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Practice paper

Using weak supervision to scale the development of machine-learning models for social media-based marketing research

Jennifer Cutler and Aron Culotta
Applied Marketing Analytics: The Peer-Reviewed Journal, 5 (2), 159-169 (2019)
https://doi.org/10.69554/ZGKN2372

Abstract

Marketers have expressed substantial enthusiasm about the potential of social media data to enhance marketing research, and the computer science literature provides many examples of using the text and network connections of social media users to infer measurements of interest to marketers. Despite this, the adoption of such machine-learning approaches has been surprisingly limited in marketing practice, in part due to the hurdle of procuring the labelled training data typically used to build such models. This paper discusses how the organic structure of social media can often be leveraged to circumvent the need for such curated data labels. It describes two emerging methodological themes of weak supervision — training on exemplars and training on groups — that are broadly promising towards this goal, providing examples of how they have been applied towards a variety of marketing tasks without requiring any manually labelled training data, and in some cases, requiring nothing more than a single keyword as input. This paper presents these approaches in the hope that examples will inspire and facilitate the development of a broader range of flexible, scalable and cost-effective models for social media-based marketing research, and stimulate additional research in this area.

Keywords: machine learning; social media; artificial intelligence; weak supervision; data mining; automation

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Author's Biography

Jennifer Cutler is an associate professor of marketing at the Kellogg School of Management at Northwestern University, where she teaches digital marketing analytics and runs the Social Media Analytics at Kellogg Lab. Her award-winning research focuses on generating insights about consumers and organisations through social media data, and she has worked with a wide range of companies including Microsoft, IBM and the Chicago Sun Times. She has a PhD from Duke University and an ScB from Brown University.

Aron Culotta is an associate professor of computer science at the Illinois Institute of Technology, where he leads the Text Analysis in the Public Interest Lab. His research focuses on extracting socially valuable insights from online social networks. He is a former Microsoft Live Labs Fellow with a PhD in computer science from the University of Massachusetts, Amherst. His work has received best paper awards at the Association for the Advancement of Artificial Intelligence conference and the Conference on Computer Supported Social Work and Social Computing.

Citation

Cutler, Jennifer and Culotta, Aron (2019, July 1). Using weak supervision to scale the development of machine-learning models for social media-based marketing research. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 5, Issue 2. https://doi.org/10.69554/ZGKN2372.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 5 / Issue 2
© Henry Stewart
Publications LLP

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