Provenance Data in Social Media by Geoffrey Barbier
Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data.-
Deep Learning for Polymer Discovery
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Mining Structures of Factual Knowledge from Text
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Exploratory Causal Analysis with Time Series Data
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Probabilistic Approaches to Recommendations
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Detecting Fake News on Social Media
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Phrase Mining from Massive Text and Its Applications
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Modeling and Data Mining in Blogosphere
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Outlier Detection for Temporal Data
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Privacy in Social Networks
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Correlation Clustering
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Exploiting the Power of Group Differences
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Multidimensional Mining of Massive Text Data
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Individual and Collective Graph Mining
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Automated Taxonomy Discovery and Exploration
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Graph Mining
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Advances in Graph Neural Networks
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Mining Human Mobility in Location-Based Social Networks
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Ensemble Methods in Data Mining
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Mining Heterogeneous Information Networks
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Community detection and mining in social media
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Mining Latent Entity Structures
Geoffrey Barbier earned his Ph.D. in Computer Science at Arizona State University in Dec 2011. He was a student in the Data Mining and Machine Learning (DMML) Laboratory. He is a 2009 Science, Math, and Research for Transformation (SMART) scholarship recipient. His research interests include social computing, applying data mining and machine learning to social media data, and leveraging crowdsourced data to improve Humanitarian Aid and Disaster Relief (HADR) efforts. He earned a bachelor's degree in computer science at Brigham Young University, Provo, Utah. Geoff also completed a master's degree in business administration through Webster University. He is currently employed as a senior computer scientist at the Air Force Research Laboratory. Any views expressed in this work are the author's personal views and not necessarily those of the Department of Defense or Federal Government. Zhuo Feng is a post-doc researcher at the Data Mining and Machine Learning (DMML) Laboratory of ComputerScience and Engineering, Arizona State University (ASU). He obtained his Ph.D. in the Department of Systems and Industrial Engineering at the University of Arizona. His research interests include social computing, data mining, optimization, and machine learning. His recent research focuses on information diffusion and provenance issues in social media. Pritam Gundecha is a computer science Ph.D. student at Arizona State University (ASU). He also works as a graduate research assistant at the Data Mining and Machine Learning (DMML) Laboratory of Computer Science and Engineering at ASU. His research interests include social computing, data mining, and machine learning. His research focuses on security, privacy, and trust issues in social media. He earned a master's degree in computer science at ASU in 2010. He was interviewed by ReadWriteWeb, New Scientist, and Toronto Star for his recent work that was mentioned at more than a dozen media sites. Huan Liu is a professor of computer scienceand engineering at Arizona State University (ASU). He received his Ph.D. from University of Southern California and Bachelor of Engineering from Shanghai Jiao Tong University. He has been recognized for excellence in teaching and research in the Departement of Computer Science and Engineering at ASU. His research interests include data/web mining, machine learning, social computing, and artificial intelligence, investigating problems that arise in many real-world applications with high-dimensional data of disparate forms and multiple sources such as feature selection, modeling group interaction, relational learning, text categorization, biomarker identification, and social media analysis. His well-cited publications include books, book chapters, encyclopedia entries, conference and journal papers. He serves on journal editorial boards and numerous conference program committees, and he is a founding organizer of the International Workshop/Conference Series on Social Computing, Behavioral Modeling, and Prediction (SBP).
| SKU | Unavailable |
| ISBN 13 | 9783031007767 |
| ISBN 10 | 303100776X |
| Title | Provenance Data in Social Media |
| Author | Geoffrey Barbier |
| Series | Synthesis Lectures On Data Mining And Knowledge Discovery |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2013-05-28 |
| Number of pages | 72 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |