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Vargo, Chris J.

Associate Professor

Positions

Research Areas research areas

Research

research overview

  • 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

Publications

selected publications

Teaching

courses taught

  • APRD 4300 - Strategic Communication Analytics and Metrics
    Primary Instructor - Fall 2018 / Spring 2019 / Fall 2019 / Spring 2020 / Spring 2021 / Spring 2022 / Fall 2022
    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 - Fall 2021
    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 4931 - Internship
    Primary Instructor - Summer 2018 / Spring 2019 / Summer 2019 / Spring 2020 / Spring 2021 / Spring 2022
    Internship course.
  • APRD 6342 - Digital Advertising
    Primary Instructor - Fall 2018 / Fall 2019 / Fall 2020 / Spring 2022
    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 - 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.
  • DTSA 5798 - Supervised Text Classification for Marketing Analytics
    Primary Instructor - Summer 2022 / Fall 2022
    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 - Summer 2022 / Fall 2022
    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 - Summer 2022 / Fall 2022
    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.

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