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Do people actually listen to ads in podcasts?: A study about how machine learning can be used to gain insight in listening behaviour
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computing Science.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computing Science.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Today, listening to podcasts is a common way of consuming media and it has been proven that listeners are much more recipient to advertisement when being addressed in a podcast, rather than through radio. This study has been performed at Acast, an audio-on-demand and podcast platform that hosts, monetizes, and distributes podcasts globally. With the use of machine learning, the goal of this study has been to obtain a credible estimate of how listeners outside the application tend to respond when exposed to ads in podcasts. The study includes a number of different machine learning models, such as Random Forest, Logistic Regression, Neural Networks and kNN. It was shown that machine learning could be applied to obtain a credible estimate of how ads are received outside the Acast application, based on data collected from the application. Additionally, out of the models included in the study, Random Forest was proven being the best performing model for this problem. Please note that the results presented in the report are based on a mix of real and simulated data.

Place, publisher, year, edition, pages
2019. , p. 73
Series
UPTEC STS, ISSN 1650-8319 ; 19016
Keywords [en]
podcast, ad, machine learning, prediction, impression, random forest, data
National Category
Information Systems
Identifiers
URN: urn:nbn:se:uu:diva-390484OAI: oai:DiVA.org:uu-390484DiVA, id: diva2:1341790
External cooperation
Acast AB
Educational program
Systems in Technology and Society Programme
Supervisors
Examiners
Available from: 2019-08-20 Created: 2019-08-12 Last updated: 2019-08-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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