As a scholar, I study public opinion and consumer behavior by investigating large amounts of data. I test social science theory using methods from computer science, such as text mining, natural language processing, data mining, machine learning and topic modeling. My research focuses on how people interact with news media and strategic communication on a large scale. My emergent research suggests that contextual data can work in concert with social science theory to explain how people behave online. The more we understand trends and tendencies of large audiences and their aggregate behaviors, the better our tactics as advertisers and public relations practitioners can become.
keywords
big data, computational social science, agenda-setting theory, social media, consumer engagement, marketing analytics, advertising analytics
APRD 4300 - Strategic Communication Analytics and Metrics
Primary Instructor
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Fall 2018 / Spring 2019 / Fall 2019 / Spring 2020 / Spring 2021 / Spring 2022 / Fall 2022 / Spring 2023 / Spring 2024
Provide students with a base knowledge of analytics and metrics used in strategic communication. Students will learn how to obtain and clean big data, how to analyze and turn it into insights and how to present and communicate insights into actionable recommendations.
APRD 4301 - Social Media Listening
Primary Instructor
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Fall 2021 / Spring 2024 / Fall 2024
Provides the practical understanding and application of strategic social media listening from the brand perspective in advertising and public relations, focusing on critical thinking and the ethics of using social media data. Provides students with hands-on experience in industry leading listening tools including Brandwatch, Social Studio, Meltwater and Hootsuite. Equips students with the skills needed to find relevant conversations, uncover insights then apply their perspectives to management for business impact.
APRD 4302 - Marketing Data Visualization
Primary Instructor
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Spring 2023
Teaches students how to use data visualization to tell business stories based upon the analysis of marketing and business data. Students will learn basic data concepts, types, and uses. They will explore, summarize, and analyze the data using various techniques and best practices with Tableau Software.
APRD 4931 - Internship
Primary Instructor
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Summer 2018 / Spring 2019 / Summer 2019 / Spring 2020 / Spring 2021 / Spring 2022 / Spring 2023
Internship course
APRD 6342 - Digital Advertising
Primary Instructor
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Fall 2018 / Fall 2019 / Fall 2020 / Spring 2022 / Spring 2023
Covers both traditional and emerging digital advertising methods, the popular platforms used to execute ads, and the leading analytic tools that can be used to assess advertising performance. Core advertising platforms covered include search, display, social media, native advertising, sponsored content and mobile. This class focuses on best practices and Key Performance Indicators that go with each advertising platform. Department consent required.
APRD 6343 - Applications of Advanced Statistical Techniques in Advertising
Primary Instructor
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Spring 2019 / Spring 2020 / Spring 2021
Building upon prior data acquisition and analysis coursework, students will effectively and flexibly generate advanced statistical models in a digital advertising-specific context. This course will focus on data originating from a variety of digital advertising sources. In addition to technical skill acquisition, students will learn how to interpret results and present them to clients and management. Department consent required.
APRD 7004 - Doctoral Professionalization Seminar
Primary Instructor
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Fall 2024
Introduces you to the university and gives you a chance to think out loud about what your academic future might look like. The course is designed to be responsive to your needs regarding your career, getting a job, getting tenure and teaching. In short, the course prepares you for a career in academia. Same as JRNL 7004.
APRD 7061 - Quantitative Research Methods
Primary Instructor
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Fall 2024
Introduces graduate students to concepts and applications in quantitative research methods. The course prepares students fordissertation writing through hands-on experience in developing research designs and conducting independent quantitative research. Same as JRNL 7061.
DTSA 5798 - Supervised Text Classification for Marketing Analytics
Primary Instructor
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Summer 2022 / Fall 2022 / Spring 2023 / Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
Marketing data often requires categorization, or labeling. In today�s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course students will learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students will walk through a conceptual overview of supervised machine learning, and dive into real-world datasets through instructor-led tutorials in Python. The course will conclude with a major project.
DTSA 5799 - Unsupervised Text Classification for Marketing Analytics
Primary Instructor
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Summer 2022 / Fall 2022 / Spring 2023 / Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course students will learn how to use unsupervised deep learning to train algorithms to extract topics and insights from text data. Students will walk through a conceptual overview of unsupervised machine learning, and dive into real-world datasets through instructor-led tutorials in Python. The course will conclude with a major project.
DTSA 5800 - Network Analysis for Marketing Analytics
Primary Instructor
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Summer 2022 / Fall 2022 / Spring 2023 / Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
Network analysis is a long-standing methodology used to understand the relationships between words and actors in the broader networks in which they exist. This course will cover network analysis at it pertains to marketing data, specifically text datasets and social networks. Students will walk through a conceptual overview of network analysis, and dive into real-world datasets through instructor-led tutorials in Python. The course will conclude with a major project.
JRNL 7004 - Doctoral Professionalization Seminar
Primary Instructor
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Fall 2024
Introduces you to the university and gives you a chance to think out loud about what your academic future might look like. The course is designed to be responsive to your needs regarding your career, getting a job, getting tenure and teaching. In short, the course prepares you for a career in academia. Same as APRD 7004.
JRNL 7061 - Quantitative Research Methods
Primary Instructor
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Fall 2024
Introduces graduate students to concepts and applications in quantitative research methods. The course prepares students fordissertation writing through hands-on experience in developing research designs and conducting independent quantitative research. Same as APRD 7061.
