Advisory Board and Editors Text Mining

Journal Factsheet
A one-page PDF to help when considering journal options with co-authors
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I told my colleagues that PeerJ is a journal where they need to publish if they want their paper to be published quickly and with the strict peer review expected from a good journal.
Sohath Vanegas,
PeerJ Author
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Keli Xiao

Dr. Keli Xiao is an Associate Professor in the College of Business at Stony Brook University. He received his Ph.D. from Rutgers University. Dr. Xiao’s research interests include business analytics, data mining, real estate/urban computing, economic bubbles and crises, and asset pricing. His research has appeared in many high-quality journals and conference proceedings, such as IEEE Transactions on Knowledge and Data Engineering (TKDE), Real Estate Economics, ACM Transactions on Knowledge Discovery from Data (TKDD), ACM Transactions on Management Information Systems (TMIS), ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), etc. He regularly serves as an SPC or PC of numerous prestigious conferences, such as AAAI, IJCAI, KDD, ICDM, SDM, CIKM, etc.. He is a senior member of the IEEE and the ACM.

Adaptive & Self-Organizing Systems Agents & Multi-Agent Systems Artificial Intelligence Computational Science Data Mining & Machine Learning Data Science Distributed & Parallel Computing Graphics Mobile & Ubiquitous Computing Natural Language & Speech Network Science & Online Social Networks Neural Networks Optimization Theory & Computation Sentiment Analysis Social Computing Spatial & Geographic Information Systems Text Mining

Jiayan Zhou

Jiayan Zhou is a postdoctoral scholar at Stanford University School of Medicine, specializing in Cardiovascular Medicine, Population Genetics, and Molecular Biology. He is a computational geneticist with a special interest in advancing statistical methodologies and software frameworks tailored for integrating multi-omics data, aimed at elucidating novel therapeutic avenues for various human diseases. He also actively explores the intersection of AI and healthcare, leveraging AI models to address intricate challenges in healthcare delivery and patient outcomes.

Bioinformatics Computational Biology Data Mining & Machine Learning Epidemiology Genetics Graphics Natural Language & Speech Text Mining

Arkaitz Zubiaga

I'm a senior lecturer (associate professor) at Queen Mary University of London. My research revolves around Social Data Science, interdisciplinary research bridging NLP and Computational Social Science. I'm particularly interested in linking online data with events in the real world, among others for tackling problematic issues on the Web and social media that can have a damaging effect on individuals or society at large, such as hate speech, misinformation and inequality.

Computational Linguistics Data Mining & Machine Learning Data Science Human-Computer Interaction Natural Language & Speech Network Science & Online Social Networks Sentiment Analysis Social Computing Text Mining World Wide Web & Web Science