Fred Conrad’s research concerns the development of new methods and data sources for conducting social research. His work is largely focused on survey methodology, but he also explores the use of social media content as a complement to survey data and as a source of large-scale qualitative insights. His focus is on data quality and reducing measurement error. For example, live video interviews promote more thoughtful responses, e.g., less straightlining – the tendency to give the same answer to a battery of survey questions, but they also promote less candor when answering questions on sensitive topics. Measurement error in social media include misclassification in the automated interpretation of content using methods such as sentiment analysis and topic modeling, as well as selective self-presentation (only posting flattering content). Equally challenging is not knowing the extent to which users differ from the population to which one might wish to generalize results.
My research interests are in natural language semantics and psycholinguistics, focusing on verbs. I conduct behavioral psycholinguistic experiments with methodologies such as self-paced reading and maze tasks, as well as surveys of linguistic and semantic judgments. I also study semantic variation using corpora and datasets such as the Twitter Decahose, to better understand how words have developed diverging meanings in different communities, age groups, or regions. I use primarily R and Python to collect, manage, and analyze data. I direct the UM WordLab in the linguistics department, working with students (especially undergraduates) on experimental and computational research focusing on lexical representations.
For human-machine systems, I first collect data from human users, whether it’s an individual, a team, or even a society. Different kinds of methods can be used, including self-report, interview, focus groups, physiological and behavioral data, as well as user-generated data from the Internet.
Based on the data collected, I attempt to understand human contexts, including different aspects of the human users, such as emotion, cognition, needs, preferences, locations and activities. Such understanding can then be applied to different human-machine systems, including healthcare systems, automated driving systems, and product-service systems.
Based on the different design theory and methodology, from the perspective of the machine dimension, I apply knowledge of computing and communication as well as practical and theoretical knowledge of social and behavior to design various systems for human users. From the human dimension, I seek to understand human needs and decision making processes, and then build mathematical models and design tools that facilitate integration of subjective experiences, social contexts, and engineering principles into the design process of human-machine systems.
Greg’s research primarily investigates information flow in financial markets and the actions of agents in those markets – both consumers and producers of that information. His approach draws on theory from the social sciences (economics, psychology and sociology) combined with large data sets from diverse sources and a variety of data science approaches. Most projects combine data from across multiple sources, including commercial data bases, experimentally created data and extracting data from sources designed for other uses (commercial media, web scrapping, cellphone data etc.). In addition to a wide range of econometric and statistical methods, his work has included applying machine learning , textual analysis, mining social media, processes for missing data and combining mixed media.
Dr. Dempsey’s research focuses on statistical methods for digital and mobile health. My current work involves three complementary research themes: (1) experimental design and data analytic methods to inform multi-stage decision making in health; (2) statistical modeling of complex longitudinal and survival data; and (3) statistical modeling of complex relational structures such as interaction networks. Current directions include (1) integration of sequential multiple assignment randomized trials (SMARTs) and micro-randomized trials (MRTs) and associated causal inference methods; (2) recurrent event analysis in the presence of high-frequency sensor data; and (3) temporal models for, community detection of, and link prediction using complex interaction data.
My research focuses on the intersection between mobile technology, parenting, parent-child interaction, and child development of processes such as executive functioning, self-regulation, and social-emotional well-being. Our projects use a combination of methods including surveys, videotaped parent-child interaction tasks, time diaries, and mobile device app logging to examine how parents and young children use mobile technologies throughout their day. We have developed novel content analysis approaches to understand the experience of young children while using commercially available mobile apps – including advertising content, educational quality, and data collection. We emphasize questions that are relevant to everyday parenting experiences, and also consider what design changes would help create an optimal default environment for children and families.
I conduct research on the use of consumer-facing technologies for chronic disease self management. My work predominantly centers on the use of mobile applications that collect and manage patient generated health data overt time.
Dr. Hemphill studies conversations in social media and aims to promote just access to social media spaces and their data. She uses computational approaches to modeling political topics, predicting and addressing toxicity in online discussions, and tracing linguistic adaptations among extremists. She also studies digital data curation and is especially interested in ways to measure and model data reuse so that we can make informed decisions about how to allocate data resources.