Overview of Influence Maximization in Social Media Data Analytics

Research output: Contribution to conferenceAbstract

Abstract

Social media has become a new and main platform for organizations to broadcast their policies, for companies to advertise their products, and for people to propagate their opinions. Therefore, social media data analytics has become a timely and significant research topic in recent years. In this talk, Dr. Li will first go through the social media background and the data representation of social media data. And then, he will briefly discuss the main stream research in social data mining and social computing. After that, Dr. Li will mainly introduce how the two most popular influence models are defined in social computing, what the influence maximization problem is defined, how its variants are defined. Finally, Dr. Li will introduce his current social media research project and discuss the interesting collaboration with attendees.
This one-hour keynote targets researchers, designers and practitioners interested in social computing, social media data analytics, big data management systems and processing. While the audience with a good background in these areas would benefit most from this keynote, we believe the material to be presented would give general audience and newcomers an introductory pointer to the current work and important research topics in this field of viral marketing and social influence maximization, and inspire them to learn more. Only preliminary knowledge about graph, data mining, algorithms and their applications are needed.
Original languageEnglish
Pages1201-1201
Number of pages1
DOIs
Publication statusPublished - 2017
EventInternational World Wide Web Conference -
Duration: 1 Jan 2011 → …

Conference

ConferenceInternational World Wide Web Conference
Abbreviated titleWWW
Period1/01/11 → …

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  • Cite this

    Li, J. (2017). Overview of Influence Maximization in Social Media Data Analytics. 1201-1201. Abstract from International World Wide Web Conference, . https://doi.org/10.1145/3041021.3053049