Mining Misinformation in Social Media: Understanding Its Rampant Spread, Harm, and Intervention
A rapid increase in social networking services in recent years has enabled people to share and seek information effectively. Meanwhile, the openness and timeliness of social networking sites also allow for the rapid creating and dissemination of misinformation. As witnessed in recent incidents of fake news, misinformation escalates quickly and can impact social media users with undesirable consequences and wreak havoc instantaneously. Despite many people have been aware of that fake news and rumors are misleading the public and even compromising elections, the problem is not going away. In this tutorial, we will discuss how misinformation gains traction in the race for attention, introduce emerging challenges of identifying misinformation, present a comparative survey of current data mining research in tackling the challenges, and suggest available resources and point to directions for future work.
This tutorial will target researchers and practitioners who are interested in the area of misinformation mining and have basic knowledge of network analysis, data mining, and machine learning. It will be delivered at a college junior/senior level, and should be easily accessible to interested parties from both industry and academia.
Challenges and Solutions in Group Recommender Systems
Group recommender systems are designed to provide suggestions in contexts in which people operate in groups. The goal of this tutorial is to provide the ICDM audience with an overview on group recommendation. We will first formally introduce the problem of producing recommendations to groups, then present a survey based on the tasks performed by these systems. We will also analyze challenging topics like their evaluation, and present emerging aspects and techniques in this area. The tutorial will end with a summary that highlights open issues and research challenges.
Mining Cohorts & Patient Data: Challenges and Solutions for the Pre-Mining, the Mining and the Post-Mining Phases
Data mining is intensively used in medicine and healthcare. Electronic Health Records (EHRs) are perceived as big patient data. On them, scientists strive to perform predictions on patients' progress, to understand and predict response to therapy, to detect adverse drug effects, and many other learning tasks. Medical researchers are also interested in learning from cohorts of population-based studies and of experiments. Learning tasks include the identification of disease predictors that can lead to new diagnostic tests and the acquisition of insights on interventions.
In this tutorial, we elaborate on data sources, methods, and case studies in medical mining. Next to the aforementioned conventional data sources, we address the potential of data from mobile devices. We discuss the learning problems that can be solved with those data, we present case studies and investigate the methods needed to prepare and mine those data and to present the results to a medical expert.