• Contact Info
Publications in VIVO
 

Keegan, Brian Assistant Professor

Positions

Research Areas research areas

Research

research overview

  • Assistant Professor Brian C. Keegan is a computational social scientist whose research is at the intersection of human-computer interaction, network science and data science. He uses computational methods to identify, analyze, and theorize the structural and temporal patterns of large-scale social interaction. This work is motivated by his belief that social life rarely unfolds at a steady state; bursts, sequences, and other dynamics play crucial roles in structuring the social world around us. His research uses large datasets from socio-technical information systems such as Wikipedia revision histories, online game user behavior logs, and social media data streams to understand the intersection of temporal dynamics and large-scale social behavior. He is also researching how data science capabilities are being adopted by the emerging cannabis industry.

keywords

  • Computational social science, Network science, Data science, Computer-mediated communication, New media, Peer production, Crowdsourcing, Computer-supported cooperative work, Web science, Cannabis, Marijuana

Publications

selected publications

Teaching

courses taught

  • ARSC 5040 - Arts and Sciences Special Topics
    Primary Instructor - Spring 2019 / Spring 2021
    Same as ARSC 4040. May be repeated up to 3 total credit hours.
  • INFO 2201 - Computational Reasoning 2: Representations of Data
    Primary Instructor - Spring 2018
    Surveys techniques for representing data and expressing relationships among data, both at small scales (for example, via programmatic data structures) and at large scales (for example, in various kinds of database systems). Introduces fundamentals of algorithm analysis and the trade-offs involved in managing data using different approaches, tools and organizing principles. Requires demonstrated proficiency with introductory computer programming.
  • INFO 3401 - Information Exploration
    Primary Instructor - Fall 2020
    Teaches students how to use information to identify interesting real world problems and to generate insight. Students will learn to find, collect, assemble and organize data to inspire new questions, make predictions, generate deliverables, and work towards solutions. They will learn to appropriately apply different methods (including computational, statistical and qualitative) for exploratory data analysis in a variety of domains.
  • INFO 3402 - Information Exposition
    Primary Instructor - Spring 2018 / Spring 2019 / Fall 2019 / Spring 2021
    Teaches students to communicate information to a wider audience and construct stories with data across a variety of domains. Students will learn to use data for rhetorical purposes, applying visual, statistical and interpretative methods. Students will learn to think critically about ethical and social implications of using data in expository media, including identification of bias.
  • INFO 3501 - Investigations in Information Science: Open Collaboration
    Primary Instructor - Fall 2018
    Analyzes the mechanisms of peer production and crowdsourcing systems like Wikipedia and OpenStreetMap. Students will investigate how these crowdsourced platforms work socially and technically, develop skills using tools for their analysis and critically evaluate platform and community limitations. Counts as Investigations in Information Science. Same as INFO 5501.
  • INFO 5613 - Network Science
    Primary Instructor - Fall 2021
    Introduces theories and methods for analyzing relational data in social, information, and other complex networks. Students will understand the processes and theories explaining network structure and dynamics as well as develop skills analyzing and visualizing real-world network data. No math or statistics training required, but course will assume familiarity with Python. Same as INFO 4613.

Background

International Activities

Other Profiles

Github

  • github.com/brianckeegan/