Podify : a podcast streaming platform with automatic logging of user behaviour for academic research

Meggetto, Francesco and Moshfeghi, Yashar; (2023) Podify : a podcast streaming platform with automatic logging of user behaviour for academic research. In: SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, TWN, pp. 3215-3219. ISBN 9781450394086 (https://doi.org/10.1145/3539618.3591824)

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Podcasts are spoken documents that, in recent years, have gained widespread popularity. Despite the growing research interest in this domain, conducting user studies remains challenging due to the lack of datasets that include user behaviour. In particular, there is a need for a podcast streaming platform that reduces the overhead of conducting user studies. To address these issues, in this work, we present Podify. It is the first web-based platform for podcast streaming and consumption specifically designed for research. The platform highly resembles existing streaming systems to provide users with a high level of familiarity on both desktop and mobile. A catalogue of podcast episodes can be easily created via RSS feeds. The platform also offers Elasticsearch-based indexing and search that is highly customisable, allowing research and experimentation in podcast search. Users can manually curate playlists of podcast episodes for consumption. With mechanisms to collect explicit feedback from users (i.e., liking and disliking behaviour), Podify also automatically collects implicit feedback (i.e., all user interactions). Users' behaviour can be easily exported to a readable format for subsequent experimental analysis. A demonstration of the platform is available at https://youtu.be/k9Z5w_KKHr8, with the code and documentation available at https://github.com/NeuraSearch/Podify.