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Researchers must overcome a number of hurdles in order to find insight in social media data.
Social media and Big Data have radically changed how people communicate, interact, work, conduct business, and more. The buzz around these phenomena continues to grow as people and organizations begin to tap into the potential of a socially connected, data-rich world. The excitement is hard to overstate, and there are many examples to illustrate the insights and benefits that can be gained by examining social media data. (See our research page for one source of examples.) However, some very real hurdles stand between the unending supply of data and those who would like to mine and use it.
Dr. Huan Lui is one of the scholars tackling the challenges inherent in mining social media data for meaning, insight, and value. Lui is a professor and researcher at Arizona State University. Last week he visited the University of Minnesota as part of the speaker series hosted by the Social Media and Business Analytics Collaborative. During his time on campus, Lui articulated some of the problems that come with mining social media data, and shared solutions he and his colleagues have devised to address those problems.
Lui discussed several concrete challenges researchers encounter when they mine social media data for answers to business or other questions:
Deception detection: Information intended to deceive can spread though social media the same as valid information. This raises questions of how to detect different types of deception (e.g., manipulating information, changing context, or outright fabrication) in different social channels and formats (e.g., text, link, audio, photo, video, multimedia).
To help students, researchers, and organizations grapple with these and other challenges in mining social media data, Lui offered the following resources, available for free download in whole or in part:
By Reza Zafarani, Mohammad Ali Abbasi, Huan Liu
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. (Cambridge University Press, 2014)
By Shamanth Kumar, Fred Morstatter, Huan Liu
A guide to harnessing data through the Twitter API. Examples show real-world applications in the context of various intriguing questions. (Springer, 2013.)
By Geoffrey Barbier,Zhuo Feng,Pritam Gundecha,Huan Liu
Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts. (Morgan & Claypool, 2013)
By Jiliang Tang and Huan Lui
The study and understanding of trust can lead to an effective approach to addressing both information overload and credibility problems. (WWW2014 Tutorial)
Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his PhD in Computer Science at University of Southern California and B.Eng. in EECS at Shanghai JiaoTong University. He was recognized for excellence in teaching and research in Computer Science and Engineering at Arizona State University. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating problems that arise in real-world applications with high-dimensional data of disparate forms. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He serves on journal editorial/advisory boards and numerous conference program committees. He is a Fellow of IEEE and a member of several professional societies.