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Twitter: A Digital Socioscope
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  • Cited by 9
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Karami, Amir Bennett, London S. and He, Xiaoyun 2018. Mining Public Opinion about Economic Issues. International Journal of Strategic Decision Sciences, Vol. 9, Issue. 1, p. 18.

    Smart, Paul 2018. Knowledge machines. The Knowledge Engineering Review, Vol. 33, Issue. ,

    Schinas, Manos Papadopoulos, Symeon Apostolidis, Lazaros Kompatsiaris, Yiannis and Mitkas, Pericles A. 2017. Internet Science. Vol. 10673, Issue. , p. 361.

    Weber, Ingmar and State, Bogdan 2017. Digital Demography. p. 935.

    Yeruva, Vijaya Kumari Junaid, Sidrah and Lee, Yugyung 2017. Exploring social contextual influences on healthy eating using big data analytics. p. 1507.

    Shaw, George and Karami, Amir 2017. Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise. Proceedings of the Association for Information Science and Technology, Vol. 54, Issue. 1, p. 357.

    Jörgens, Helge Kolleck, Nina and Saerbeck, Barbara 2016. Exploring the hidden influence of international treaty secretariats: using social network analysis to analyse the Twitter debate on the ‘Lima Work Programme on Gender’. Journal of European Public Policy, Vol. 23, Issue. 7, p. 979.

    Qureshi, M. Atif Younus, Arjumand and Greene, Derek 2016. Machine Learning and Knowledge Discovery in Databases. Vol. 9853, Issue. , p. 71.

    Morstatter, Fred Wu, Liang Nazer, Tahora H. Carley, Kathleen M. and Liu, Huan 2016. A new approach to bot detection: Striking the balance between precision and recall. p. 533.

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Book description

How can Twitter data be used to study individual-level human behavior and social interaction on a global scale? This book introduces readers to the methods, opportunities, and challenges of using Twitter data to analyze phenomena ranging from the number of people infected by the flu, to national elections, to tomorrow's stock prices. Each chapter, written by leading domain experts in clear and accessible language, takes the reader to the forefront of the newly emerging field of computational social science. An introductory chapter on Twitter data analysis provides an overview of key tools and skills, and gives pointers on how to get started, while the case studies demonstrate shortcomings, limitations, and pitfalls of Twitter data as well as its advantages. The book will be an excellent resource for social science students and researchers wanting to explore the use of online data.

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