navigation system of torpedo. Since 2018, he engaged in the field of flight control system and UAV (unmanned aerial vehicle ) navigation system. He is now working at Meituan (the largest takeway delivery company of China), aiming to make UAV takeway deliverying to be realized.

---

This book explores and analyzes influential predictors and the underlying mechanisms of individual content sharing/retweeting behavior on social networking sites (SNS) from an empirical perspective. Since Individual content sharing/ retweeting behavior expedites information dissemination, it is a critical mechanism of information diffusion on Twitter.  Individual sharing/retweeting behavior does not appear to happen randomly. So, what factors lead to individual information dissemination behavior? What are the dominating predictors? How does the recipient make retweeting decisions? How do these influential predictors combine and by what mechanism do they influence an individual's retweeting decisions? Furthermore, are there any differences in the process of individual retweeting decisions? If so, what causes such differences? In order to answer these previously unexplored questions and gain a holistic view of individual retweeting behavior, the authors examined people's retweeting history on Twitter and obtained a real dataset containing more than 60 million Twitter posts. They then employed text mining and natural language processing techniques to extract useful information from social media content, and used various feature selection methods to identify a subset of salient features that have substantial effects on individual retweeting behavior. Lastly, they applied the Elaboration Likelihood Model to build an overarching theoretical framework to reveal the underlying mechanisms of individual retweeting behavior. Given its scope, this book will appeal to researchers interested in investigating information dissemination on social media, as well as to marketers and administrators who plan to use social networking sites as an important avenue for information dissemination.