File Name: social media data mining and analytics .zip
- Social media mining
- Social Big Data: Mining, Applications, and Beyond
- Social Media Data Mining and Analytics
Social media mining
Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining.
It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
You can also purchase the book at Cambridge University Press or Amazon. Contents: Chapter 1. Introduction to social media mining Chapter 2. Graph essentials Chapter 3. Network measures Chapter 4. Network models Chapter 5. Data mining essentials Chapter 6. Community analysis Chapter 7. Information diffusion in Social Media Chapter 8. Influence and homophily Chapter 9. Recommendation in social media Chapter Behavior analytics Bibliography. Previous post. Sign Up.
Social Big Data: Mining, Applications, and Beyond
Social media mining is the process of obtaining big data from user-generated content on social media sites and mobile apps in order to extract patterns, form conclusions about users, and act upon the information, often for the purpose of advertising to users or conducting research. The term is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quantities of raw ore to find the precious minerals; likewise, social media mining requires human data analysts and automated software programs to sift through massive amounts of raw social media data in order to discern patterns and trends relating to social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, and more. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs, new products, processes or services. Social media mining uses a range of basic concepts from computer science , data mining , machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis , network science , sociology , ethnography , optimization and mathematics.
The social nature of Web 2. Existing research in social media data mining has focused on techniques for extracting information for specific applications from separate social media sources. The mobile network and the Internet of Things are transforming what it means to be social online. Humans, everyday objects, and smart devices interact and form an intelligent social network that is a highly adaptive complex system. Assisted by personal devices, people can access real-time traffic, weather, and news event information and exchange such information through social interaction and form communities dynamically. The rich user- and device-generated data and user interactions generate complex social big data that is different from classical structured attribute-value data. The data objects take various forms including unstructured text, geotagged data objects, and data object streams.
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Social Media Data Mining and Analytics
Presenting a detailed exploration into the progression of the field, the book describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications. The illuminating viewpoints presented in this comprehensive volume will be of great interest to researchers and graduate students involved in machine learning and pattern recognition, as well as to professional multimedia analysts and software developers.
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