Title	Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining [electronic resource] / edited by Nitin Agarwal, Nima Dokoohaki, Serpil Tokdemir
Imprint	Cham : Springer International Publishing : Imprint: Springer, 2019
Edition	1st ed. 2019
Connect to	https://doi.org/10.1007/978-3-319-94105-9
Descript	X, 278 p. 46 illus., 28 illus. in color. online resource

The contributors in this book share, exchange, and develop new concepts, ideas, principles, and methodologies in order to advance and deepen our understanding of social networks in the new generation of Information and Communication Technologies (ICT) enabled by Web 2.0, also referred to as social media, to help policy-making. This interdisciplinary work provides a platform for researchers, practitioners, and graduate students from sociology, behavioral science, computer science, psychology, cultural studies, information systems, operations research and communication to share, exchange, learn, and develop new concepts, ideas, principles, and methodologies. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining will be of interest to researchers, practitioners, and graduate students from the various disciplines listed above. The text facilitates the dissemination of investigations of the dynamics and structure of web based social networks. The book can be used as a reference text for advanced courses on Social Network Analysis, Sociology, Communication, Organization Theory, Cyber-anthropology, Cyber-diplomacy, and Information Technology and Justice

Chapter1: Intent Mining for the Good, Bad & Ugly Use of Social Web: Concepts, Methods, and Challenges -- Chapter2: Bot-ivistm: Assessing Information Manipulation in Social Media Using Network Analytics -- Chapter3: Studying Fake News via Network Analysis: Detection and Mitigation -- Chapter4: Predictive Analysis on Twitter: Techniques and Applications -- Chapter5: Using Subgraph Distributions for Characterizing Networks and Fitting Random Graph Models -- Chapter6: Towards Effective Assessment of Group Collaborations in OSNs -- Chapter7: Dynamics of Overlapping Community Structures with Application to Expert Identification -- Chapter8: On Dynamic Topic Models for Mining Social Media -- Chapter9: Domain Specific Use Cases for Knowledge Enabled Social Media Analysis -- Chapter10: Privacy in Human Computation: User awareness study, Implications for existing platforms, Recommendations, and Research Directions

1. Social sciences—Data processing
2. Social sciences—Computer programs
3. Data mining
4. Social sciences -- Data processing
5. Computational Social Sciences. http://scigraph.springernature.com/things/product-market-codes/X34000
6. Data Mining and Knowledge Discovery. http://scigraph.springernature.com/things/product-market-codes/I18030
7. Data-driven Science
8. Modeling and Theory Building. http://scigraph.springernature.com/things/product-market-codes/P33030
9. Computer Appl. in Social and Behavioral Sciences. http://scigraph.springernature.com/things/product-market-codes/I23028